Manual — WorkShop_3.0.1 SunOS_4

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Section (5)

history	Workspace command and file-change log
putback.cmt	Putback transaction comment log file

Section ---

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Section 01 December 1992

vertool

VersionTool is an OpenWindows graphical user interface (GUI) tool for the Source Code Control System (SCCS). VersionTool is available as part of the SPARCworks/TeamWare product.

1. Commands (intro)

CC	C++ compilation system
acc	C compiler
alint	a C program checker (Solaris 2.x)	[ lint ]
asa	convert FORTRAN carriage-control output to printable form
bcheck	batch utility for Runtime Checking (SPARC only)
bringover	copy files from a parent workspace to a child workspace
c++filt	C++ name demangler	[ C++filt ]
cflow	generate C flowgraph
codemgr	The CodeManager "umbrella" command.
codemgrtool	codemgrtool is an OpenWindows graphical user interface (GUI) tool for CodeManager commands.
ctags	create a tags file for use with ex and vi
dbx	source-level debugger
debugger	OpenWindows interface for the dbx source-level debugger
def.dir.flp	default directory file list program
dem	demangle a C++ name
error	insert compiler error messages at right source lines
f77	FORTRAN compiler
filemerge	window-based file comparison and merging program
fpr	convert FORTRAN carriage-control output to printable form
fpversion	print information about the system CPU and FPU
freezept	generate or translate SCCS Mergeable delta IDs for lists of files
freezepttool	generate or translate SCCS Mergeable delta IDs for lists of files
fsplit	split a multi-routine FORTRAN file into individual files
gprof	display call-graph profile data
indent	indent and format a C program source file
inline	in-line procedure call expander
intro	introduction to FORTRAN Manual Pages
lmdown	graceful shutdown of all license daemons
lmgrd.ste	flexible license manager daemon	[ lmgrd ]
lmhostid	report the hostid of a system
lmremove	remove specific licenses and return them to license pool
lmreread	tells the license daemon to reread the license file
lmstat	report status on license manager daemons and feature usage
lmutil	generic FLEXlm utility program.
lmver	report the FLEXlm version of a library or binary file
make	ParallelMake supplemental information
maketool	Makefile browser and OpenWindows interface to the make(1) program
nm	print name list
pc	Pascal compiler
prof	display profile data
ptclean	clean up the parameterized types database
putback	copy files from a child workspace to its parent workspace
ratfor	rational FORTRAN dialect
resolve	merge files in conflict using interactive commands and/or Filemerge
rpcgen	RPC protocol compiler
rtc_patch_area	patch area utility for Runtime Checking (SPARC only)
sbcleanup	deletes old Source Browser files
sbquery	command-line interface to Sun SourceBrowser
sbrowser	OpenWindows interface to Sun SourceBrowser
sbtags	create tags files for GNU Emacs and ex/vi sbtags − create tags files for Source Browser	[ etags, ctags ]
sparcworks	OpenWindows interface for the interactive session management of SPARCworks Tools.
strip	remove symbol table, debugging and line number information from an object file
tcov	construct test coverage analysis and statement-by-statement profile
version	display version identification of object file or binary
workspace	manipulate CodeManager workspaces
ws_undo	undo the effects of the last bringover or putback command

3. C Library

decimal_to_floating	convert decimal record to floating-point value	[ decimal_to_single, decimal_to_double, decimal_to_extended, decimal_to_quadruple ]
demangle	decode a C++ encoded symbol name
econvert	output conversion	[ econvert, fconvert, gconvert, seconvert, sfconvert, sgconvert, qeconvert, qfconvert, qgconvert, ecvt, fcvt, gcvt ]
fcvt	output conversion	[ econvert, fconvert, gconvert, seconvert, sfconvert, sgconvert, qeconvert, qfconvert, qgconvert, ecvt, fcvt, gcvt ]
floating_to_decimal	convert floating-point value to decimal record	[ floating_to_decimal, single_to_decimal, double_to_decimal, extended_to_decimal, quadruple_to_decimal ]
gcvt	output conversion	[ econvert, fconvert, gconvert, seconvert, sfconvert, sgconvert, qeconvert, qfconvert, qgconvert, ecvt, fcvt, gcvt ]
sigfpe	signal handling for specific SIGFPE codes
string_to_decimal	parse characters into decimal record	[ string_to_decimal, file_to_decimal, func_to_decimal ]
strtod	convert string to double-precision number	[ strtod, atof ]

3C. Compatibility Routines

atexit	add program termination routine
difftime	computes the difference between two calendar times
div	compute the quotient and remainder	[ div, ldiv ]
fflush	close or flush a stream
fsetpos	reposition a file pointer in a stream	[ fsetpos, fgetpos ]
labs	return absolute value of integer
memmove	memory operations
raise	send signal to program
rand	simple random-number generator	[ rand, srand ]
strerror	get error message string
strtoul	convert string to integer

Section 3C++

cartpol	cartesian/polar functions in the C++ complex number math library
cplx.intro	introduction to C++ complex number math library	[ cplx.intro complex ]
cplxerr	error-handling functions in the C++ complex number math library	[ cplxerr complex error ]
cplxexp	functions in the C++ complex number math library	[ cplxexp, exp, log, log10, pow, sqrt ]
cplxops	arithmetic operator functions in the C++ complex number math library
cplxtrig	trigonometric functions in the C++ complex number math library
filebuf	buffer class for file I/O
fstream	stream class for file I/O
generic	generic macro definitions used mainly for creating generic types	[ generic.h ]
interrupt	signal handling for the task library	[ interrupt Interrupt_handler ]
ios	basic iostreams formatting
ios.intro	introduction to iostreams and the man pages
istream	formatted and unformatted input
manip	iostream manipulators
ostream	formatted and unformatted output
queue	list management for the task library
sbufprot	protected interface of the stream buffer base class
sbufpub	public interface of the stream buffer base class
ssbuf	buffer class for for character arrays
stdarg	handle variable argument list
stdiobuf	buffer and stream classes for use with C stdio
stream_MT	base class to provide dynamic changing of iostream class objects to and from MT safety.
stream_locker	class used for application level locking of iostream class objects.
strstream	stream class for “I/O” using character arrays
task	coroutines in the C++ task library
task.intro	introduction to the coroutine library and man pages
tasksim	histogram and random numbers for the task library
varargs	handle variable argument list
vector	generic vector and stack

3F. FORTRAN Library (intro)

abort	terminate abruptly; write memory image to core file
access	return access mode (r,w,x) or existence of a file
alarm	execute a subroutine after a specified time
bit	and, or, xor, not, rshift, lshift, bic, bis, bit, setbit functions
chdir	change default directory
chmod	change mode of a file
ctime	return system time	[ time, ctime, ltime, gmtime ]
date	return date in character form
etime	return elapsed time	[ etime, dtime ]
exit	terminate process with status
f77_floatingpoint	FORTRAN IEEE floating-point definitions
f77_ieee_environment	mode, status, and signal handling for IEEE arithmetic
fdate	return date and time in an ASCII string
fgetc	get a character from a logical unit	[ getc, fgetc ]
flush	flush output to a logical unit
fork	create a copy of this process
fputc	write a character to a FORTRAN logical unit	[ putc, fputc ]
free	deallocate a region of memory allocated by malloc
fseek	reposition a file on a logical unit	[ fseek, ftell ]
fstat	get file status	[ stat, lstat, fstat ]
ftell	reposition a file on a logical unit	[ fseek, ftell ]
gerror	get system error messages	[ perror, gerror, ierrno ]
getarg	get the kth command line argument	[ getarg, iargc ]
getc	get a character from a logical unit	[ getc, fgetc ]
getcwd	get pathname of current working directory
getenv	get value of environment variables
getfd	get the file descriptor of an external unit number
getfilep	get the file pointer of an external unit number
getlog	get user’s login name
getpid	get process id
getuid	get user or group ID of the caller	[ getuid, getgid ]
gmtime	return system time	[ time, ctime, ltime, gmtime ]
hostnm	get name of current host
iargc	get the kth command line argument	[ getarg, iargc ]
idate	return date in numerical form
ierrno	get system error messages	[ perror, gerror, ierrno ]
index	get index/length of substring	[ index, rindex, lnblnk, len ]
intro	introduction to FORTRAN library functions and subroutines.
ioinit	initialize I/O: carriage control, blanks, append, filenames
irand	return random values	[ rand, drand, irand ]
isatty	find name of a terminal port; also: is unit a terminal?	[ ttynam, isatty ]
isetjmp	longjmp returns to the location set by isetjmp	[ longjmp, isetjmp ]
itime	return time in numerical form
kill	send a signal to a process
len	return the declared length of a character string
libm_double	FORTRAN access to double precision libm functions and subroutines
libm_quadruple	FORTRAN access to quadruple-precision libm functions (SPARC only)
libm_single	FORTRAN access to single-precision libm functions and subroutines
link	make a link to an existing file	[ link, symlnk ]
lnblnk	get index/length of substring	[ index, rindex, lnblnk, len ]
loc	return the address of an object
long	integer object conversion	[ long, short ]
longjmp	longjmp returns to the location set by isetjmp	[ longjmp, isetjmp ]
lstat	get file status	[ stat, lstat, fstat ]
ltime	return system time	[ time, ctime, ltime, gmtime ]
malloc	allocate an amount of memory and return the address
mvbits	move specified bits
perror	get system error messages	[ perror, gerror, ierrno ]
putc	write a character to a FORTRAN logical unit	[ putc, fputc ]
qsort	quick sort
ran	return a random number between 0 and 1
rand	return random values	[ rand, drand, irand ]
range	return maximum positive integer	[ inmax ]
rename	rename a file
rindex	get index/length of substring	[ index, rindex, lnblnk, len ]
secnds	return system time in seconds since midnight
sh	fast execution of an sh shell command
short	integer object conversion	[ long, short ]
signal	change the action for a signal
sleep	suspend execution for an interval
stat	get file status	[ stat, lstat, fstat ]
symlnk	make a link to an existing file	[ link, symlnk ]
system	execute operating system command
tclose	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
time	return system time	[ time, ctime, ltime, gmtime ]
topen	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
tread	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
trewin	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
tskipf	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
tstate	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
ttynam	find name of a terminal port; also: is unit a terminal?	[ ttynam, isatty ]
twrite	FORTRAN tape I/O	[ topen, tclose, tread, twrite, trewin, tskipf, tstate ]
unlink	remove a file
wait	wait for a process to terminate

3M. Math Library (intro)

Intro	introduction to mathematical library functions and constants	[ intro ]
acos	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
acosd	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
acosh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
acosp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
acospi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
addrans	additive pseudo-random number generators
aint	round to integral value in floating-point or integer format	[ aint, anint, irint, nint ]
anint	round to integral value in floating-point or integer format	[ aint, anint, irint, nint ]
annuity	exponential, logarithm, financial	[ exp2, exp10, log2, compound, annuity ]
asin	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
asind	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
asinh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
asinp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
asinpi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
atan	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
atan2	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
atan2d	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
atan2pi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
atand	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
atanh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
atanp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
atanpi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
bessel	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
cabs	Euclidean distance	[ hypot ]
cbrt	square root, cube root	[ sqrt, cbrt ]
ceil	round to integral value in floating-point format	[ floor, ceil, rint ]
class	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
compound	exponential, logarithm, financial	[ exp2, exp10, log2, compound, annuity ]
convert_external	convert external binary data formats
copysign	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
cos	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
cosd	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
cosh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
cosp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
cospi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
erf	error functions	[ erf, erfc ]
erfc	error functions	[ erf, erfc ]
exp	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
exp10	exponential, logarithm, financial	[ exp2, exp10, log2, compound, annuity ]
exp2	exponential, logarithm, financial	[ exp2, exp10, log2, compound, annuity ]
expm1	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
fabs	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
finite	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
floor	round to integral value in floating-point format	[ floor, ceil, rint ]
fmod	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
fp_class	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
gamma	log gamma function	[ lgamma, gamma ]
gamma_r	log gamma function	[ lgamma, gamma ]
hyperbolic	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
hypot	Euclidean distance
ieee_flags	mode and status function for IEEE standard arithmetic
ieee_functions	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
ieee_handler	IEEE exception trap handler function
ieee_retrospective	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
ieee_sun	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
ieee_test	IEEE test functions for verifying standard compliance	[ logb, scalb, significand ]
ieee_values	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
ilogb	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
infinity	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
intro	introduction to mathematical library functions and constants
irint	round to integral value in floating-point or integer format	[ aint, anint, irint, nint ]
isinf	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
isnan	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
isnormal	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
issubnormal	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
iszero	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
j0	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
j1	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
jn	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
lcrans	linear congruential pseudo-random number generators
lgamma	log gamma function	[ lgamma, gamma ]
lgamma_r	log gamma function	[ lgamma, gamma ]
log	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
log10	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
log1p	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
log2	exponential, logarithm, financial	[ exp2, exp10, log2, compound, annuity ]
logb	IEEE test functions for verifying standard compliance	[ logb, scalb, significand ]
matherr	math library exception-handling function
max_normal	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
max_subnormal	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
min_normal	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
min_subnormal	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
nextafter	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
nint	round to integral value in floating-point or integer format	[ aint, anint, irint, nint ]
nonstandard_arithmetic	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
pow	exponential, logarithm, power	[ exp, expm1, log, log1p, log10, pow ]
quad_precision	Quadruple-precision access to libm and libsunmath functions
quiet_nan	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
remainder	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
rint	round to integral value in floating-point format	[ floor, ceil, rint ]
scalb	IEEE test functions for verifying standard compliance	[ logb, scalb, significand ]
scalbn	appendix and related miscellaneous functions for IEEE arithmetic	[ ilogb, isnan, copysign, fabs, finite, fmod, nextafter, remainder, scalbn ]
shufrans	random number shufflers
signaling_nan	functions that return extreme values of IEEE arithmetic	[ ieee_values, min_subnormal, max_subnormal, min_normal, max_normal, infinity, quiet_nan, signaling_nan ]
signbit	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
significand	IEEE test functions for verifying standard compliance	[ logb, scalb, significand ]
sin	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
sincos	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sincosd	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sincosp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sincospi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sind	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
single_precision	Single-precision access to libm and libsunmath functions
sinh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
sinp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sinpi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
sqrt	square root, cube root	[ sqrt, cbrt ]
standard_arithmetic	miscellaneous functions for IEEE arithmetic	[ fp_class, isinf, isnormal, issubnormal, iszero, signbit, nonstandard_arithmetic, standard_arithmetic, ieee_retrospective ]
tan	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
tand	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
tanh	hyperbolic functions	[ sinh, cosh, tanh, asinh, acosh, atanh ]
tanp	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
tanpi	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
trig	trigonometric functions	[ sin, cos, tan, asin, acos, atan, atan2 ]
trig_sun	more trigonometric functions	[ sincos, sind, cosd, tand, asind, acosd, atand, atan2d, sincosd, sinp, cosp, tanp, asinp, acosp, atanp, sincosp, sinpi, cospi, tanpi, asinpi, acospi, atanpi, atan2pi, sincospi ]
y0	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
y1	Bessel functions	[ j0, j1, jn, y0, y1, yn ]
yn	Bessel functions	[ j0, j1, jn, y0, y1, yn ]

3V. POSIX/System V Compatibility Routines

printf	formatted output conversion	[ printf, fprintf, sprintf ]
scanf	formatted input conversion	[ scanf, fscanf, sscanf ]

3m. Math Library

HUGE
HUGE_VAL
List
list

4. Device Drivers

dbxinit	commands to dbx	[ dbxinit, .dbxinit ]
dbxrc	commands to dbx	[ dbxrc, .dbxrc ]
sbinit	directives to SourceBrowser and compilers	[ .sbinit ]

5. File Formats

access_control	CodeManager access control file
args	CodeManager argument caching file
children	List of a workspace’s child workspaces
conflicts	List of files in conflict in a workspace
floatingpoint	IEEE floating point definitions
freezepointfile	format of a freezepoint file
locks	CodeManager locks file
math	math functions and constants	[ HUGE, HUGE_VAL ]
nametable	CodeManager file name table
notification	CodeManager notification file
parent	Path name of a workspace’s parent

l. Local Commands

caxpy.l	Compute y := alpha ∗ x + y	[ CAXPY ]
cbdsqr.l	compute the singular value decomposition (SVD) of a real N-by-N (upper or lower) bidiagonal matrix B	[ cbdsqr ]
cchdc.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ cchdc ]
cchdd.l	downdate an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ cchdd ]
cchex.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ cchex ]
cchud.l	update an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ cchud ]
ccopy.l	Copy x to y	[ CCOPY ]
cdotc.l	Compute the dot product of two vectors x and conjg(y).	[ CDOTU ]
cdotu.l	Compute the dot product of two vectors x and y.	[ CDOTU ]
cfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ cfftb ]
cfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ cfftf ]
cffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ cffti ]
cgbbrd.l	reduce a complex general m-by-n band matrix A to real upper bidiagonal form B by a unitary transformation	[ cgbbrd ]
cgbco.l	compute the LU factorization and condition number of a general matrix A in banded storage. If the condition number is not needed then xGBFA is slightly faster. It is typical to follow a call to xGBCO with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ cgbco ]
cgbcon.l	estimate the reciprocal of the condition number of a complex general band matrix A, in either the 1-norm or the infinity-norm,	[ cgbcon ]
cgbdi.l	compute the determinant of a general matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA.	[ cgbdi ]
cgbequ.l	compute row and column scalings intended to equilibrate an M-by-N band matrix A and reduce its condition number	[ cgbequ ]
cgbfa.l	compute the LU factorization of a matrix A in banded storage. It is typical to follow a call to xGBFA with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ cgbfa ]
cgbmv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y, or y := alpha∗A’∗x + beta∗y, or y := alpha∗conjg( A’ )∗x + beta∗y	[ cgbmv ]
cgbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is banded, and provides error bounds and backward error estimates for the solution	[ cgbrfs ]
cgbsl.l	solve the linear system Ax = b for a matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA, and vectors b and x.	[ cgbsl ]
cgbsv.l	compute the solution to a complex system of linear equations A ∗ X = B, where A is a band matrix of order N with KL subdiagonals and KU superdiagonals, and X and B are N-by-NRHS matrices	[ cgbsv ]
cgbsvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ cgbsvx ]
cgbtf2.l	compute an LU factorization of a complex m-by-n band matrix A using partial pivoting with row interchanges	[ cgbtf2 ]
cgbtrf.l	compute an LU factorization of a complex m-by-n band matrix A using partial pivoting with row interchanges	[ cgbtrf ]
cgbtrs.l	solve a system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B with a general band matrix A using the LU factorization computed by CGBTRF	[ cgbtrs ]
cgebak.l	form the right or left eigenvectors of a complex general matrix by backward transformation on the computed eigenvectors of the balanced matrix output by CGEBAL	[ cgebak ]
cgebal.l	balance a general complex matrix A	[ cgebal ]
cgebd2.l	reduce a complex general m by n matrix A to upper or lower real bidiagonal form B by a unitary transformation	[ cgebd2 ]
cgebrd.l	reduce a general complex M-by-N matrix A to upper or lower bidiagonal form B by a unitary transformation	[ cgebrd ]
cgeco.l	compute the LU factorization and estimate the condition number of a general matrix A. If the condition number is not needed then xGEFA is slightly faster. It is typical to follow a call to xGECO with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant and inverse of A.	[ cgeco ]
cgecon.l	estimate the reciprocal of the condition number of a general complex matrix A, in either the 1-norm or the infinity-norm, using the LU factorization computed by CGETRF	[ cgecon ]
cgedi.l	compute the determinant and inverse of a general matrix A, which has been LU-factored by xGECO or xGEFA.	[ cgedi ]
cgeequ.l	compute row and column scalings intended to equilibrate an M-by-N matrix A and reduce its condition number	[ cgeequ ]
cgees.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues, the Schur form T, and, optionally, the matrix of Schur vectors Z	[ cgees ]
cgeesx.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues, the Schur form T, and, optionally, the matrix of Schur vectors Z	[ cgeesx ]
cgeev.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ cgeev ]
cgeevx.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ cgeevx ]
cgefa.l	compute the LU factorization of a general matrix A. It is typical to follow a call to xGEFA with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant of A.	[ cgefa ]
cgegs.l	compute for a pair of N-by-N complex nonsymmetric matrices A,	[ cgegs ]
cgegv.l	compute for a pair of N-by-N complex nonsymmetric matrices A and B, the generalized eigenvalues (alpha, beta), and optionally,	[ cgegv ]
cgehd2.l	reduce a complex general matrix A to upper Hessenberg form H by a unitary similarity transformation	[ cgehd2 ]
cgehrd.l	reduce a complex general matrix A to upper Hessenberg form H by a unitary similarity transformation	[ cgehrd ]
cgelq2.l	compute an LQ factorization of a complex m by n matrix A	[ cgelq2 ]
cgelqf.l	compute an LQ factorization of a complex M-by-N matrix A	[ cgelqf ]
cgels.l	solve overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A	[ cgels ]
cgelss.l	compute the minimum norm solution to a complex linear least squares problem	[ cgelss ]
cgelsx.l	compute the minimum-norm solution to a complex linear least squares problem	[ cgelsx ]
cgemm.l	perform one of the matrix-matrix operations C := alpha∗op( A )∗op( B ) + beta∗C	[ cgemm ]
cgemv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y, or y := alpha∗A’∗x + beta∗y, or y := alpha∗conjg( A’ )∗x + beta∗y	[ cgemv ]
cgeql2.l	compute a QL factorization of a complex m by n matrix A	[ cgeql2 ]
cgeqlf.l	compute a QL factorization of a complex M-by-N matrix A	[ cgeqlf ]
cgeqpf.l	compute a QR factorization with column pivoting of a complex M-by-N matrix A	[ cgeqpf ]
cgeqr2.l	compute a QR factorization of a complex m by n matrix A	[ cgeqr2 ]
cgeqrf.l	compute a QR factorization of a complex M-by-N matrix A	[ cgeqrf ]
cgerc.l	perform the rank 1 operation A := alpha∗x∗conjg( y’ ) + A	[ cgerc ]
cgerfs.l	improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solution	[ cgerfs ]
cgerq2.l	compute an RQ factorization of a complex m by n matrix A	[ cgerq2 ]
cgerqf.l	compute an RQ factorization of a complex M-by-N matrix A	[ cgerqf ]
cgeru.l	perform the rank 1 operation A := alpha∗x∗y’ + A	[ cgeru ]
cgesl.l	solve the linear system Ax = b for a general matrix A, which has been LU- factored by xGECO or xGEFA, and vectors b and x.	[ cgesl ]
cgesv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ cgesv ]
cgesvd.l	compute the singular value decomposition (SVD) of a complex M-by-N matrix A, optionally computing the left and/or right singular vectors	[ cgesvd ]
cgesvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ cgesvx ]
cgetf2.l	compute an LU factorization of a general m-by-n matrix A using partial pivoting with row interchanges	[ cgetf2 ]
cgetrf.l	compute an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges	[ cgetrf ]
cgetri.l	compute the inverse of a matrix using the LU factorization computed by CGETRF	[ cgetri ]
cgetrs.l	solve a system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B with a general N-by-N matrix A using the LU factorization computed by CGETRF	[ cgetrs ]
cggbak.l	form the right or left eigenvectors of a complex generalized eigenvalue problem A∗x = lambda∗B∗x, by backward transformation on the computed eigenvectors of the balanced pair of matrices output by CGGBAL	[ cggbak ]
cggbal.l	balance a pair of general complex matrices (A,B)	[ cggbal ]
cggglm.l	solve a general Gauss-Markov linear model (GLM) problem	[ cggglm ]
cgghrd.l	reduce a pair of complex matrices (A,B) to generalized upper Hessenberg form using unitary transformations, where A is a general matrix and B is upper triangular	[ cgghrd ]
cgglse.l	solve the linear equality-constrained least squares (LSE) problem	[ cgglse ]
cggqrf.l	compute a generalized QR factorization of an N-by-M matrix A and an N-by-P matrix B	[ cggqrf ]
cggrqf.l	compute a generalized RQ factorization of an M-by-N matrix A and a P-by-N matrix B	[ cggrqf ]
cggsvd.l	compute the generalized singular value decomposition (GSVD) of an M-by-N complex matrix A and P-by-N complex matrix B	[ cggsvd ]
cggsvp.l	compute unitary matrices U, V and Q such that N-K-L K L U’∗A∗Q = K ( 0 A12 A13 ) if M-K-L >= 0	[ cggsvp ]
cgtcon.l	estimate the reciprocal of the condition number of a complex tridiagonal matrix A using the LU factorization as computed by CGTTRF	[ cgtcon ]
cgtrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is tridiagonal, and provides error bounds and backward error estimates for the solution	[ cgtrfs ]
cgtsl.l	solve the linear system Ax = b for a tridiagonal matrix A and vectors b and x.	[ cgtsl ]
cgtsv.l	solve the equation A∗X = B,	[ cgtsv ]
cgtsvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ cgtsvx ]
cgttrf.l	compute an LU factorization of a complex tridiagonal matrix A using elimination with partial pivoting and row interchanges	[ cgttrf ]
cgttrs.l	solve one of the systems of equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ cgttrs ]
chbev.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ chbev ]
chbevd.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ chbevd ]
chbevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ chbevx ]
chbgst.l	reduce a complex Hermitian-definite banded generalized eigenproblem A∗x = lambda∗B∗x to standard form C∗y = lambda∗y,	[ chbgst ]
chbgv.l	compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite banded eigenproblem, of the form A∗x=(lambda)∗B∗x	[ chbgv ]
chbmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ chbmv ]
chbtrd.l	reduce a complex Hermitian band matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ chbtrd ]
checon.l	estimate the reciprocal of the condition number of a complex Hermitian matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHETRF	[ checon ]
cheev.l	compute all eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ cheev ]
cheevd.l	compute all eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ cheevd ]
cheevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ cheevx ]
chegs2.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form	[ chegs2 ]
chegst.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form	[ chegst ]
chegv.l	compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ chegv ]
chemm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ chemm ]
chemv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ chemv ]
cher.l	perform the hermitian rank 1 operation A := alpha∗x∗conjg( x’ ) + A	[ cher ]
cher2.l	perform the hermitian rank 2 operation A := alpha∗x∗conjg( y’ ) + conjg( alpha )∗y∗conjg( x’ ) + A	[ cher2 ]
cher2k.l	perform one of the Hermitian rank 2k operations C := alpha∗A∗conjg( B’ ) + conjg( alpha )∗B∗conjg( A’ ) + beta∗C or C := alpha∗conjg( A’ )∗B + conjg( alpha )∗conjg( B’ )∗A + beta∗C	[ cher2k ]
cherfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian indefinite, and provides error bounds and backward error estimates for the solution	[ cherfs ]
cherk.l	perform one of the Hermitian rank k operations C := alpha∗A∗conjg( A’ ) + beta∗C or C := alpha∗conjg( A’ )∗A + beta∗C	[ cherk ]
chesv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ chesv ]
chesvx.l	use the diagonal pivoting factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ chesvx ]
chetd2.l	reduce a complex Hermitian matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ chetd2 ]
chetf2.l	compute the factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ chetf2 ]
chetrd.l	reduce a complex Hermitian matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ chetrd ]
chetrf.l	compute the factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ chetrf ]
chetri.l	compute the inverse of a complex Hermitian indefinite matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHETRF	[ chetri ]
chetrs.l	solve a system of linear equations A∗X = B with a complex Hermitian matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHETRF	[ chetrs ]
chgeqz.l	implement a single-shift version of the QZ method for finding the generalized eigenvalues w(i)=ALPHA(i)/BETA(i) of the equation det( A - w(i) B ) = 0 If JOB=’S’, then the pair (A,B) is simultaneously reduced to Schur form (i.e., A and B are both upper triangular) by applying one unitary tranformation (usually called Q) on the left and another (usually called Z) on the right	[ chgeqz ]
chico.l	compute the UDU factorization and condition number of a Hermitian matrix A. If the condition number is not needed then xHIFA is slightly faster. It is typical to follow a call to xHICO with a call to xHISL to solve Ax = b or to xHIDI to compute the determinant, inverse, and inertia of A.	[ chico ]
chidi.l	compute the determinant, inertia, and inverse of a Hermitian matrix A, which has been UDU-factored by xHICO or xHIFA.	[ chidi ]
chifa.l	compute the UDU factorization of a Hermitian matrix A. It is typical to follow a call to xHIFA with a call to xHISL to solve Ax = b or to xHIDI to compute the determinant, inverse, and inertia of A.	[ chifa ]
chisl.l	solve the linear system Ax = b for a Hermitian matrix A, which has been UDU-factored by xHICO or xHIFA, and vectors b and x.	[ chisl ]
chpco.l	compute the UDU factorization and condition number of a Hermitian matrix A in packed storage. If the condition number is not needed then xHPFA is slightly faster. It is typical to follow a call to xHPCO with a call to xHPSL to solve Ax = b or to xHPDI to compute the determinant, inverse, and inertia of A.	[ chpco ]
chpcon.l	estimate the reciprocal of the condition number of a complex Hermitian packed matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHPTRF	[ chpcon ]
chpdi.l	compute the determinant, inertia, and inverse of a Hermitian matrix A in packed storage, which has been UDU-factored by xHPCO or xHPFA.	[ chpdi ]
chpev.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix in packed storage	[ chpev ]
chpevd.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A in packed storage	[ chpevd ]
chpevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A in packed storage	[ chpevx ]
chpfa.l	compute the UDU factorization of a Hermitian matrix A in packed storage. It is typical to follow a call to xHPFA with a call to xHPSL to solve Ax = b or to xHPDI to compute the determinant, inverse, and inertia of A.	[ chpfa ]
chpgst.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form, using packed storage	[ chpgst ]
chpgv.l	compute all the eigenvalues and, optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ chpgv ]
chpmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ chpmv ]
chpr.l	perform the hermitian rank 1 operation A := alpha∗x∗conjg( x’ ) + A	[ chpr ]
chpr2.l	perform the Hermitian rank 2 operation A := alpha∗x∗conjg( y’ ) + conjg( alpha )∗y∗conjg( x’ ) + A	[ chpr2 ]
chprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian indefinite and packed, and provides error bounds and backward error estimates for the solution	[ chprfs ]
chpsl.l	solve the linear system Ax = b for a Hermitian matrix A in packed storage, which has been UDU-factored by xHPCO or xHPFA, and vectors b and x.	[ chpsl ]
chpsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ chpsv ]
chpsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B, where A is an N-by-N Hermitian matrix stored in packed format and X and B are N-by-NRHS matrices	[ chpsvx ]
chptrd.l	reduce a complex Hermitian matrix A stored in packed form to real symmetric tridiagonal form T by a unitary similarity transformation	[ chptrd ]
chptrf.l	compute the factorization of a complex Hermitian packed matrix A using the Bunch-Kaufman diagonal pivoting method	[ chptrf ]
chptri.l	compute the inverse of a complex Hermitian indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHPTRF	[ chptri ]
chptrs.l	solve a system of linear equations A∗X = B with a complex Hermitian matrix A stored in packed format using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by CHPTRF	[ chptrs ]
chsein.l	use inverse iteration to find specified right and/or left eigenvectors of a complex upper Hessenberg matrix H	[ chsein ]
chseqr.l	compute the eigenvalues of a complex upper Hessenberg matrix H, and, optionally, the matrices T and Z from the Schur decomposition H = Z T Z∗∗H, where T is an upper triangular matrix (the Schur form), and Z is the unitary matrix of Schur vectors	[ chseqr ]
clabrd.l	reduce the first NB rows and columns of a complex general m by n matrix A to upper or lower real bidiagonal form by a unitary transformation Q’ ∗ A ∗ P, and returns the matrices X and Y which are needed to apply the transformation to the unreduced part of A	[ clabrd ]
clacgv.l	conjugate a complex vector of length N	[ clacgv ]
clacon.l	estimate the 1-norm of a square, complex matrix A	[ clacon ]
clacpy.l	copie all or part of a two-dimensional matrix A to another matrix B	[ clacpy ]
clacrm.l	perform a very simple matrix-matrix multiplication	[ clacrm ]
clacrt.l	applie a plane rotation, where the cos and sin (C and S) are complex and the vectors CX and CY are complex	[ clacrt ]
cladiv.l	:= X / Y, where X and Y are complex	[ cladiv ]
claed0.l	the divide and conquer method, CLAED0 computes all eigenvalues of a symmetric tridiagonal matrix which is one diagonal block of those from reducing a dense or band Hermitian matrix and corresponding eigenvectors of the dense or band matrix	[ claed0 ]
claed7.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ claed7 ]
claed8.l	merge the two sets of eigenvalues together into a single sorted set	[ claed8 ]
claein.l	use inverse iteration to find a right or left eigenvector corresponding to the eigenvalue W of a complex upper Hessenberg matrix H	[ claein ]
claesy.l	compute the eigendecomposition of a 2-by-2 symmetric matrix ( ( A, B );( B, C ) ) provided the norm of the matrix of eigenvectors is larger than some threshold value	[ claesy ]
claev2.l	compute the eigendecomposition of a 2-by-2 Hermitian matrix [ A B ] [ CONJG(B) C ]	[ claev2 ]
clags2.l	compute 2-by-2 unitary matrices U, V and Q, such that if ( UPPER ) then U’∗A∗Q = U’∗( A1 A2 )∗Q = ( x 0 ) ( 0 A3 ) ( x x ) and V’∗B∗Q = V’∗( B1 B2 )∗Q = ( x 0 ) ( 0 B3 ) ( x x ) or if ( .NOT.UPPER ) then U’∗A∗Q = U’∗( A1 0 )∗Q = ( x x ) ( A2 A3 ) ( 0 x ) and V’∗B∗Q = V’∗( B1 0 )∗Q = ( x x ) ( B2 B3 ) ( 0 x ) where U = ( CSU SNU ), V = ( CSV SNV ),	[ clags2 ]
clagtm.l	perform a matrix-vector product of the form B := alpha ∗ A ∗ X + beta ∗ B where A is a tridiagonal matrix of order N, B and X are N by NRHS matrices, and alpha and beta are real scalars, each of which may be 0., 1., or -1	[ clagtm ]
clahef.l	compute a partial factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ clahef ]
clahqr.l	i an auxiliary routine called by CHSEQR to update the eigenvalues and Schur decomposition already computed by CHSEQR, by dealing with the Hessenberg submatrix in rows and columns ILO to IHI	[ clahqr ]
clahrd.l	reduce the first NB columns of a complex general n-by-(n-k+1) matrix A so that elements below the k-th subdiagonal are zero	[ clahrd ]
claic1.l	applie one step of incremental condition estimation in its simplest version	[ claic1 ]
clangb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n band matrix A, with kl sub-diagonals and ku super-diagonals	[ clangb ]
clange.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex matrix A	[ clange ]
clangt.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex tridiagonal matrix A	[ clangt ]
clanhb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n hermitian band matrix A, with k super-diagonals	[ clanhb ]
clanhe.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex hermitian matrix A	[ clanhe ]
clanhp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex hermitian matrix A, supplied in packed form	[ clanhp ]
clanhs.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a Hessenberg matrix A	[ clanhs ]
clanht.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex Hermitian tridiagonal matrix A	[ clanht ]
clansb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n symmetric band matrix A, with k super-diagonals	[ clansb ]
clansp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex symmetric matrix A, supplied in packed form	[ clansp ]
clansy.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex symmetric matrix A	[ clansy ]
clantb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n triangular band matrix A, with ( k + 1 ) diagonals	[ clantb ]
clantp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a triangular matrix A, supplied in packed form	[ clantp ]
clantr.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix A	[ clantr ]
clapll.l	two column vectors X and Y, let A = ( X Y )	[ clapll ]
clapmt.l	rearrange the columns of the M by N matrix X as specified by the permutation K(1),K(2),...,K(N) of the integers 1,...,N	[ clapmt ]
claqgb.l	equilibrate a general M by N band matrix A with KL subdiagonals and KU superdiagonals using the row and scaling factors in the vectors R and C	[ claqgb ]
claqge.l	equilibrate a general M by N matrix A using the row and scaling factors in the vectors R and C	[ claqge ]
claqhb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ claqhb ]
claqhe.l	equilibrate a Hermitian matrix A using the scaling factors in the vector S	[ claqhe ]
claqhp.l	equilibrate a Hermitian matrix A using the scaling factors in the vector S	[ claqhp ]
claqsb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ claqsb ]
claqsp.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ claqsp ]
claqsy.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ claqsy ]
clar2v.l	applie a vector of complex plane rotations with real cosines from both sides to a sequence of 2-by-2 complex Hermitian matrices,	[ clar2v ]
clarf.l	applie a complex elementary reflector H to a complex M-by-N matrix C, from either the left or the right	[ clarf ]
clarfb.l	applie a complex block reflector H or its transpose H’ to a complex M-by-N matrix C, from either the left or the right	[ clarfb ]
clarfg.l	generate a complex elementary reflector H of order n, such that H’ ∗ ( alpha ) = ( beta ), H’ ∗ H = I	[ clarfg ]
clarft.l	form the triangular factor T of a complex block reflector H of order n, which is defined as a product of k elementary reflectors	[ clarft ]
clarfx.l	applie a complex elementary reflector H to a complex m by n matrix C, from either the left or the right	[ clarfx ]
clargv.l	generate a vector of complex plane rotations with real cosines, determined by elements of the complex vectors x and y	[ clargv ]
clarnv.l	return a vector of n random complex numbers from a uniform or normal distribution	[ clarnv ]
clartg.l	generate a plane rotation so that [ CS SN ] [ F ] [ R ] [ __ ]	[ clartg ]
clartv.l	applie a vector of complex plane rotations with real cosines to elements of the complex vectors x and y	[ clartv ]
clascl.l	multiply the M by N complex matrix A by the real scalar CTO/CFROM	[ clascl ]
claset.l	initialize a 2-D array A to BETA on the diagonal and ALPHA on the offdiagonals	[ claset ]
clasr.l	perform the transformation A := P∗A, when SIDE = ’L’ or ’l’ ( Left-hand side ) A := A∗P’, when SIDE = ’R’ or ’r’ ( Right-hand side ) where A is an m by n complex matrix and P is an orthogonal matrix,	[ clasr ]
classq.l	return the values scl and ssq such that ( scl∗∗2 )∗ssq = x( 1 )∗∗2 +...+ x( n )∗∗2 + ( scale∗∗2 )∗sumsq,	[ classq ]
claswp.l	perform a series of row interchanges on the matrix A	[ claswp ]
clasyf.l	compute a partial factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ clasyf ]
clatbs.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ clatbs ]
clatps.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ clatps ]
clatrd.l	reduce NB rows and columns of a complex Hermitian matrix A to Hermitian tridiagonal form by a unitary similarity transformation Q’ ∗ A ∗ Q, and returns the matrices V and W which are needed to apply the transformation to the unreduced part of A	[ clatrd ]
clatrs.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ clatrs ]
clatzm.l	applie a Householder matrix generated by CTZRQF to a matrix	[ clatzm ]
clauu2.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ clauu2 ]
clauum.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ clauum ]
cosqb.l	synthesize a Fourier sequence from its representation in terms of a cosine series with odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ cosqb ]
cosqf.l	compute the Fourier coefficients in a cosine series representation with only odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ cosqf ]
cosqi.l	initialize the array xWSAVE, which is used in both xCOSQF and xCOSQB.	[ cosqi ]
cost.l	compute the discrete Fourier cosine transform of an even sequence. The xCOST transforms are unnormalized inverses of themselves, so a call of xCOST followed by another call of xCOST will multiply the input sequence by 2 ∗ (N - 1). The VxCOST transforms are normalized, so a call of VxCOST followed by a call of VxCOST will return the original sequence.	[ cost ]
costi.l	initialize the array xWSAVE, which is used in xCOST.	[ costi ]
cpbco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in banded storage. If the condition number is not needed then xPBFA is slightly faster. It is typical to follow a call to xPBCO with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ cpbco ]
cpbcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite band matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPBTRF	[ cpbcon ]
cpbdi.l	compute the determinant of a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA.	[ cpbdi ]
cpbequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite band matrix A and reduce its condition number (with respect to the two-norm)	[ cpbequ ]
cpbfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in banded storage. It is typical to follow a call to xPBFA with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ cpbfa ]
cpbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and banded, and provides error bounds and backward error estimates for the solution	[ cpbrfs ]
cpbsl.l	section solve the linear system Ax = b for a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA, and vectors b and x.	[ cpbsl ]
cpbstf.l	compute a split Cholesky factorization of a complex Hermitian positive definite band matrix A	[ cpbstf ]
cpbsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ cpbsv ]
cpbsvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ cpbsvx ]
cpbtf2.l	compute the Cholesky factorization of a complex Hermitian positive definite band matrix A	[ cpbtf2 ]
cpbtrf.l	compute the Cholesky factorization of a complex Hermitian positive definite band matrix A	[ cpbtrf ]
cpbtrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite band matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPBTRF	[ cpbtrs ]
cpoco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A. If the condition number is not needed then xPOFA is slightly faster. It is typical to follow a call to xPOCO with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ cpoco ]
cpocon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPOTRF	[ cpocon ]
cpodi.l	compute the determinant and inverse of a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO, xPOFA, or xQRDC.	[ cpodi ]
cpoequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite matrix A and reduce its condition number (with respect to the two-norm)	[ cpoequ ]
cpofa.l	compute a Cholesky factorization of a symmetric positive definite matrix A. It is typical to follow a call to xPOFA with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ cpofa ]
cporfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite,	[ cporfs ]
cposl.l	solve the linear system Ax = b for a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO or xPOFA, and vectors b and x.	[ cposl ]
cposv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ cposv ]
cposvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ cposvx ]
cpotf2.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A	[ cpotf2 ]
cpotrf.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A	[ cpotrf ]
cpotri.l	compute the inverse of a complex Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPOTRF	[ cpotri ]
cpotrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPOTRF	[ cpotrs ]
cppco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in packed storage. If the condition number is not needed then xPPFA is slightly faster. It is typical to follow a call to xPPCO with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ cppco ]
cppcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite packed matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPPTRF	[ cppcon ]
cppdi.l	compute the determinant and inverse of a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA.	[ cppdi ]
cppequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite matrix A in packed storage and reduce its condition number (with respect to the two-norm)	[ cppequ ]
cppfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in packed storage. It is typical to follow a call to xPPFA with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ cppfa ]
cpprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and packed, and provides error bounds and backward error estimates for the solution	[ cpprfs ]
cppsl.l	solve the linear system Ax = b for a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA, and vectors b and x.	[ cppsl ]
cppsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ cppsv ]
cppsvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ cppsvx ]
cpptrf.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A stored in packed format	[ cpptrf ]
cpptri.l	compute the inverse of a complex Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPPTRF	[ cpptri ]
cpptrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite matrix A in packed storage using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by CPPTRF	[ cpptrs ]
cptcon.l	compute the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite tridiagonal matrix using the factorization A = L∗D∗L∗∗H or A = U∗∗H∗D∗U computed by CPTTRF	[ cptcon ]
cpteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric positive definite tridiagonal matrix by first factoring the matrix using SPTTRF and then calling CBDSQR to compute the singular values of the bidiagonal factor	[ cpteqr ]
cptrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and tridiagonal, and provides error bounds and backward error estimates for the solution	[ cptrfs ]
cptsl.l	solve the linear system Ax = b for a symmetric positive definite tridiagonal matrix A and vectors b and x.	[ cptsl ]
cptsv.l	compute the solution to a complex system of linear equations A∗X = B, where A is an N-by-N Hermitian positive definite tridiagonal matrix, and X and B are N-by-NRHS matrices	[ cptsv ]
cptsvx.l	use the factorization A = L∗D∗L∗∗H to compute the solution to a complex system of linear equations A∗X = B, where A is an N-by-N Hermitian positive definite tridiagonal matrix and X and B are N-by-NRHS matrices	[ cptsvx ]
cpttrf.l	compute the factorization of a complex Hermitian positive definite tridiagonal matrix A	[ cpttrf ]
cpttrs.l	solve a system of linear equations A ∗ X = B with a Hermitian positive definite tridiagonal matrix A using the factorization A = U∗∗H∗D∗U or A = L∗D∗L∗∗H computed by CPTTRF	[ cpttrs ]
cqrdc.l	compute the QR factorization of a general matrix A. It is typical to follow a call to xQRDC with a call to xQRSL to solve Ax = b or to xPODI to compute the determinant of A.	[ cqrdc ]
cqrsl.l	solve the linear system Ax = b for a general matrix A, which has been QR- factored by xQRDC, and vectors b and x.	[ cqrsl ]
crot.l	apply a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex	[ crot ]
crotg.l	Construct a Given’s plane rotation	[ CROTG ]
cscal.l	Compute y := alpha ∗ y	[ CSCAL ]
csico.l	compute the UDU factorization and condition number of a symmetric matrix A. If the condition number is not needed then xSIFA is slightly faster. It is typical to follow a call to xSICO with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ csico ]
csidi.l	compute the determinant, inertia, and inverse of a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA.	[ csidi ]
csifa.l	compute the UDU factorization of a symmetric matrix A. It is typical to follow a call to xSIFA with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ csifa ]
csisl.l	solve the linear system Ax = b for a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA, and vectors b and x.	[ csisl ]
cspco.l	compute the UDU factorization and condition number of a symmetric matrix A in packed storage. If the condition number is not needed then xSPFA is slightly faster. It is typical to follow a call to xSPCO with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ cspco ]
cspcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex symmetric packed matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSPTRF	[ cspcon ]
cspdi.l	compute the determinant, inertia, and inverse of a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA.	[ cspdi ]
cspfa.l	compute the UDU factorization of a symmetric matrix A in packed storage. It is typical to follow a call to xSPFA with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ cspfa ]
cspmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y,	[ cspmv ]
cspr.l	perform the symmetric rank 1 operation A := alpha∗x∗conjg( x’ ) + A,	[ cspr ]
csprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite and packed, and provides error bounds and backward error estimates for the solution	[ csprfs ]
cspsl.l	solve the linear system Ax = b for a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA, and vectors b and x.	[ cspsl ]
cspsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ cspsv ]
cspsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T to compute the solution to a complex system of linear equations A ∗ X = B, where A is an N-by-N symmetric matrix stored in packed format and X and B are N-by-NRHS matrices	[ cspsvx ]
csptrf.l	compute the factorization of a complex symmetric matrix A stored in packed format using the Bunch-Kaufman diagonal pivoting method	[ csptrf ]
csptri.l	compute the inverse of a complex symmetric indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSPTRF	[ csptri ]
csptrs.l	solve a system of linear equations A∗X = B with a complex symmetric matrix A stored in packed format using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSPTRF	[ csptrs ]
csrot.l	Apply a Given’s rotation constructed by SROTG.	[ SROT ]
csrscl.l	multiply an n-element complex vector x by the real scalar 1/a	[ csrscl ]
csscal.l	Compute y := alpha ∗ y	[ csscal ]
cstedc.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ cstedc ]
cstein.l	compute the eigenvectors of a real symmetric tridiagonal matrix T corresponding to specified eigenvalues, using inverse iteration	[ cstein ]
csteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the implicit QL or QR method	[ csteqr ]
csvdc.l	compute the singular value decomposition of a general matrix A.	[ csvdc ]
cswap.l	Exchange vectors x and y.	[ CSWAP ]
csycon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSYTRF	[ csycon ]
csymm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ csymm ]
csymv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y,	[ csymv ]
csyr.l	perform the symmetric rank 1 operation A := alpha∗x∗( x’ ) + A,	[ csyr ]
csyr2k.l	perform one of the symmetric rank 2k operations C := alpha∗A∗B’ + alpha∗B∗A’ + beta∗C or C := alpha∗A’∗B + alpha∗B’∗A + beta∗C	[ csyr2k ]
csyrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite, and provides error bounds and backward error estimates for the solution	[ csyrfs ]
csyrk.l	perform one of the symmetric rank k operations C := alpha∗A∗A’ + beta∗C or C := alpha∗A’∗A + beta∗C	[ csyrk ]
csysv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ csysv ]
csysvx.l	use the diagonal pivoting factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ csysvx ]
csytf2.l	compute the factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ csytf2 ]
csytrf.l	compute the factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ csytrf ]
csytri.l	compute the inverse of a complex symmetric indefinite matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSYTRF	[ csytri ]
csytrs.l	solve a system of linear equations A∗X = B with a complex symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by CSYTRF	[ csytrs ]
ctbcon.l	estimate the reciprocal of the condition number of a triangular band matrix A, in either the 1-norm or the infinity-norm	[ ctbcon ]
ctbmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ctbmv ]
ctbrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular band coefficient matrix	[ ctbrfs ]
ctbsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ctbsv ]
ctbtrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ctbtrs ]
ctgevc.l	compute some or all of the right and/or left generalized eigenvectors of a pair of complex upper triangular matrices (A,B)	[ ctgevc ]
ctgsja.l	compute the generalized singular value decomposition (GSVD) of two complex upper triangular (or trapezoidal) matrices A and B	[ ctgsja ]
ctpcon.l	estimate the reciprocal of the condition number of a packed triangular matrix A, in either the 1-norm or the infinity-norm	[ ctpcon ]
ctpmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ctpmv ]
ctprfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular packed coefficient matrix	[ ctprfs ]
ctpsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ctpsv ]
ctptri.l	compute the inverse of a complex upper or lower triangular matrix A stored in packed format	[ ctptri ]
ctptrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ctptrs ]
ctrco.l	estimate the condition number of a triangular matrix A. It is typical to follow a call to xTRCO with a call to xTRSL to solve Ax = b or to xTRDI to compute the determinant and inverse of A.	[ ctrco ]
ctrcon.l	estimate the reciprocal of the condition number of a triangular matrix A, in either the 1-norm or the infinity-norm	[ ctrcon ]
ctrdi.l	compute the determinant and inverse of a triangular matrix A.	[ ctrdi ]
ctrevc.l	compute some or all of the right and/or left eigenvectors of a complex upper triangular matrix T	[ ctrevc ]
ctrexc.l	reorder the Schur factorization of a complex matrix A = Q∗T∗Q∗∗H, so that the diagonal element of T with row index IFST is moved to row ILST	[ ctrexc ]
ctrmm.l	perform one of the matrix-matrix operations B := alpha∗op( A )∗B, or B := alpha∗B∗op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A’ or op( A ) = conjg( A’ )	[ ctrmm ]
ctrmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ctrmv ]
ctrrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix	[ ctrrfs ]
ctrsen.l	reorder the Schur factorization of a complex matrix A = Q∗T∗Q∗∗H, so that a selected cluster of eigenvalues appears in the leading positions on the diagonal of the upper triangular matrix T, and the leading columns of Q form an orthonormal basis of the corresponding right invariant subspace	[ ctrsen ]
ctrsl.l	solve the linear system Ax = b for a triangular matrix A and vectors b and x.	[ ctrsl ]
ctrsm.l	solve one of the matrix equations op( A )∗X = alpha∗B, or X∗op( A ) = alpha∗B	[ ctrsm ]
ctrsna.l	estimate reciprocal condition numbers for specified eigenvalues and/or right eigenvectors of a complex upper triangular matrix T (or of any matrix Q∗T∗Q∗∗H with Q unitary)	[ ctrsna ]
ctrsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ctrsv ]
ctrsyl.l	solve the complex Sylvester matrix equation	[ ctrsyl ]
ctrti2.l	compute the inverse of a complex upper or lower triangular matrix	[ ctrti2 ]
ctrtri.l	compute the inverse of a complex upper or lower triangular matrix A	[ ctrtri ]
ctrtrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ctrtrs ]
ctzrqf.l	reduce the M-by-N ( M<=N ) complex upper trapezoidal matrix A to upper triangular form by means of unitary transformations	[ ctzrqf ]
cung2l.l	generate an m by n complex matrix Q with orthonormal columns,	[ cung2l ]
cung2r.l	generate an m by n complex matrix Q with orthonormal columns,	[ cung2r ]
cungbr.l	generate one of the complex unitary matrices Q or P∗∗H determined by CGEBRD when reducing a complex matrix A to bidiagonal form	[ cungbr ]
cunghr.l	generate a complex unitary matrix Q which is defined as the product of IHI-ILO elementary reflectors of order N, as returned by CGEHRD	[ cunghr ]
cungl2.l	generate an m-by-n complex matrix Q with orthonormal rows,	[ cungl2 ]
cunglq.l	generate an M-by-N complex matrix Q with orthonormal rows,	[ cunglq ]
cungql.l	generate an M-by-N complex matrix Q with orthonormal columns,	[ cungql ]
cungqr.l	generate an M-by-N complex matrix Q with orthonormal columns,	[ cungqr ]
cungr2.l	generate an m by n complex matrix Q with orthonormal rows,	[ cungr2 ]
cungrq.l	generate an M-by-N complex matrix Q with orthonormal rows,	[ cungrq ]
cungtr.l	generate a complex unitary matrix Q which is defined as the product of n-1 elementary reflectors of order N, as returned by CHETRD	[ cungtr ]
cunm2l.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ cunm2l ]
cunm2r.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ cunm2r ]
cunmbr.l	VECT = ’Q’, CUNMBR overwrites the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmbr ]
cunmhr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmhr ]
cunml2.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ cunml2 ]
cunmlq.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmlq ]
cunmql.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmql ]
cunmqr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmqr ]
cunmr2.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ cunmr2 ]
cunmrq.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmrq ]
cunmtr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cunmtr ]
cupgtr.l	generate a complex unitary matrix Q which is defined as the product of n-1 elementary reflectors H(i) of order n, as returned by CHPTRD using packed storage	[ cupgtr ]
cupmtr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ cupmtr ]
dasum.l	Return the sum of the absolute values of a vector x.	[ DASUM ]
daxpy.l	Compute y := alpha ∗ x + y	[ DAXPY ]
dbdsqr.l	compute the singular value decomposition (SVD) of a real N-by-N (upper or lower) bidiagonal matrix B	[ dbdsqr ]
dchdc.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ dchdc ]
dchdd.l	downdate an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ dchdd ]
dchex.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ dchex ]
dchud.l	update an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ dchud ]
dcopy.l	Copy x to y	[ DCOPY ]
dcosqb.l	synthesize a Fourier sequence from its representation in terms of a cosine series with odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ dcosqb ]
dcosqf.l	compute the Fourier coefficients in a cosine series representation with only odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ dcosqf ]
dcosqi.l	initialize the array xWSAVE, which is used in both xCOSQF and xCOSQB.	[ dcosqi ]
dcost.l	compute the discrete Fourier cosine transform of an even sequence. The xCOST transforms are unnormalized inverses of themselves, so a call of xCOST followed by another call of xCOST will multiply the input sequence by 2 ∗ (N - 1). The VxCOST transforms are normalized, so a call of VxCOST followed by a call of VxCOST will return the original sequence.	[ dcost ]
dcosti.l	initialize the array xWSAVE, which is used in xCOST.	[ dcosti ]
ddisna.l	compute the reciprocal condition numbers for the eigenvectors of a real symmetric or complex Hermitian matrix or for the left or right singular vectors of a general m-by-n matrix	[ ddisna ]
ddot.l	Compute the dot product of two vectors x and y.	[ DDOT ]
dfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ dfftb ]
dfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ dfftf ]
dffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ dffti ]
dgbbrd.l	reduce a real general m-by-n band matrix A to upper bidiagonal form B by an orthogonal transformation	[ dgbbrd ]
dgbco.l	compute the LU factorization and condition number of a general matrix A in banded storage. If the condition number is not needed then xGBFA is slightly faster. It is typical to follow a call to xGBCO with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ dgbco ]
dgbcon.l	estimate the reciprocal of the condition number of a real general band matrix A, in either the 1-norm or the infinity-norm,	[ dgbcon ]
dgbdi.l	compute the determinant of a general matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA.	[ dgbdi ]
dgbequ.l	compute row and column scalings intended to equilibrate an M-by-N band matrix A and reduce its condition number	[ dgbequ ]
dgbfa.l	compute the LU factorization of a matrix A in banded storage. It is typical to follow a call to xGBFA with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ dgbfa ]
dgbmv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y or y := alpha∗A’∗x + beta∗y	[ dgbmv ]
dgbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is banded, and provides error bounds and backward error estimates for the solution	[ dgbrfs ]
dgbsl.l	solve the linear system Ax = b for a matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA, and vectors b and x.	[ dgbsl ]
dgbsv.l	compute the solution to a real system of linear equations A ∗ X = B, where A is a band matrix of order N with KL subdiagonals and KU superdiagonals, and X and B are N-by-NRHS matrices	[ dgbsv ]
dgbsvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ dgbsvx ]
dgbtf2.l	compute an LU factorization of a real m-by-n band matrix A using partial pivoting with row interchanges	[ dgbtf2 ]
dgbtrf.l	compute an LU factorization of a real m-by-n band matrix A using partial pivoting with row interchanges	[ dgbtrf ]
dgbtrs.l	solve a system of linear equations A ∗ X = B or A’ ∗ X = B with a general band matrix A using the LU factorization computed by DGBTRF	[ dgbtrs ]
dgebak.l	form the right or left eigenvectors of a real general matrix by backward transformation on the computed eigenvectors of the balanced matrix output by DGEBAL	[ dgebak ]
dgebal.l	balance a general real matrix A	[ dgebal ]
dgebd2.l	reduce a real general m by n matrix A to upper or lower bidiagonal form B by an orthogonal transformation	[ dgebd2 ]
dgebrd.l	reduce a general real M-by-N matrix A to upper or lower bidiagonal form B by an orthogonal transformation	[ dgebrd ]
dgeco.l	compute the LU factorization and estimate the condition number of a general matrix A. If the condition number is not needed then xGEFA is slightly faster. It is typical to follow a call to xGECO with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant and inverse of A.	[ dgeco ]
dgecon.l	estimate the reciprocal of the condition number of a general real matrix A, in either the 1-norm or the infinity-norm, using the LU factorization computed by DGETRF	[ dgecon ]
dgedi.l	compute the determinant and inverse of a general matrix A, which has been LU-factored by xGECO or xGEFA.	[ dgedi ]
dgeequ.l	compute row and column scalings intended to equilibrate an M-by-N matrix A and reduce its condition number	[ dgeequ ]
dgees.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues, the real Schur form T, and, optionally, the matrix of Schur vectors Z	[ dgees ]
dgeesx.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues, the real Schur form T, and, optionally, the matrix of Schur vectors Z	[ dgeesx ]
dgeev.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ dgeev ]
dgeevx.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ dgeevx ]
dgefa.l	compute the LU factorization of a general matrix A. It is typical to follow a call to xGEFA with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant of A.	[ dgefa ]
dgegs.l	compute for a pair of N-by-N real nonsymmetric matrices A, B	[ dgegs ]
dgegv.l	compute for a pair of n-by-n real nonsymmetric matrices A and B, the generalized eigenvalues (alphar +/- alphai∗i, beta), and optionally, the left and/or right generalized eigenvectors (VL and VR)	[ dgegv ]
dgehd2.l	reduce a real general matrix A to upper Hessenberg form H by an orthogonal similarity transformation	[ dgehd2 ]
dgehrd.l	reduce a real general matrix A to upper Hessenberg form H by an orthogonal similarity transformation	[ dgehrd ]
dgelq2.l	compute an LQ factorization of a real m by n matrix A	[ dgelq2 ]
dgelqf.l	compute an LQ factorization of a real M-by-N matrix A	[ dgelqf ]
dgels.l	solve overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A	[ dgels ]
dgelss.l	compute the minimum norm solution to a real linear least squares problem	[ dgelss ]
dgelsx.l	compute the minimum-norm solution to a real linear least squares problem	[ dgelsx ]
dgemm.l	perform one of the matrix-matrix operations C := alpha∗op( A )∗op( B ) + beta∗C	[ dgemm ]
dgemv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y or y := alpha∗A’∗x + beta∗y	[ dgemv ]
dgeql2.l	compute a QL factorization of a real m by n matrix A	[ dgeql2 ]
dgeqlf.l	compute a QL factorization of a real M-by-N matrix A	[ dgeqlf ]
dgeqpf.l	compute a QR factorization with column pivoting of a real M-by-N matrix A	[ dgeqpf ]
dgeqr2.l	compute a QR factorization of a real m by n matrix A	[ dgeqr2 ]
dgeqrf.l	compute a QR factorization of a real M-by-N matrix A	[ dgeqrf ]
dger.l	perform the rank 1 operation A := alpha∗x∗y’ + A	[ dger ]
dgerfs.l	improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solution	[ dgerfs ]
dgerq2.l	compute an RQ factorization of a real m by n matrix A	[ dgerq2 ]
dgerqf.l	compute an RQ factorization of a real M-by-N matrix A	[ dgerqf ]
dgesl.l	solve the linear system Ax = b for a general matrix A, which has been LU- factored by xGECO or xGEFA, and vectors b and x.	[ dgesl ]
dgesv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dgesv ]
dgesvd.l	compute the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors	[ dgesvd ]
dgesvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B,	[ dgesvx ]
dgetf2.l	compute an LU factorization of a general m-by-n matrix A using partial pivoting with row interchanges	[ dgetf2 ]
dgetrf.l	compute an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges	[ dgetrf ]
dgetri.l	compute the inverse of a matrix using the LU factorization computed by DGETRF	[ dgetri ]
dgetrs.l	solve a system of linear equations A ∗ X = B or A’ ∗ X = B with a general N-by-N matrix A using the LU factorization computed by DGETRF	[ dgetrs ]
dggbak.l	form the right or left eigenvectors of a real generalized eigenvalue problem A∗x = lambda∗B∗x, by backward transformation on the computed eigenvectors of the balanced pair of matrices output by DGGBAL	[ dggbak ]
dggbal.l	balance a pair of general real matrices (A,B)	[ dggbal ]
dggglm.l	solve a general Gauss-Markov linear model (GLM) problem	[ dggglm ]
dgghrd.l	reduce a pair of real matrices (A,B) to generalized upper Hessenberg form using orthogonal transformations, where A is a general matrix and B is upper triangular	[ dgghrd ]
dgglse.l	solve the linear equality-constrained least squares (LSE) problem	[ dgglse ]
dggqrf.l	compute a generalized QR factorization of an N-by-M matrix A and an N-by-P matrix B	[ dggqrf ]
dggrqf.l	compute a generalized RQ factorization of an M-by-N matrix A and a P-by-N matrix B	[ dggrqf ]
dggsvd.l	compute the generalized singular value decomposition (GSVD) of an M-by-N real matrix A and P-by-N real matrix B	[ dggsvd ]
dggsvp.l	compute orthogonal matrices U, V and Q such that N-K-L K L U’∗A∗Q = K ( 0 A12 A13 ) if M-K-L >= 0	[ dggsvp ]
dgtcon.l	estimate the reciprocal of the condition number of a real tridiagonal matrix A using the LU factorization as computed by DGTTRF	[ dgtcon ]
dgtrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is tridiagonal, and provides error bounds and backward error estimates for the solution	[ dgtrfs ]
dgtsl.l	solve the linear system Ax = b for a tridiagonal matrix A and vectors b and x.	[ dgtsl ]
dgtsv.l	solve the equation A∗X = B,	[ dgtsv ]
dgtsvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B or A∗∗T ∗ X = B,	[ dgtsvx ]
dgttrf.l	compute an LU factorization of a real tridiagonal matrix A using elimination with partial pivoting and row interchanges	[ dgttrf ]
dgttrs.l	solve one of the systems of equations A∗X = B or A’∗X = B,	[ dgttrs ]
dhgeqz.l	implement a single-/double-shift version of the QZ method for finding the generalized eigenvalues w(j)=(ALPHAR(j) + i∗ALPHAI(j))/BETAR(j) of the equation det( A - w(i) B ) = 0 In addition, the pair A,B may be reduced to generalized Schur form	[ dhgeqz ]
dhsein.l	use inverse iteration to find specified right and/or left eigenvectors of a real upper Hessenberg matrix H	[ dhsein ]
dhseqr.l	compute the eigenvalues of a real upper Hessenberg matrix H and, optionally, the matrices T and Z from the Schur decomposition H = Z T Z∗∗T, where T is an upper quasi-triangular matrix (the Schur form), and Z is the orthogonal matrix of Schur vectors	[ dhseqr ]
dlabad.l	take as input the values computed by SLAMCH for underflow and overflow, and returns the square root of each of these values if the log of LARGE is sufficiently large	[ dlabad ]
dlabrd.l	reduce the first NB rows and columns of a real general m by n matrix A to upper or lower bidiagonal form by an orthogonal transformation Q’ ∗ A ∗ P, and returns the matrices X and Y which are needed to apply the transformation to the unreduced part of A	[ dlabrd ]
dlacon.l	estimate the 1-norm of a square, real matrix A	[ dlacon ]
dlacpy.l	copie all or part of a two-dimensional matrix A to another matrix B	[ dlacpy ]
dladiv.l	perform complex division in real arithmetic a + i∗b p + i∗q = --------- c + i∗d The algorithm is due to Robert L	[ dladiv ]
dlae2.l	compute the eigenvalues of a 2-by-2 symmetric matrix [ A B ] [ B C ]	[ dlae2 ]
dlaebz.l	contain the iteration loops which compute and use the function N(w), which is the count of eigenvalues of a symmetric tridiagonal matrix T less than or equal to its argument w	[ dlaebz ]
dlaed0.l	compute all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ dlaed0 ]
dlaed1.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ dlaed1 ]
dlaed2.l	merge the two sets of eigenvalues together into a single sorted set	[ dlaed2 ]
dlaed3.l	find the roots of the secular equation, as defined by the values in D, W, and RHO, between KSTART and KSTOP	[ dlaed3 ]
dlaed4.l	subroutine computes the I-th updated eigenvalue of a symmetric rank-one modification to a diagonal matrix whose elements are given in the array d, and that D(i) < D(j) for i < j and that RHO > 0	[ dlaed4 ]
dlaed5.l	subroutine computes the I-th eigenvalue of a symmetric rank-one modification of a 2-by-2 diagonal matrix diag( D ) + RHO The diagonal elements in the array D are assumed to satisfy D(i) < D(j) for i < j	[ dlaed5 ]
dlaed6.l	compute the positive or negative root (closest to the origin) of z(1) z(2) z(3) f(x) = rho + --------- + ---------- + --------- d(1)-x d(2)-x d(3)-x It is assumed that if ORGATI = .true	[ dlaed6 ]
dlaed7.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ dlaed7 ]
dlaed8.l	merge the two sets of eigenvalues together into a single sorted set	[ dlaed8 ]
dlaed9.l	find the roots of the secular equation, as defined by the values in D, Z, and RHO, between KSTART and KSTOP	[ dlaed9 ]
dlaeda.l	compute the Z vector corresponding to the merge step in the CURLVLth step of the merge process with TLVLS steps for the CURPBMth problem	[ dlaeda ]
dlaein.l	use inverse iteration to find a right or left eigenvector corresponding to the eigenvalue (WR,WI) of a real upper Hessenberg matrix H	[ dlaein ]
dlaev2.l	compute the eigendecomposition of a 2-by-2 symmetric matrix [ A B ] [ B C ]	[ dlaev2 ]
dlaexc.l	swap adjacent diagonal blocks T11 and T22 of order 1 or 2 in an upper quasi-triangular matrix T by an orthogonal similarity transformation	[ dlaexc ]
dlag2.l	compute the eigenvalues of a 2 x 2 generalized eigenvalue problem A - w B, with scaling as necessary to avoid over-/underflow	[ dlag2 ]
dlags2.l	compute 2-by-2 orthogonal matrices U, V and Q, such that if ( UPPER ) then U’∗A∗Q = U’∗( A1 A2 )∗Q = ( x 0 ) ( 0 A3 ) ( x x ) and V’∗B∗Q = V’∗( B1 B2 )∗Q = ( x 0 ) ( 0 B3 ) ( x x ) or if ( .NOT.UPPER ) then U’∗A∗Q = U’∗( A1 0 )∗Q = ( x x ) ( A2 A3 ) ( 0 x ) and V’∗B∗Q = V’∗( B1 0 )∗Q = ( x x ) ( B2 B3 ) ( 0 x ) The rows of the transformed A and B are parallel, where U = ( CSU SNU ), V = ( CSV SNV ), Q = ( CSQ SNQ ) ( -SNU CSU ) ( -SNV CSV ) ( -SNQ CSQ ) Z’ denotes the transpose of Z	[ dlags2 ]
dlagtf.l	factorize the matrix (T - lambda∗I), where T is an n by n tridiagonal matrix and lambda is a scalar, as T - lambda∗I = PLU,	[ dlagtf ]
dlagtm.l	perform a matrix-vector product of the form B := alpha ∗ A ∗ X + beta ∗ B where A is a tridiagonal matrix of order N, B and X are N by NRHS matrices, and alpha and beta are real scalars, each of which may be 0., 1., or -1	[ dlagtm ]
dlagts.l	may be used to solve one of the systems of equations (T - lambda∗I)∗x = y or (T - lambda∗I)’∗x = y,	[ dlagts ]
dlahqr.l	i an auxiliary routine called by DHSEQR to update the eigenvalues and Schur decomposition already computed by DHSEQR, by dealing with the Hessenberg submatrix in rows and columns ILO to IHI	[ dlahqr ]
dlahrd.l	reduce the first NB columns of a real general n-by-(n-k+1) matrix A so that elements below the k-th subdiagonal are zero	[ dlahrd ]
dlaic1.l	applie one step of incremental condition estimation in its simplest version	[ dlaic1 ]
dlaln2.l	solve a system of the form (ca A - w D ) X = s B or (ca A’ - w D) X = s B with possible scaling ("s") and perturbation of A	[ dlaln2 ]
dlamch.l	determine double precision machine parameters	[ dlamch ]
dlamrg.l	will create a permutation list which will merge the elements of A (which is composed of two independently sorted sets) into a single set which is sorted in ascending order	[ dlamrg ]
dlangb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n band matrix A, with kl sub-diagonals and ku super-diagonals	[ dlangb ]
dlange.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real matrix A	[ dlange ]
dlangt.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real tridiagonal matrix A	[ dlangt ]
dlanhs.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a Hessenberg matrix A	[ dlanhs ]
dlansb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n symmetric band matrix A, with k super-diagonals	[ dlansb ]
dlansp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric matrix A, supplied in packed form	[ dlansp ]
dlanst.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric tridiagonal matrix A	[ dlanst ]
dlansy.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric matrix A	[ dlansy ]
dlantb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n triangular band matrix A, with ( k + 1 ) diagonals	[ dlantb ]
dlantp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a triangular matrix A, supplied in packed form	[ dlantp ]
dlantr.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix A	[ dlantr ]
dlanv2.l	compute the Schur factorization of a real 2-by-2 nonsymmetric matrix in standard form	[ dlanv2 ]
dlapll.l	two column vectors X and Y, let A = ( X Y )	[ dlapll ]
dlapmt.l	rearrange the columns of the M by N matrix X as specified by the permutation K(1),K(2),...,K(N) of the integers 1,...,N	[ dlapmt ]
dlapy2.l	return sqrt(x∗∗2+y∗∗2), taking care not to cause unnecessary overflow	[ dlapy2 ]
dlapy3.l	return sqrt(x∗∗2+y∗∗2+z∗∗2), taking care not to cause unnecessary overflow	[ dlapy3 ]
dlaqgb.l	equilibrate a general M by N band matrix A with KL subdiagonals and KU superdiagonals using the row and scaling factors in the vectors R and C	[ dlaqgb ]
dlaqge.l	equilibrate a general M by N matrix A using the row and scaling factors in the vectors R and C	[ dlaqge ]
dlaqsb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ dlaqsb ]
dlaqsp.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ dlaqsp ]
dlaqsy.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ dlaqsy ]
dlaqtr.l	solve the real quasi-triangular system op(T)∗p = scale∗c, if LREAL = .TRUE	[ dlaqtr ]
dlar2v.l	applie a vector of real plane rotations from both sides to a sequence of 2-by-2 real symmetric matrices, defined by the elements of the vectors x, y and z	[ dlar2v ]
dlarf.l	applie a real elementary reflector H to a real m by n matrix C, from either the left or the right	[ dlarf ]
dlarfb.l	applie a real block reflector H or its transpose H’ to a real m by n matrix C, from either the left or the right	[ dlarfb ]
dlarfg.l	generate a real elementary reflector H of order n, such that H ∗ ( alpha ) = ( beta ), H’ ∗ H = I	[ dlarfg ]
dlarft.l	form the triangular factor T of a real block reflector H of order n, which is defined as a product of k elementary reflectors	[ dlarft ]
dlarfx.l	applie a real elementary reflector H to a real m by n matrix C, from either the left or the right	[ dlarfx ]
dlargv.l	generate a vector of real plane rotations, determined by elements of the real vectors x and y	[ dlargv ]
dlarnv.l	return a vector of n random real numbers from a uniform or normal distribution	[ dlarnv ]
dlartg.l	generate a plane rotation so that [ CS SN ]	[ dlartg ]
dlartv.l	applie a vector of real plane rotations to elements of the real vectors x and y	[ dlartv ]
dlaruv.l	return a vector of n random real numbers from a uniform (0,1)	[ dlaruv ]
dlas2.l	compute the singular values of the 2-by-2 matrix [ F G ] [ 0 H ]	[ dlas2 ]
dlascl.l	multiply the M by N real matrix A by the real scalar CTO/CFROM	[ dlascl ]
dlaset.l	initialize an m-by-n matrix A to BETA on the diagonal and ALPHA on the offdiagonals	[ dlaset ]
dlasq1.l	DLASQ1 computes the singular values of a real N-by-N bidiagonal matrix with diagonal D and off-diagonal E	[ dlasq1 ]
dlasq2.l	DLASQ2 computes the singular values of a real N-by-N unreduced bidiagonal matrix with squared diagonal elements in Q and squared off-diagonal elements in E	[ dlasq2 ]
dlasq3.l	DLASQ3 is the workhorse of the whole bidiagonal SVD algorithm	[ dlasq3 ]
dlasq4.l	DLASQ4 estimates TAU, the smallest eigenvalue of a matrix	[ dlasq4 ]
dlasr.l	perform the transformation A := P∗A, when SIDE = ’L’ or ’l’ ( Left-hand side ) A := A∗P’, when SIDE = ’R’ or ’r’ ( Right-hand side ) where A is an m by n real matrix and P is an orthogonal matrix,	[ dlasr ]
dlasrt.l	the numbers in D in increasing order (if ID = ’I’) or in decreasing order (if ID = ’D’ )	[ dlasrt ]
dlassq.l	return the values scl and smsq such that ( scl∗∗2 )∗smsq = x( 1 )∗∗2 +...+ x( n )∗∗2 + ( scale∗∗2 )∗sumsq,	[ dlassq ]
dlasv2.l	compute the singular value decomposition of a 2-by-2 triangular matrix [ F G ] [ 0 H ]	[ dlasv2 ]
dlaswp.l	perform a series of row interchanges on the matrix A	[ dlaswp ]
dlasy2.l	solve for the N1 by N2 matrix X, 1 <= N1,N2 <= 2, in op(TL)∗X + ISGN∗X∗op(TR) = SCALE∗B,	[ dlasy2 ]
dlasyf.l	compute a partial factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ dlasyf ]
dlatbs.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow, where A is an upper or lower triangular band matrix	[ dlatbs ]
dlatps.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow, where A is an upper or lower triangular matrix stored in packed form	[ dlatps ]
dlatrd.l	reduce NB rows and columns of a real symmetric matrix A to symmetric tridiagonal form by an orthogonal similarity transformation Q’ ∗ A ∗ Q, and returns the matrices V and W which are needed to apply the transformation to the unreduced part of A	[ dlatrd ]
dlatrs.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow	[ dlatrs ]
dlatzm.l	applie a Householder matrix generated by DTZRQF to a matrix	[ dlatzm ]
dlauu2.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ dlauu2 ]
dlauum.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ dlauum ]
dnrm2.l	Return the Euclidian norm of a vector.	[ DNRM2 ]
dopgtr.l	generate a real orthogonal matrix Q which is defined as the product of n-1 elementary reflectors H(i) of order n, as returned by DSPTRD using packed storage	[ dopgtr ]
dopmtr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dopmtr ]
dorg2l.l	generate an m by n real matrix Q with orthonormal columns,	[ dorg2l ]
dorg2r.l	generate an m by n real matrix Q with orthonormal columns,	[ dorg2r ]
dorgbr.l	generate one of the real orthogonal matrices Q or P∗∗T determined by DGEBRD when reducing a real matrix A to bidiagonal form	[ dorgbr ]
dorghr.l	generate a real orthogonal matrix Q which is defined as the product of IHI-ILO elementary reflectors of order N, as returned by DGEHRD	[ dorghr ]
dorgl2.l	generate an m by n real matrix Q with orthonormal rows,	[ dorgl2 ]
dorglq.l	generate an M-by-N real matrix Q with orthonormal rows,	[ dorglq ]
dorgql.l	generate an M-by-N real matrix Q with orthonormal columns,	[ dorgql ]
dorgqr.l	generate an M-by-N real matrix Q with orthonormal columns,	[ dorgqr ]
dorgr2.l	generate an m by n real matrix Q with orthonormal rows,	[ dorgr2 ]
dorgrq.l	generate an M-by-N real matrix Q with orthonormal rows,	[ dorgrq ]
dorgtr.l	generate a real orthogonal matrix Q which is defined as the product of n-1 elementary reflectors of order N, as returned by DSYTRD	[ dorgtr ]
dorm2l.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ dorm2l ]
dorm2r.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ dorm2r ]
dormbr.l	VECT = ’Q’, DORMBR overwrites the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormbr ]
dormhr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormhr ]
dorml2.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ dorml2 ]
dormlq.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormlq ]
dormql.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormql ]
dormqr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormqr ]
dormr2.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ dormr2 ]
dormrq.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormrq ]
dormtr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ dormtr ]
dpbco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in banded storage. If the condition number is not needed then xPBFA is slightly faster. It is typical to follow a call to xPBCO with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ dpbco ]
dpbcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite band matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPBTRF	[ dpbcon ]
dpbdi.l	compute the determinant of a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA.	[ dpbdi ]
dpbequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite band matrix A and reduce its condition number (with respect to the two-norm)	[ dpbequ ]
dpbfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in banded storage. It is typical to follow a call to xPBFA with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ dpbfa ]
dpbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and banded, and provides error bounds and backward error estimates for the solution	[ dpbrfs ]
dpbsl.l	section solve the linear system Ax = b for a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA, and vectors b and x.	[ dpbsl ]
dpbstf.l	compute a split Cholesky factorization of a real symmetric positive definite band matrix A	[ dpbstf ]
dpbsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dpbsv ]
dpbsvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ dpbsvx ]
dpbtf2.l	compute the Cholesky factorization of a real symmetric positive definite band matrix A	[ dpbtf2 ]
dpbtrf.l	compute the Cholesky factorization of a real symmetric positive definite band matrix A	[ dpbtrf ]
dpbtrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite band matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPBTRF	[ dpbtrs ]
dpoco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A. If the condition number is not needed then xPOFA is slightly faster. It is typical to follow a call to xPOCO with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ dpoco ]
dpocon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPOTRF	[ dpocon ]
dpodi.l	compute the determinant and inverse of a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO, xPOFA, or xQRDC.	[ dpodi ]
dpoequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite matrix A and reduce its condition number (with respect to the two-norm)	[ dpoequ ]
dpofa.l	compute a Cholesky factorization of a symmetric positive definite matrix A. It is typical to follow a call to xPOFA with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ dpofa ]
dporfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite,	[ dporfs ]
dposl.l	solve the linear system Ax = b for a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO or xPOFA, and vectors b and x.	[ dposl ]
dposv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dposv ]
dposvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ dposvx ]
dpotf2.l	compute the Cholesky factorization of a real symmetric positive definite matrix A	[ dpotf2 ]
dpotrf.l	compute the Cholesky factorization of a real symmetric positive definite matrix A	[ dpotrf ]
dpotri.l	compute the inverse of a real symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPOTRF	[ dpotri ]
dpotrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPOTRF	[ dpotrs ]
dppco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in packed storage. If the condition number is not needed then xPPFA is slightly faster. It is typical to follow a call to xPPCO with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ dppco ]
dppcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite packed matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPPTRF	[ dppcon ]
dppdi.l	compute the determinant and inverse of a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA.	[ dppdi ]
dppequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite matrix A in packed storage and reduce its condition number (with respect to the two-norm)	[ dppequ ]
dppfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in packed storage. It is typical to follow a call to xPPFA with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ dppfa ]
dpprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and packed, and provides error bounds and backward error estimates for the solution	[ dpprfs ]
dppsl.l	solve the linear system Ax = b for a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA, and vectors b and x.	[ dppsl ]
dppsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dppsv ]
dppsvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ dppsvx ]
dpptrf.l	compute the Cholesky factorization of a real symmetric positive definite matrix A stored in packed format	[ dpptrf ]
dpptri.l	compute the inverse of a real symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPPTRF	[ dpptri ]
dpptrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite matrix A in packed storage using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by DPPTRF	[ dpptrs ]
dptcon.l	compute the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite tridiagonal matrix using the factorization A = L∗D∗L∗∗T or A = U∗∗T∗D∗U computed by DPTTRF	[ dptcon ]
dpteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric positive definite tridiagonal matrix by first factoring the matrix using DPTTRF, and then calling DBDSQR to compute the singular values of the bidiagonal factor	[ dpteqr ]
dptrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and tridiagonal, and provides error bounds and backward error estimates for the solution	[ dptrfs ]
dptsl.l	solve the linear system Ax = b for a symmetric positive definite tridiagonal matrix A and vectors b and x.	[ dptsl ]
dptsv.l	compute the solution to a real system of linear equations A∗X = B, where A is an N-by-N symmetric positive definite tridiagonal matrix, and X and B are N-by-NRHS matrices	[ dptsv ]
dptsvx.l	use the factorization A = L∗D∗L∗∗T to compute the solution to a real system of linear equations A∗X = B, where A is an N-by-N symmetric positive definite tridiagonal matrix and X and B are N-by-NRHS matrices	[ dptsvx ]
dpttrf.l	compute the factorization of a real symmetric positive definite tridiagonal matrix A	[ dpttrf ]
dpttrs.l	solve a system of linear equations A ∗ X = B with a symmetric positive definite tridiagonal matrix A using the factorization A = L∗D∗L∗∗T or A = U∗∗T∗D∗U computed by DPTTRF	[ dpttrs ]
dqdota.l	Compute a double precision constant plus an extended precision constant plus the extended precision dot product of two double precision vectors x and y.	[ DQDOTA ]
dqdoti.l	Compute a constant plus the extended precision dot product of two double precision vectors x and y.	[ DQDOTI ]
dqrdc.l	compute the QR factorization of a general matrix A. It is typical to follow a call to xQRDC with a call to xQRSL to solve Ax = b or to xPODI to compute the determinant of A.	[ dqrdc ]
dqrsl.l	solve the linear system Ax = b for a general matrix A, which has been QR- factored by xQRDC, and vectors b and x.	[ dqrsl ]
drot.l	Apply a Given’s rotation constructed by DROTG.	[ DROT ]
drotg.l	Construct a Given’s plane rotation	[ DROTG ]
drotm.l	Apply a Gentleman’s modified Given’s rotation constructed by DROTMG.	[ DROTM ]
drotmg.l	Construct a Gentleman’s modified Given’s plane rotation	[ DROTMG ]
drscl.l	multiply an n-element real vector x by the real scalar 1/a	[ drscl ]
dsbev.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ dsbev ]
dsbevd.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ dsbevd ]
dsbevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ dsbevx ]
dsbgst.l	reduce a real symmetric-definite banded generalized eigenproblem A∗x = lambda∗B∗x to standard form C∗y = lambda∗y,	[ dsbgst ]
dsbgv.l	compute all the eigenvalues, and optionally, the eigenvectors of a real generalized symmetric-definite banded eigenproblem, of the form A∗x=(lambda)∗B∗x	[ dsbgv ]
dsbmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ dsbmv ]
dsbtrd.l	reduce a real symmetric band matrix A to symmetric tridiagonal form T by an orthogonal similarity transformation	[ dsbtrd ]
dscal.l	Compute y := alpha ∗ y	[ DSCAL ]
dsdot.l	Compute the double precision dot product of two single precision vectors x and y.	[ DSDOT ]
dsecnd.l	return the user time for a process in seconds	[ dsecnd ]
dsico.l	compute the UDU factorization and condition number of a symmetric matrix A. If the condition number is not needed then xSIFA is slightly faster. It is typical to follow a call to xSICO with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ dsico ]
dsidi.l	compute the determinant, inertia, and inverse of a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA.	[ dsidi ]
dsifa.l	compute the UDU factorization of a symmetric matrix A. It is typical to follow a call to xSIFA with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ dsifa ]
dsinqb.l	synthesize a Fourier sequence from its representation in terms of a sine series with odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ dsinqb ]
dsinqf.l	compute the Fourier coefficients in a sine series representation with only odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ dsinqf ]
dsinqi.l	initialize the array xWSAVE, which is used in both xSINQF and xSINQB.	[ dsinqi ]
dsint.l	compute the discrete Fourier sine transform of an odd sequence. The xSINT transforms are unnormalized inverses of themselves, so a call of xSINT followed by another call of xSINT will multiply the input sequence by 2 ∗ (N+1). The VxSINT transforms are normalized, so a call of VxSINT followed by a call of VxSINT will return the original sequence.	[ dsint ]
dsinti.l	initialize the array xWSAVE, which is used in subroutine xSINT.	[ dsinti ]
dsisl.l	solve the linear system Ax = b for a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA, and vectors b and x.	[ dsisl ]
dspco.l	compute the UDU factorization and condition number of a symmetric matrix A in packed storage. If the condition number is not needed then xSPFA is slightly faster. It is typical to follow a call to xSPCO with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ dspco ]
dspcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric packed matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSPTRF	[ dspcon ]
dspdi.l	compute the determinant, inertia, and inverse of a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA.	[ dspdi ]
dspev.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ dspev ]
dspevd.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ dspevd ]
dspevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ dspevx ]
dspfa.l	compute the UDU factorization of a symmetric matrix A in packed storage. It is typical to follow a call to xSPFA with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ dspfa ]
dspgst.l	reduce a real symmetric-definite generalized eigenproblem to standard form, using packed storage	[ dspgst ]
dspgv.l	compute all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ dspgv ]
dspmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ dspmv ]
dspr.l	perform the symmetric rank 1 operation A := alpha∗x∗x’ + A	[ dspr ]
dspr2.l	perform the symmetric rank 2 operation A := alpha∗x∗y’ + alpha∗y∗x’ + A	[ dspr2 ]
dsprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite and packed, and provides error bounds and backward error estimates for the solution	[ dsprfs ]
dspsl.l	solve the linear system Ax = b for a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA, and vectors b and x.	[ dspsl ]
dspsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dspsv ]
dspsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B, where A is an N-by-N symmetric matrix stored in packed format and X and B are N-by-NRHS matrices	[ dspsvx ]
dsptrd.l	reduce a real symmetric matrix A stored in packed form to symmetric tridiagonal form T by an orthogonal similarity transformation	[ dsptrd ]
dsptrf.l	compute the factorization of a real symmetric matrix A stored in packed format using the Bunch-Kaufman diagonal pivoting method	[ dsptrf ]
dsptri.l	compute the inverse of a real symmetric indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSPTRF	[ dsptri ]
dsptrs.l	solve a system of linear equations A∗X = B with a real symmetric matrix A stored in packed format using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSPTRF	[ dsptrs ]
dstebz.l	compute the eigenvalues of a symmetric tridiagonal matrix T	[ dstebz ]
dstedc.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ dstedc ]
dstein.l	compute the eigenvectors of a real symmetric tridiagonal matrix T corresponding to specified eigenvalues, using inverse iteration	[ dstein ]
dsteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the implicit QL or QR method	[ dsteqr ]
dsterf.l	compute all eigenvalues of a symmetric tridiagonal matrix using the Pal-Walker-Kahan variant of the QL or QR algorithm	[ dsterf ]
dstev.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix A	[ dstev ]
dstevd.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix	[ dstevd ]
dstevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix A	[ dstevx ]
dsvdc.l	compute the singular value decomposition of a general matrix A.	[ dsvdc ]
dswap.l	Exchange vectors x and y.	[ DSWAP ]
dsycon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSYTRF	[ dsycon ]
dsyev.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ dsyev ]
dsyevd.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ dsyevd ]
dsyevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ dsyevx ]
dsygs2.l	reduce a real symmetric-definite generalized eigenproblem to standard form	[ dsygs2 ]
dsygst.l	reduce a real symmetric-definite generalized eigenproblem to standard form	[ dsygst ]
dsygv.l	compute all the eigenvalues, and optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ dsygv ]
dsymm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ dsymm ]
dsymv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ dsymv ]
dsyr.l	perform the symmetric rank 1 operation A := alpha∗x∗x’ + A	[ dsyr ]
dsyr2.l	perform the symmetric rank 2 operation A := alpha∗x∗y’ + alpha∗y∗x’ + A	[ dsyr2 ]
dsyr2k.l	perform one of the symmetric rank 2k operations C := alpha∗A∗B’ + alpha∗B∗A’ + beta∗C or C := alpha∗A’∗B + alpha∗B’∗A + beta∗C	[ dsyr2k ]
dsyrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite, and provides error bounds and backward error estimates for the solution	[ dsyrfs ]
dsyrk.l	perform one of the symmetric rank k operations C := alpha∗A∗A’ + beta∗C or C := alpha∗A’∗A + beta∗C	[ dsyrk ]
dsysv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ dsysv ]
dsysvx.l	use the diagonal pivoting factorization to compute the solution to a real system of linear equations A ∗ X = B,	[ dsysvx ]
dsytd2.l	reduce a real symmetric matrix A to symmetric tridiagonal form T by an orthogonal similarity transformation	[ dsytd2 ]
dsytf2.l	compute the factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ dsytf2 ]
dsytrd.l	reduce a real symmetric matrix A to real symmetric tridiagonal form T by an orthogonal similarity transformation	[ dsytrd ]
dsytrf.l	compute the factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ dsytrf ]
dsytri.l	compute the inverse of a real symmetric indefinite matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSYTRF	[ dsytri ]
dsytrs.l	solve a system of linear equations A∗X = B with a real symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by DSYTRF	[ dsytrs ]
dtbcon.l	estimate the reciprocal of the condition number of a triangular band matrix A, in either the 1-norm or the infinity-norm	[ dtbcon ]
dtbmv.l	perform one of the matrix-vector operations x := A∗x or x := A’∗x	[ dtbmv ]
dtbrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular band coefficient matrix	[ dtbrfs ]
dtbsv.l	solve one of the systems of equations A∗x = b or A’∗x = b	[ dtbsv ]
dtbtrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ dtbtrs ]
dtgevc.l	compute some or all of the right and/or left generalized eigenvectors of a pair of real upper triangular matrices (A,B)	[ dtgevc ]
dtgsja.l	compute the generalized singular value decomposition (GSVD) of two real upper triangular (or trapezoidal) matrices A and B	[ dtgsja ]
dtpcon.l	estimate the reciprocal of the condition number of a packed triangular matrix A, in either the 1-norm or the infinity-norm	[ dtpcon ]
dtpmv.l	perform one of the matrix-vector operations x := A∗x or x := A’∗x	[ dtpmv ]
dtprfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular packed coefficient matrix	[ dtprfs ]
dtpsv.l	solve one of the systems of equations A∗x = b or A’∗x = b	[ dtpsv ]
dtptri.l	compute the inverse of a real upper or lower triangular matrix A stored in packed format	[ dtptri ]
dtptrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ dtptrs ]
dtrco.l	estimate the condition number of a triangular matrix A. It is typical to follow a call to xTRCO with a call to xTRSL to solve Ax = b or to xTRDI to compute the determinant and inverse of A.	[ dtrco ]
dtrcon.l	estimate the reciprocal of the condition number of a triangular matrix A, in either the 1-norm or the infinity-norm	[ dtrcon ]
dtrdi.l	compute the determinant and inverse of a triangular matrix A.	[ dtrdi ]
dtrevc.l	compute some or all of the right and/or left eigenvectors of a real upper quasi-triangular matrix T	[ dtrevc ]
dtrexc.l	reorder the real Schur factorization of a real matrix A = Q∗T∗Q∗∗T, so that the diagonal block of T with row index IFST is moved to row ILST	[ dtrexc ]
dtrmm.l	perform one of the matrix-matrix operations B := alpha∗op( A )∗B, or B := alpha∗B∗op( A )	[ dtrmm ]
dtrmv.l	perform one of the matrix-vector operations x := A∗x or x := A’∗x	[ dtrmv ]
dtrrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix	[ dtrrfs ]
dtrsen.l	reorder the real Schur factorization of a real matrix A = Q∗T∗Q∗∗T, so that a selected cluster of eigenvalues appears in the leading diagonal blocks of the upper quasi-triangular matrix T,	[ dtrsen ]
dtrsl.l	solve the linear system Ax = b for a triangular matrix A and vectors b and x.	[ dtrsl ]
dtrsm.l	solve one of the matrix equations op( A )∗X = alpha∗B, or X∗op( A ) = alpha∗B	[ dtrsm ]
dtrsna.l	estimate reciprocal condition numbers for specified eigenvalues and/or right eigenvectors of a real upper quasi-triangular matrix T (or of any matrix Q∗T∗Q∗∗T with Q orthogonal)	[ dtrsna ]
dtrsv.l	solve one of the systems of equations A∗x = b or A’∗x = b	[ dtrsv ]
dtrsyl.l	solve the real Sylvester matrix equation	[ dtrsyl ]
dtrti2.l	compute the inverse of a real upper or lower triangular matrix	[ dtrti2 ]
dtrtri.l	compute the inverse of a real upper or lower triangular matrix A	[ dtrtri ]
dtrtrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ dtrtrs ]
dtzrqf.l	reduce the M-by-N ( M<=N ) real upper trapezoidal matrix A to upper triangular form by means of orthogonal transformations	[ dtzrqf ]
dzasum.l	Return the sum of the absolute values of a vector x.	[ DZASUM ]
dznrm2.l	Return the Euclidian norm of a vector.	[ DZNRM2 ]
dzsum1.l	take the sum of the absolute values of a complex vector and returns a double precision result	[ dzsum1 ]
ezfftb.l	computes a perodic sequence from its Fourier coefficients. EZFFTB is a simplified but slower version of RFFTB.	[ ezfftb ]
ezfftf.l	computes the Fourier coefficients of a perodic sequence. EZFFTF is a simplified but slower version of RFFTF.	[ ezfftf ]
ezffti.l	initializes the array WSAVE, which is used in both EZFFTF and EZFFTB.	[ ezffti ]
icamax.l	Return the index of the element with largest absolute value.	[ ICAMAX ]
icmax1.l	find the index of the element whose real part has maximum absolute value	[ icmax1 ]
idamax.l	Return the index of the element with largest absolute value.	[ IDAMAX ]
ilaenv.l	choose problem-dependent parameters	[ ilaenv ]
isamax.l	Return the index of the element with largest absolute value.	[ ISAMAX ]
izamax.l	Return the index of the element with largest absolute value.	[ IZAMAX ]
izmax1.l	find the index of the element whose real part has maximum absolute value	[ izmax1 ]
lapack.l
lsame.l	case-insensitive comparison of two characters	[ lsame ]
lsamen.l	test if the first N letters of CA are the same as the first N letters of CB, regardless of case	[ lsamen ]
rfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ rfftb ]
rfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ rfftf ]
rffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ rffti ]
sasum.l	Return the sum of the absolute values of a vector x.	[ SASUM ]
saxpy.l	Compute y := alpha ∗ x + y	[ SAXPY ]
sbdsqr.l	compute the singular value decomposition (SVD) of a real N-by-N (upper or lower) bidiagonal matrix B	[ sbdsqr ]
scasum.l	Return the sum of the absolute values of a vector x.	[ SCASUM ]
schdc.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ schdc ]
schdd.l	downdate an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ schdd ]
schex.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ schex ]
schud.l	update an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ schud ]
scnrm2.l	Return the Euclidian norm of a vector.	[ SCNRM2 ]
scopy.l	Copy x to y	[ SCOPY ]
scsum1.l	take the sum of the absolute values of a complex vector and returns a single precision result	[ scsum1 ]
sdisna.l	compute the reciprocal condition numbers for the eigenvectors of a real symmetric or complex Hermitian matrix or for the left or right singular vectors of a general m-by-n matrix	[ sdisna ]
sdot.l	Compute the dot product of two vectors x and y.	[ SDOT ]
sdsdot.l	Compute a constant plus the double precision dot product of two single precision vectors x and y.	[ SDSDOT ]
second.l	return the user time for a process in seconds	[ second ]
sgbbrd.l	reduce a real general m-by-n band matrix A to upper bidiagonal form B by an orthogonal transformation	[ sgbbrd ]
sgbco.l	compute the LU factorization and condition number of a general matrix A in banded storage. If the condition number is not needed then xGBFA is slightly faster. It is typical to follow a call to xGBCO with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ sgbco ]
sgbcon.l	estimate the reciprocal of the condition number of a real general band matrix A, in either the 1-norm or the infinity-norm,	[ sgbcon ]
sgbdi.l	compute the determinant of a general matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA.	[ sgbdi ]
sgbequ.l	compute row and column scalings intended to equilibrate an M-by-N band matrix A and reduce its condition number	[ sgbequ ]
sgbfa.l	compute the LU factorization of a matrix A in banded storage. It is typical to follow a call to xGBFA with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ sgbfa ]
sgbmv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y or y := alpha∗A’∗x + beta∗y	[ sgbmv ]
sgbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is banded, and provides error bounds and backward error estimates for the solution	[ sgbrfs ]
sgbsl.l	solve the linear system Ax = b for a matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA, and vectors b and x.	[ sgbsl ]
sgbsv.l	compute the solution to a real system of linear equations A ∗ X = B, where A is a band matrix of order N with KL subdiagonals and KU superdiagonals, and X and B are N-by-NRHS matrices	[ sgbsv ]
sgbsvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ sgbsvx ]
sgbtf2.l	compute an LU factorization of a real m-by-n band matrix A using partial pivoting with row interchanges	[ sgbtf2 ]
sgbtrf.l	compute an LU factorization of a real m-by-n band matrix A using partial pivoting with row interchanges	[ sgbtrf ]
sgbtrs.l	solve a system of linear equations A ∗ X = B or A’ ∗ X = B with a general band matrix A using the LU factorization computed by SGBTRF	[ sgbtrs ]
sgebak.l	form the right or left eigenvectors of a real general matrix by backward transformation on the computed eigenvectors of the balanced matrix output by SGEBAL	[ sgebak ]
sgebal.l	balance a general real matrix A	[ sgebal ]
sgebd2.l	reduce a real general m by n matrix A to upper or lower bidiagonal form B by an orthogonal transformation	[ sgebd2 ]
sgebrd.l	reduce a general real M-by-N matrix A to upper or lower bidiagonal form B by an orthogonal transformation	[ sgebrd ]
sgeco.l	compute the LU factorization and estimate the condition number of a general matrix A. If the condition number is not needed then xGEFA is slightly faster. It is typical to follow a call to xGECO with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant and inverse of A.	[ sgeco ]
sgecon.l	estimate the reciprocal of the condition number of a general real matrix A, in either the 1-norm or the infinity-norm, using the LU factorization computed by SGETRF	[ sgecon ]
sgedi.l	compute the determinant and inverse of a general matrix A, which has been LU-factored by xGECO or xGEFA.	[ sgedi ]
sgeequ.l	compute row and column scalings intended to equilibrate an M-by-N matrix A and reduce its condition number	[ sgeequ ]
sgees.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues, the real Schur form T, and, optionally, the matrix of Schur vectors Z	[ sgees ]
sgeesx.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues, the real Schur form T, and, optionally, the matrix of Schur vectors Z	[ sgeesx ]
sgeev.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ sgeev ]
sgeevx.l	compute for an N-by-N real nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ sgeevx ]
sgefa.l	compute the LU factorization of a general matrix A. It is typical to follow a call to xGEFA with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant of A.	[ sgefa ]
sgegs.l	compute for a pair of N-by-N real nonsymmetric matrices A, B	[ sgegs ]
sgegv.l	compute for a pair of n-by-n real nonsymmetric matrices A and B, the generalized eigenvalues (alphar +/- alphai∗i, beta), and optionally, the left and/or right generalized eigenvectors (VL and VR)	[ sgegv ]
sgehd2.l	reduce a real general matrix A to upper Hessenberg form H by an orthogonal similarity transformation	[ sgehd2 ]
sgehrd.l	reduce a real general matrix A to upper Hessenberg form H by an orthogonal similarity transformation	[ sgehrd ]
sgelq2.l	compute an LQ factorization of a real m by n matrix A	[ sgelq2 ]
sgelqf.l	compute an LQ factorization of a real M-by-N matrix A	[ sgelqf ]
sgels.l	solve overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its transpose, using a QR or LQ factorization of A	[ sgels ]
sgelss.l	compute the minimum norm solution to a real linear least squares problem	[ sgelss ]
sgelsx.l	compute the minimum-norm solution to a real linear least squares problem	[ sgelsx ]
sgemm.l	perform one of the matrix-matrix operations C := alpha∗op( A )∗op( B ) + beta∗C	[ sgemm ]
sgemv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y or y := alpha∗A’∗x + beta∗y	[ sgemv ]
sgeql2.l	compute a QL factorization of a real m by n matrix A	[ sgeql2 ]
sgeqlf.l	compute a QL factorization of a real M-by-N matrix A	[ sgeqlf ]
sgeqpf.l	compute a QR factorization with column pivoting of a real M-by-N matrix A	[ sgeqpf ]
sgeqr2.l	compute a QR factorization of a real m by n matrix A	[ sgeqr2 ]
sgeqrf.l	compute a QR factorization of a real M-by-N matrix A	[ sgeqrf ]
sger.l	perform the rank 1 operation A := alpha∗x∗y’ + A	[ sger ]
sgerfs.l	improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solution	[ sgerfs ]
sgerq2.l	compute an RQ factorization of a real m by n matrix A	[ sgerq2 ]
sgerqf.l	compute an RQ factorization of a real M-by-N matrix A	[ sgerqf ]
sgesl.l	solve the linear system Ax = b for a general matrix A, which has been LU- factored by xGECO or xGEFA, and vectors b and x.	[ sgesl ]
sgesv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ sgesv ]
sgesvd.l	compute the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and/or right singular vectors	[ sgesvd ]
sgesvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B,	[ sgesvx ]
sgetf2.l	compute an LU factorization of a general m-by-n matrix A using partial pivoting with row interchanges	[ sgetf2 ]
sgetrf.l	compute an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges	[ sgetrf ]
sgetri.l	compute the inverse of a matrix using the LU factorization computed by SGETRF	[ sgetri ]
sgetrs.l	solve a system of linear equations A ∗ X = B or A’ ∗ X = B with a general N-by-N matrix A using the LU factorization computed by SGETRF	[ sgetrs ]
sggbak.l	form the right or left eigenvectors of a real generalized eigenvalue problem A∗x = lambda∗B∗x, by backward transformation on the computed eigenvectors of the balanced pair of matrices output by SGGBAL	[ sggbak ]
sggbal.l	balance a pair of general real matrices (A,B)	[ sggbal ]
sggglm.l	solve a general Gauss-Markov linear model (GLM) problem	[ sggglm ]
sgghrd.l	reduce a pair of real matrices (A,B) to generalized upper Hessenberg form using orthogonal transformations, where A is a general matrix and B is upper triangular	[ sgghrd ]
sgglse.l	solve the linear equality-constrained least squares (LSE) problem	[ sgglse ]
sggqrf.l	compute a generalized QR factorization of an N-by-M matrix A and an N-by-P matrix B	[ sggqrf ]
sggrqf.l	compute a generalized RQ factorization of an M-by-N matrix A and a P-by-N matrix B	[ sggrqf ]
sggsvd.l	compute the generalized singular value decomposition (GSVD) of an M-by-N real matrix A and P-by-N real matrix B	[ sggsvd ]
sggsvp.l	compute orthogonal matrices U, V and Q such that N-K-L K L U’∗A∗Q = K ( 0 A12 A13 ) if M-K-L >= 0	[ sggsvp ]
sgtcon.l	estimate the reciprocal of the condition number of a real tridiagonal matrix A using the LU factorization as computed by SGTTRF	[ sgtcon ]
sgtrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is tridiagonal, and provides error bounds and backward error estimates for the solution	[ sgtrfs ]
sgtsl.l	solve the linear system Ax = b for a tridiagonal matrix A and vectors b and x.	[ sgtsl ]
sgtsv.l	solve the equation A∗X = B,	[ sgtsv ]
sgtsvx.l	use the LU factorization to compute the solution to a real system of linear equations A ∗ X = B or A∗∗T ∗ X = B,	[ sgtsvx ]
sgttrf.l	compute an LU factorization of a real tridiagonal matrix A using elimination with partial pivoting and row interchanges	[ sgttrf ]
sgttrs.l	solve one of the systems of equations A∗X = B or A’∗X = B,	[ sgttrs ]
shgeqz.l	implement a single-/double-shift version of the QZ method for finding the generalized eigenvalues w(j)=(ALPHAR(j) + i∗ALPHAI(j))/BETAR(j) of the equation det( A - w(i) B ) = 0 In addition, the pair A,B may be reduced to generalized Schur form	[ shgeqz ]
shsein.l	use inverse iteration to find specified right and/or left eigenvectors of a real upper Hessenberg matrix H	[ shsein ]
shseqr.l	compute the eigenvalues of a real upper Hessenberg matrix H and, optionally, the matrices T and Z from the Schur decomposition H = Z T Z∗∗T, where T is an upper quasi-triangular matrix (the Schur form), and Z is the orthogonal matrix of Schur vectors	[ shseqr ]
sinqb.l	synthesize a Fourier sequence from its representation in terms of a sine series with odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ sinqb ]
sinqf.l	compute the Fourier coefficients in a sine series representation with only odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ sinqf ]
sinqi.l	initialize the array xWSAVE, which is used in both xSINQF and xSINQB.	[ sinqi ]
sint.l	compute the discrete Fourier sine transform of an odd sequence. The xSINT transforms are unnormalized inverses of themselves, so a call of xSINT followed by another call of xSINT will multiply the input sequence by 2 ∗ (N+1). The VxSINT transforms are normalized, so a call of VxSINT followed by a call of VxSINT will return the original sequence.	[ sint ]
sinti.l	initialize the array xWSAVE, which is used in subroutine xSINT.	[ sinti ]
slabad.l	take as input the values computed by SLAMCH for underflow and overflow, and returns the square root of each of these values if the log of LARGE is sufficiently large	[ slabad ]
slabrd.l	reduce the first NB rows and columns of a real general m by n matrix A to upper or lower bidiagonal form by an orthogonal transformation Q’ ∗ A ∗ P, and returns the matrices X and Y which are needed to apply the transformation to the unreduced part of A	[ slabrd ]
slacon.l	estimate the 1-norm of a square, real matrix A	[ slacon ]
slacpy.l	copie all or part of a two-dimensional matrix A to another matrix B	[ slacpy ]
sladiv.l	perform complex division in real arithmetic a + i∗b p + i∗q = --------- c + i∗d The algorithm is due to Robert L	[ sladiv ]
slae2.l	compute the eigenvalues of a 2-by-2 symmetric matrix [ A B ] [ B C ]	[ slae2 ]
slaebz.l	contain the iteration loops which compute and use the function N(w), which is the count of eigenvalues of a symmetric tridiagonal matrix T less than or equal to its argument w	[ slaebz ]
slaed0.l	compute all eigenvalues and corresponding eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ slaed0 ]
slaed1.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ slaed1 ]
slaed2.l	merge the two sets of eigenvalues together into a single sorted set	[ slaed2 ]
slaed3.l	find the roots of the secular equation, as defined by the values in D, W, and RHO, between KSTART and KSTOP	[ slaed3 ]
slaed4.l	subroutine computes the I-th updated eigenvalue of a symmetric rank-one modification to a diagonal matrix whose elements are given in the array d, and that D(i) < D(j) for i < j and that RHO > 0	[ slaed4 ]
slaed5.l	subroutine computes the I-th eigenvalue of a symmetric rank-one modification of a 2-by-2 diagonal matrix diag( D ) + RHO The diagonal elements in the array D are assumed to satisfy D(i) < D(j) for i < j	[ slaed5 ]
slaed6.l	compute the positive or negative root (closest to the origin) of z(1) z(2) z(3) f(x) = rho + --------- + ---------- + --------- d(1)-x d(2)-x d(3)-x It is assumed that if ORGATI = .true	[ slaed6 ]
slaed7.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ slaed7 ]
slaed8.l	merge the two sets of eigenvalues together into a single sorted set	[ slaed8 ]
slaed9.l	find the roots of the secular equation, as defined by the values in D, Z, and RHO, between KSTART and KSTOP	[ slaed9 ]
slaeda.l	compute the Z vector corresponding to the merge step in the CURLVLth step of the merge process with TLVLS steps for the CURPBMth problem	[ slaeda ]
slaein.l	use inverse iteration to find a right or left eigenvector corresponding to the eigenvalue (WR,WI) of a real upper Hessenberg matrix H	[ slaein ]
slaev2.l	compute the eigendecomposition of a 2-by-2 symmetric matrix [ A B ] [ B C ]	[ slaev2 ]
slaexc.l	swap adjacent diagonal blocks T11 and T22 of order 1 or 2 in an upper quasi-triangular matrix T by an orthogonal similarity transformation	[ slaexc ]
slag2.l	compute the eigenvalues of a 2 x 2 generalized eigenvalue problem A - w B, with scaling as necessary to avoid over-/underflow	[ slag2 ]
slags2.l	compute 2-by-2 orthogonal matrices U, V and Q, such that if ( UPPER ) then U’∗A∗Q = U’∗( A1 A2 )∗Q = ( x 0 ) ( 0 A3 ) ( x x ) and V’∗B∗Q = V’∗( B1 B2 )∗Q = ( x 0 ) ( 0 B3 ) ( x x ) or if ( .NOT.UPPER ) then U’∗A∗Q = U’∗( A1 0 )∗Q = ( x x ) ( A2 A3 ) ( 0 x ) and V’∗B∗Q = V’∗( B1 0 )∗Q = ( x x ) ( B2 B3 ) ( 0 x ) The rows of the transformed A and B are parallel, where U = ( CSU SNU ), V = ( CSV SNV ), Q = ( CSQ SNQ ) ( -SNU CSU ) ( -SNV CSV ) ( -SNQ CSQ ) Z’ denotes the transpose of Z	[ slags2 ]
slagtf.l	factorize the matrix (T - lambda∗I), where T is an n by n tridiagonal matrix and lambda is a scalar, as T - lambda∗I = PLU,	[ slagtf ]
slagtm.l	perform a matrix-vector product of the form B := alpha ∗ A ∗ X + beta ∗ B where A is a tridiagonal matrix of order N, B and X are N by NRHS matrices, and alpha and beta are real scalars, each of which may be 0., 1., or -1	[ slagtm ]
slagts.l	may be used to solve one of the systems of equations (T - lambda∗I)∗x = y or (T - lambda∗I)’∗x = y,	[ slagts ]
slahqr.l	i an auxiliary routine called by SHSEQR to update the eigenvalues and Schur decomposition already computed by SHSEQR, by dealing with the Hessenberg submatrix in rows and columns ILO to IHI	[ slahqr ]
slahrd.l	reduce the first NB columns of a real general n-by-(n-k+1) matrix A so that elements below the k-th subdiagonal are zero	[ slahrd ]
slaic1.l	applie one step of incremental condition estimation in its simplest version	[ slaic1 ]
slaln2.l	solve a system of the form (ca A - w D ) X = s B or (ca A’ - w D) X = s B with possible scaling ("s") and perturbation of A	[ slaln2 ]
slamch.l	determine single precision machine parameters	[ slamch ]
slamrg.l	will create a permutation list which will merge the elements of A (which is composed of two independently sorted sets) into a single set which is sorted in ascending order	[ slamrg ]
slangb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n band matrix A, with kl sub-diagonals and ku super-diagonals	[ slangb ]
slange.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real matrix A	[ slange ]
slangt.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real tridiagonal matrix A	[ slangt ]
slanhs.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a Hessenberg matrix A	[ slanhs ]
slansb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n symmetric band matrix A, with k super-diagonals	[ slansb ]
slansp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric matrix A, supplied in packed form	[ slansp ]
slanst.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric tridiagonal matrix A	[ slanst ]
slansy.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real symmetric matrix A	[ slansy ]
slantb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n triangular band matrix A, with ( k + 1 ) diagonals	[ slantb ]
slantp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a triangular matrix A, supplied in packed form	[ slantp ]
slantr.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix A	[ slantr ]
slanv2.l	compute the Schur factorization of a real 2-by-2 nonsymmetric matrix in standard form	[ slanv2 ]
slapll.l	two column vectors X and Y, let A = ( X Y )	[ slapll ]
slapmt.l	rearrange the columns of the M by N matrix X as specified by the permutation K(1),K(2),...,K(N) of the integers 1,...,N	[ slapmt ]
slapy2.l	return sqrt(x∗∗2+y∗∗2), taking care not to cause unnecessary overflow	[ slapy2 ]
slapy3.l	return sqrt(x∗∗2+y∗∗2+z∗∗2), taking care not to cause unnecessary overflow	[ slapy3 ]
slaqgb.l	equilibrate a general M by N band matrix A with KL subdiagonals and KU superdiagonals using the row and scaling factors in the vectors R and C	[ slaqgb ]
slaqge.l	equilibrate a general M by N matrix A using the row and scaling factors in the vectors R and C	[ slaqge ]
slaqsb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ slaqsb ]
slaqsp.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ slaqsp ]
slaqsy.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ slaqsy ]
slaqtr.l	solve the real quasi-triangular system op(T)∗p = scale∗c, if LREAL = .TRUE	[ slaqtr ]
slar2v.l	applie a vector of real plane rotations from both sides to a sequence of 2-by-2 real symmetric matrices, defined by the elements of the vectors x, y and z	[ slar2v ]
slarf.l	applie a real elementary reflector H to a real m by n matrix C, from either the left or the right	[ slarf ]
slarfb.l	applie a real block reflector H or its transpose H’ to a real m by n matrix C, from either the left or the right	[ slarfb ]
slarfg.l	generate a real elementary reflector H of order n, such that H ∗ ( alpha ) = ( beta ), H’ ∗ H = I	[ slarfg ]
slarft.l	form the triangular factor T of a real block reflector H of order n, which is defined as a product of k elementary reflectors	[ slarft ]
slarfx.l	applie a real elementary reflector H to a real m by n matrix C, from either the left or the right	[ slarfx ]
slargv.l	generate a vector of real plane rotations, determined by elements of the real vectors x and y	[ slargv ]
slarnv.l	return a vector of n random real numbers from a uniform or normal distribution	[ slarnv ]
slartg.l	generate a plane rotation so that [ CS SN ]	[ slartg ]
slartv.l	applie a vector of real plane rotations to elements of the real vectors x and y	[ slartv ]
slaruv.l	return a vector of n random real numbers from a uniform (0,1)	[ slaruv ]
slas2.l	compute the singular values of the 2-by-2 matrix [ F G ] [ 0 H ]	[ slas2 ]
slascl.l	multiply the M by N real matrix A by the real scalar CTO/CFROM	[ slascl ]
slaset.l	initialize an m-by-n matrix A to BETA on the diagonal and ALPHA on the offdiagonals	[ slaset ]
slasq1.l	SLASQ1 computes the singular values of a real N-by-N bidiagonal matrix with diagonal D and off-diagonal E	[ slasq1 ]
slasq2.l	SLASQ2 computes the singular values of a real N-by-N unreduced bidiagonal matrix with squared diagonal elements in Q and squared off-diagonal elements in E	[ slasq2 ]
slasq3.l	SLASQ3 is the workhorse of the whole bidiagonal SVD algorithm	[ slasq3 ]
slasq4.l	SLASQ4 estimates TAU, the smallest eigenvalue of a matrix	[ slasq4 ]
slasr.l	perform the transformation A := P∗A, when SIDE = ’L’ or ’l’ ( Left-hand side ) A := A∗P’, when SIDE = ’R’ or ’r’ ( Right-hand side ) where A is an m by n real matrix and P is an orthogonal matrix,	[ slasr ]
slasrt.l	the numbers in D in increasing order (if ID = ’I’) or in decreasing order (if ID = ’D’ )	[ slasrt ]
slassq.l	return the values scl and smsq such that ( scl∗∗2 )∗smsq = x( 1 )∗∗2 +...+ x( n )∗∗2 + ( scale∗∗2 )∗sumsq,	[ slassq ]
slasv2.l	compute the singular value decomposition of a 2-by-2 triangular matrix [ F G ] [ 0 H ]	[ slasv2 ]
slaswp.l	perform a series of row interchanges on the matrix A	[ slaswp ]
slasy2.l	solve for the N1 by N2 matrix X, 1 <= N1,N2 <= 2, in op(TL)∗X + ISGN∗X∗op(TR) = SCALE∗B,	[ slasy2 ]
slasyf.l	compute a partial factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ slasyf ]
slatbs.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow, where A is an upper or lower triangular band matrix	[ slatbs ]
slatps.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow, where A is an upper or lower triangular matrix stored in packed form	[ slatps ]
slatrd.l	reduce NB rows and columns of a real symmetric matrix A to symmetric tridiagonal form by an orthogonal similarity transformation Q’ ∗ A ∗ Q, and returns the matrices V and W which are needed to apply the transformation to the unreduced part of A	[ slatrd ]
slatrs.l	solve one of the triangular systems A ∗x = s∗b or A’∗x = s∗b with scaling to prevent overflow	[ slatrs ]
slatzm.l	applie a Householder matrix generated by STZRQF to a matrix	[ slatzm ]
slauu2.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ slauu2 ]
slauum.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ slauum ]
snrm2.l	Return the Euclidian norm of a vector.	[ SNRM2 ]
sopgtr.l	generate a real orthogonal matrix Q which is defined as the product of n-1 elementary reflectors H(i) of order n, as returned by SSPTRD using packed storage	[ sopgtr ]
sopmtr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sopmtr ]
sorg2l.l	generate an m by n real matrix Q with orthonormal columns,	[ sorg2l ]
sorg2r.l	generate an m by n real matrix Q with orthonormal columns,	[ sorg2r ]
sorgbr.l	generate one of the real orthogonal matrices Q or P∗∗T determined by SGEBRD when reducing a real matrix A to bidiagonal form	[ sorgbr ]
sorghr.l	generate a real orthogonal matrix Q which is defined as the product of IHI-ILO elementary reflectors of order N, as returned by SGEHRD	[ sorghr ]
sorgl2.l	generate an m by n real matrix Q with orthonormal rows,	[ sorgl2 ]
sorglq.l	generate an M-by-N real matrix Q with orthonormal rows,	[ sorglq ]
sorgql.l	generate an M-by-N real matrix Q with orthonormal columns,	[ sorgql ]
sorgqr.l	generate an M-by-N real matrix Q with orthonormal columns,	[ sorgqr ]
sorgr2.l	generate an m by n real matrix Q with orthonormal rows,	[ sorgr2 ]
sorgrq.l	generate an M-by-N real matrix Q with orthonormal rows,	[ sorgrq ]
sorgtr.l	generate a real orthogonal matrix Q which is defined as the product of n-1 elementary reflectors of order N, as returned by SSYTRD	[ sorgtr ]
sorm2l.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ sorm2l ]
sorm2r.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ sorm2r ]
sormbr.l	VECT = ’Q’, SORMBR overwrites the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormbr ]
sormhr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormhr ]
sorml2.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ sorml2 ]
sormlq.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormlq ]
sormql.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormql ]
sormqr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormqr ]
sormr2.l	overwrite the general real m by n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’T’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’T’,	[ sormr2 ]
sormrq.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormrq ]
sormtr.l	overwrite the general real M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ sormtr ]
spbco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in banded storage. If the condition number is not needed then xPBFA is slightly faster. It is typical to follow a call to xPBCO with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ spbco ]
spbcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite band matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPBTRF	[ spbcon ]
spbdi.l	compute the determinant of a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA.	[ spbdi ]
spbequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite band matrix A and reduce its condition number (with respect to the two-norm)	[ spbequ ]
spbfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in banded storage. It is typical to follow a call to xPBFA with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ spbfa ]
spbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and banded, and provides error bounds and backward error estimates for the solution	[ spbrfs ]
spbsl.l	section solve the linear system Ax = b for a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA, and vectors b and x.	[ spbsl ]
spbstf.l	compute a split Cholesky factorization of a real symmetric positive definite band matrix A	[ spbstf ]
spbsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ spbsv ]
spbsvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ spbsvx ]
spbtf2.l	compute the Cholesky factorization of a real symmetric positive definite band matrix A	[ spbtf2 ]
spbtrf.l	compute the Cholesky factorization of a real symmetric positive definite band matrix A	[ spbtrf ]
spbtrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite band matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPBTRF	[ spbtrs ]
spoco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A. If the condition number is not needed then xPOFA is slightly faster. It is typical to follow a call to xPOCO with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ spoco ]
spocon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPOTRF	[ spocon ]
spodi.l	compute the determinant and inverse of a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO, xPOFA, or xQRDC.	[ spodi ]
spoequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite matrix A and reduce its condition number (with respect to the two-norm)	[ spoequ ]
spofa.l	compute a Cholesky factorization of a symmetric positive definite matrix A. It is typical to follow a call to xPOFA with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ spofa ]
sporfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite,	[ sporfs ]
sposl.l	solve the linear system Ax = b for a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO or xPOFA, and vectors b and x.	[ sposl ]
sposv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ sposv ]
sposvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ sposvx ]
spotf2.l	compute the Cholesky factorization of a real symmetric positive definite matrix A	[ spotf2 ]
spotrf.l	compute the Cholesky factorization of a real symmetric positive definite matrix A	[ spotrf ]
spotri.l	compute the inverse of a real symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPOTRF	[ spotri ]
spotrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPOTRF	[ spotrs ]
sppco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in packed storage. If the condition number is not needed then xPPFA is slightly faster. It is typical to follow a call to xPPCO with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ sppco ]
sppcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite packed matrix using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPPTRF	[ sppcon ]
sppdi.l	compute the determinant and inverse of a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA.	[ sppdi ]
sppequ.l	compute row and column scalings intended to equilibrate a symmetric positive definite matrix A in packed storage and reduce its condition number (with respect to the two-norm)	[ sppequ ]
sppfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in packed storage. It is typical to follow a call to xPPFA with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ sppfa ]
spprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and packed, and provides error bounds and backward error estimates for the solution	[ spprfs ]
sppsl.l	solve the linear system Ax = b for a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA, and vectors b and x.	[ sppsl ]
sppsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ sppsv ]
sppsvx.l	use the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B,	[ sppsvx ]
spptrf.l	compute the Cholesky factorization of a real symmetric positive definite matrix A stored in packed format	[ spptrf ]
spptri.l	compute the inverse of a real symmetric positive definite matrix A using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPPTRF	[ spptri ]
spptrs.l	solve a system of linear equations A∗X = B with a symmetric positive definite matrix A in packed storage using the Cholesky factorization A = U∗∗T∗U or A = L∗L∗∗T computed by SPPTRF	[ spptrs ]
sptcon.l	compute the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite tridiagonal matrix using the factorization A = L∗D∗L∗∗T or A = U∗∗T∗D∗U computed by SPTTRF	[ sptcon ]
spteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric positive definite tridiagonal matrix by first factoring the matrix using SPTTRF, and then calling SBDSQR to compute the singular values of the bidiagonal factor	[ spteqr ]
sptrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and tridiagonal, and provides error bounds and backward error estimates for the solution	[ sptrfs ]
sptsl.l	solve the linear system Ax = b for a symmetric positive definite tridiagonal matrix A and vectors b and x.	[ sptsl ]
sptsv.l	compute the solution to a real system of linear equations A∗X = B, where A is an N-by-N symmetric positive definite tridiagonal matrix, and X and B are N-by-NRHS matrices	[ sptsv ]
sptsvx.l	use the factorization A = L∗D∗L∗∗T to compute the solution to a real system of linear equations A∗X = B, where A is an N-by-N symmetric positive definite tridiagonal matrix and X and B are N-by-NRHS matrices	[ sptsvx ]
spttrf.l	compute the factorization of a real symmetric positive definite tridiagonal matrix A	[ spttrf ]
spttrs.l	solve a system of linear equations A ∗ X = B with a symmetric positive definite tridiagonal matrix A using the factorization A = L∗D∗L∗∗T or A = U∗∗T∗D∗U computed by SPTTRF	[ spttrs ]
sqrdc.l	compute the QR factorization of a general matrix A. It is typical to follow a call to xQRDC with a call to xQRSL to solve Ax = b or to xPODI to compute the determinant of A.	[ sqrdc ]
sqrsl.l	solve the linear system Ax = b for a general matrix A, which has been QR- factored by xQRDC, and vectors b and x.	[ sqrsl ]
srot.l	Apply a Given’s rotation constructed by SROTG.	[ SROT ]
srotg.l	Construct a Given’s plane rotation	[ SROTG ]
srotm.l	Apply a Gentleman’s modified Given’s rotation constructed by SROTMG.	[ SROTM ]
srotmg.l	Construct a Gentleman’s modified Given’s plane rotation	[ SROTMG ]
srscl.l	multiply an n-element real vector x by the real scalar 1/a	[ srscl ]
ssbev.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ ssbev ]
ssbevd.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ ssbevd ]
ssbevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric band matrix A	[ ssbevx ]
ssbgst.l	reduce a real symmetric-definite banded generalized eigenproblem A∗x = lambda∗B∗x to standard form C∗y = lambda∗y,	[ ssbgst ]
ssbgv.l	compute all the eigenvalues, and optionally, the eigenvectors of a real generalized symmetric-definite banded eigenproblem, of the form A∗x=(lambda)∗B∗x	[ ssbgv ]
ssbmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ ssbmv ]
ssbtrd.l	reduce a real symmetric band matrix A to symmetric tridiagonal form T by an orthogonal similarity transformation	[ ssbtrd ]
sscal.l	Compute y := alpha ∗ y	[ SSCAL ]
ssico.l	compute the UDU factorization and condition number of a symmetric matrix A. If the condition number is not needed then xSIFA is slightly faster. It is typical to follow a call to xSICO with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ ssico ]
ssidi.l	compute the determinant, inertia, and inverse of a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA.	[ ssidi ]
ssifa.l	compute the UDU factorization of a symmetric matrix A. It is typical to follow a call to xSIFA with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ ssifa ]
ssisl.l	solve the linear system Ax = b for a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA, and vectors b and x.	[ ssisl ]
sspco.l	compute the UDU factorization and condition number of a symmetric matrix A in packed storage. If the condition number is not needed then xSPFA is slightly faster. It is typical to follow a call to xSPCO with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ sspco ]
sspcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric packed matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSPTRF	[ sspcon ]
sspdi.l	compute the determinant, inertia, and inverse of a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA.	[ sspdi ]
sspev.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ sspev ]
sspevd.l	compute all the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ sspevd ]
sspevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A in packed storage	[ sspevx ]
sspfa.l	compute the UDU factorization of a symmetric matrix A in packed storage. It is typical to follow a call to xSPFA with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ sspfa ]
sspgst.l	reduce a real symmetric-definite generalized eigenproblem to standard form, using packed storage	[ sspgst ]
sspgv.l	compute all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ sspgv ]
sspmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ sspmv ]
sspr.l	perform the symmetric rank 1 operation A := alpha∗x∗x’ + A	[ sspr ]
sspr2.l	perform the symmetric rank 2 operation A := alpha∗x∗y’ + alpha∗y∗x’ + A	[ sspr2 ]
ssprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite and packed, and provides error bounds and backward error estimates for the solution	[ ssprfs ]
sspsl.l	solve the linear system Ax = b for a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA, and vectors b and x.	[ sspsl ]
sspsv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ sspsv ]
sspsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T to compute the solution to a real system of linear equations A ∗ X = B, where A is an N-by-N symmetric matrix stored in packed format and X and B are N-by-NRHS matrices	[ sspsvx ]
ssptrd.l	reduce a real symmetric matrix A stored in packed form to symmetric tridiagonal form T by an orthogonal similarity transformation	[ ssptrd ]
ssptrf.l	compute the factorization of a real symmetric matrix A stored in packed format using the Bunch-Kaufman diagonal pivoting method	[ ssptrf ]
ssptri.l	compute the inverse of a real symmetric indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSPTRF	[ ssptri ]
ssptrs.l	solve a system of linear equations A∗X = B with a real symmetric matrix A stored in packed format using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSPTRF	[ ssptrs ]
sstebz.l	compute the eigenvalues of a symmetric tridiagonal matrix T	[ sstebz ]
sstedc.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ sstedc ]
sstein.l	compute the eigenvectors of a real symmetric tridiagonal matrix T corresponding to specified eigenvalues, using inverse iteration	[ sstein ]
ssteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the implicit QL or QR method	[ ssteqr ]
ssterf.l	compute all eigenvalues of a symmetric tridiagonal matrix using the Pal-Walker-Kahan variant of the QL or QR algorithm	[ ssterf ]
sstev.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix A	[ sstev ]
sstevd.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix	[ sstevd ]
sstevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix A	[ sstevx ]
ssvdc.l	compute the singular value decomposition of a general matrix A.	[ ssvdc ]
sswap.l	Exchange vectors x and y.	[ SSWAP ]
ssycon.l	estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSYTRF	[ ssycon ]
ssyev.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ ssyev ]
ssyevd.l	compute all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ ssyevd ]
ssyevx.l	compute selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A	[ ssyevx ]
ssygs2.l	reduce a real symmetric-definite generalized eigenproblem to standard form	[ ssygs2 ]
ssygst.l	reduce a real symmetric-definite generalized eigenproblem to standard form	[ ssygst ]
ssygv.l	compute all the eigenvalues, and optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ ssygv ]
ssymm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ ssymm ]
ssymv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ ssymv ]
ssyr.l	perform the symmetric rank 1 operation A := alpha∗x∗x’ + A	[ ssyr ]
ssyr2.l	perform the symmetric rank 2 operation A := alpha∗x∗y’ + alpha∗y∗x’ + A	[ ssyr2 ]
ssyr2k.l	perform one of the symmetric rank 2k operations C := alpha∗A∗B’ + alpha∗B∗A’ + beta∗C or C := alpha∗A’∗B + alpha∗B’∗A + beta∗C	[ ssyr2k ]
ssyrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite, and provides error bounds and backward error estimates for the solution	[ ssyrfs ]
ssyrk.l	perform one of the symmetric rank k operations C := alpha∗A∗A’ + beta∗C or C := alpha∗A’∗A + beta∗C	[ ssyrk ]
ssysv.l	compute the solution to a real system of linear equations A ∗ X = B,	[ ssysv ]
ssysvx.l	use the diagonal pivoting factorization to compute the solution to a real system of linear equations A ∗ X = B,	[ ssysvx ]
ssytd2.l	reduce a real symmetric matrix A to symmetric tridiagonal form T by an orthogonal similarity transformation	[ ssytd2 ]
ssytf2.l	compute the factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ ssytf2 ]
ssytrd.l	reduce a real symmetric matrix A to real symmetric tridiagonal form T by an orthogonal similarity transformation	[ ssytrd ]
ssytrf.l	compute the factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ ssytrf ]
ssytri.l	compute the inverse of a real symmetric indefinite matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSYTRF	[ ssytri ]
ssytrs.l	solve a system of linear equations A∗X = B with a real symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by SSYTRF	[ ssytrs ]
stbcon.l	estimate the reciprocal of the condition number of a triangular band matrix A, in either the 1-norm or the infinity-norm	[ stbcon ]
stbmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x	[ stbmv ]
stbrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular band coefficient matrix	[ stbrfs ]
stbsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b	[ stbsv ]
stbtrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ stbtrs ]
stgevc.l	compute some or all of the right and/or left generalized eigenvectors of a pair of real upper triangular matrices (A,B)	[ stgevc ]
stgsja.l	compute the generalized singular value decomposition (GSVD) of two real upper triangular (or trapezoidal) matrices A and B	[ stgsja ]
stpcon.l	estimate the reciprocal of the condition number of a packed triangular matrix A, in either the 1-norm or the infinity-norm	[ stpcon ]
stpmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x	[ stpmv ]
stprfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular packed coefficient matrix	[ stprfs ]
stpsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b	[ stpsv ]
stptri.l	compute the inverse of a real upper or lower triangular matrix A stored in packed format	[ stptri ]
stptrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ stptrs ]
strco.l	estimate the condition number of a triangular matrix A. It is typical to follow a call to xTRCO with a call to xTRSL to solve Ax = b or to xTRDI to compute the determinant and inverse of A.	[ strco ]
strcon.l	estimate the reciprocal of the condition number of a triangular matrix A, in either the 1-norm or the infinity-norm	[ strcon ]
strdi.l	compute the determinant and inverse of a triangular matrix A.	[ strdi ]
strevc.l	compute some or all of the right and/or left eigenvectors of a real upper quasi-triangular matrix T	[ strevc ]
strexc.l	reorder the real Schur factorization of a real matrix A = Q∗T∗Q∗∗T, so that the diagonal block of T with row index IFST is moved to row ILST	[ strexc ]
strmm.l	perform one of the matrix-matrix operations B := alpha∗op( A )∗B, or B := alpha∗B∗op( A )	[ strmm ]
strmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x	[ strmv ]
strrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix	[ strrfs ]
strsen.l	reorder the real Schur factorization of a real matrix A = Q∗T∗Q∗∗T, so that a selected cluster of eigenvalues appears in the leading diagonal blocks of the upper quasi-triangular matrix T,	[ strsen ]
strsl.l	solve the linear system Ax = b for a triangular matrix A and vectors b and x.	[ strsl ]
strsm.l	solve one of the matrix equations op( A )∗X = alpha∗B, or X∗op( A ) = alpha∗B	[ strsm ]
strsna.l	estimate reciprocal condition numbers for specified eigenvalues and/or right eigenvectors of a real upper quasi-triangular matrix T (or of any matrix Q∗T∗Q∗∗T with Q orthogonal)	[ strsna ]
strsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b	[ strsv ]
strsyl.l	solve the real Sylvester matrix equation	[ strsyl ]
strti2.l	compute the inverse of a real upper or lower triangular matrix	[ strti2 ]
strtri.l	compute the inverse of a real upper or lower triangular matrix A	[ strtri ]
strtrs.l	solve a triangular system of the form A ∗ X = B or A∗∗T ∗ X = B,	[ strtrs ]
stzrqf.l	reduce the M-by-N ( M<=N ) real upper trapezoidal matrix A to upper triangular form by means of orthogonal transformations	[ stzrqf ]
vcosqb.l	synthesize a Fourier sequence from its representation in terms of a cosine series with odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ vcosqb ]
vcosqf.l	compute the Fourier coefficients in a cosine series representation with only odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ vcosqf ]
vcosqi.l	initialize the array xWSAVE, which is used in both xCOSQF and xCOSQB.	[ vcosqi ]
vcost.l	compute the discrete Fourier cosine transform of an even sequence. The xCOST transforms are unnormalized inverses of themselves, so a call of xCOST followed by another call of xCOST will multiply the input sequence by 2 ∗ (N - 1). The VxCOST transforms are normalized, so a call of VxCOST followed by a call of VxCOST will return the original sequence.	[ vcost ]
vcosti.l	initialize the array xWSAVE, which is used in xCOST.	[ vcosti ]
vdcosqb.l	synthesize a Fourier sequence from its representation in terms of a cosine series with odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ vdcosqb ]
vdcosqf.l	compute the Fourier coefficients in a cosine series representation with only odd wave numbers. The xCOSQ operations are unnormalized inverses of themselves, so a call to xCOSQF followed by a call to xCOSQB will multiply the input sequence by 4 ∗ N. The VxCOSQ operations are normalized, so a call of VxCOSQF followed by a call of VxCOSQB will return the original sequence.	[ vdcosqf ]
vdcosqi.l	initialize the array xWSAVE, which is used in both xCOSQF and xCOSQB.	[ vdcosqi ]
vdcost.l	compute the discrete Fourier cosine transform of an even sequence. The xCOST transforms are unnormalized inverses of themselves, so a call of xCOST followed by another call of xCOST will multiply the input sequence by 2 ∗ (N - 1). The VxCOST transforms are normalized, so a call of VxCOST followed by a call of VxCOST will return the original sequence.	[ vdcost ]
vdcosti.l	initialize the array xWSAVE, which is used in xCOST.	[ vdcosti ]
vdfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ vdfftb ]
vdfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ vdfftf ]
vdffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ vdffti ]
vdsinqb.l	synthesize a Fourier sequence from its representation in terms of a sine series with odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ vdsinqb ]
vdsinqf.l	compute the Fourier coefficients in a sine series representation with only odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ vdsinqf ]
vdsinqi.l	initialize the array xWSAVE, which is used in both xSINQF and xSINQB.	[ vdsinqi ]
vdsint.l	initialize the array xWSAVE, which is used in subroutine xSINT.	[ vdsinti ]
vdsinti.l	initialize the array xWSAVE, which is used in subroutine xSINT.	[ vdsinti ]
vrfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ vrfftb ]
vrfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ vrfftf ]
vrffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ vrffti ]
vsinqb.l	synthesize a Fourier sequence from its representation in terms of a sine series with odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ vsinqb ]
vsinqf.l	compute the Fourier coefficients in a sine series representation with only odd wave numbers. The xSINQ operations are unnormalized inverses of themselves, so a call to xSINQF followed by a call to xSINQB will multiply the input sequence by 4 ∗ N. The VxSINQ operations are normalized, so a call of VxSINQF followed by a call of VxSINQB will return the original sequence.	[ vsinqf ]
vsinqi.l	initialize the array xWSAVE, which is used in both xSINQF and xSINQB.	[ vsinqi ]
vsint.l	compute the discrete Fourier sine transform of an odd sequence. The xSINT transforms are unnormalized inverses of themselves, so a call of xSINT followed by another call of xSINT will multiply the input sequence by 2 ∗ (N+1). The VxSINT transforms are normalized, so a call of VxSINT followed by a call of VxSINT will return the original sequence.	[ vsint ]
vsinti.l	initialize the array xWSAVE, which is used in subroutine xSINT.	[ vsinti ]
xerbla.l	error handler for the LAPACK routines	[ xerbla ]
zaxpy.l	Compute y := alpha ∗ x + y	[ ZAXPY ]
zbdsqr.l	compute the singular value decomposition (SVD) of a real N-by-N (upper or lower) bidiagonal matrix B	[ zbdsqr ]
zchdc.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ zchdc ]
zchdd.l	downdate an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ zchdd ]
zchex.l	compute the Cholesky decomposition of a symmetric positive definite matrix A.	[ zchex ]
zchud.l	update an augmented Cholesky decomposition of the triangular part of an augmented QR decomposition.	[ zchud ]
zcopy.l	Copy x to y	[ ZCOPY ]
zdotc.l	Compute the dot product of two vectors x and conjg(y).	[ ZDOTU ]
zdotu.l	Compute the dot product of two vectors x and y.	[ ZDOTU ]
zdrscl.l	multiply an n-element complex vector x by the real scalar 1/a	[ zdrscl ]
zdscal.l	Compute y := alpha ∗ y	[ zdscal ]
zfftb.l	compute a perodic sequence from its Fourier coefficients. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ zfftb ]
zfftf.l	compute the Fourier coefficients of a perodic sequence. The xFFT operations are unnormalized, so a call of xFFTF followed by a call of xFFTB will multiply the input sequence by N. The VxFFT operations are normalized, so a call of VxFFTF followed by a call of VxFFTB will return the original sequence.	[ zfftf ]
zffti.l	initialize the array xWSAVE, which is used in both xFFTF and xFFTB.	[ zffti ]
zgbbrd.l	reduce a complex general m-by-n band matrix A to real upper bidiagonal form B by a unitary transformation	[ zgbbrd ]
zgbco.l	compute the LU factorization and condition number of a general matrix A in banded storage. If the condition number is not needed then xGBFA is slightly faster. It is typical to follow a call to xGBCO with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ zgbco ]
zgbcon.l	estimate the reciprocal of the condition number of a complex general band matrix A, in either the 1-norm or the infinity-norm,	[ zgbcon ]
zgbdi.l	compute the determinant of a general matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA.	[ zgbdi ]
zgbequ.l	compute row and column scalings intended to equilibrate an M-by-N band matrix A and reduce its condition number	[ zgbequ ]
zgbfa.l	compute the LU factorization of a matrix A in banded storage. It is typical to follow a call to xGBFA with a call to xGBSL to solve Ax = b or to xGBDI to compute the determinant of A.	[ zgbfa ]
zgbmv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y, or y := alpha∗A’∗x + beta∗y, or y := alpha∗conjg( A’ )∗x + beta∗y	[ zgbmv ]
zgbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is banded, and provides error bounds and backward error estimates for the solution	[ zgbrfs ]
zgbsl.l	solve the linear system Ax = b for a matrix A in banded storage, which has been LU-factored by xGBCO or xGBFA, and vectors b and x.	[ zgbsl ]
zgbsv.l	compute the solution to a complex system of linear equations A ∗ X = B, where A is a band matrix of order N with KL subdiagonals and KU superdiagonals, and X and B are N-by-NRHS matrices	[ zgbsv ]
zgbsvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ zgbsvx ]
zgbtf2.l	compute an LU factorization of a complex m-by-n band matrix A using partial pivoting with row interchanges	[ zgbtf2 ]
zgbtrf.l	compute an LU factorization of a complex m-by-n band matrix A using partial pivoting with row interchanges	[ zgbtrf ]
zgbtrs.l	solve a system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B with a general band matrix A using the LU factorization computed by ZGBTRF	[ zgbtrs ]
zgebak.l	form the right or left eigenvectors of a complex general matrix by backward transformation on the computed eigenvectors of the balanced matrix output by ZGEBAL	[ zgebak ]
zgebal.l	balance a general complex matrix A	[ zgebal ]
zgebd2.l	reduce a complex general m by n matrix A to upper or lower real bidiagonal form B by a unitary transformation	[ zgebd2 ]
zgebrd.l	reduce a general complex M-by-N matrix A to upper or lower bidiagonal form B by a unitary transformation	[ zgebrd ]
zgeco.l	compute the LU factorization and estimate the condition number of a general matrix A. If the condition number is not needed then xGEFA is slightly faster. It is typical to follow a call to xGECO with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant and inverse of A.	[ zgeco ]
zgecon.l	estimate the reciprocal of the condition number of a general complex matrix A, in either the 1-norm or the infinity-norm, using the LU factorization computed by ZGETRF	[ zgecon ]
zgedi.l	compute the determinant and inverse of a general matrix A, which has been LU-factored by xGECO or xGEFA.	[ zgedi ]
zgeequ.l	compute row and column scalings intended to equilibrate an M-by-N matrix A and reduce its condition number	[ zgeequ ]
zgees.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues, the Schur form T, and, optionally, the matrix of Schur vectors Z	[ zgees ]
zgeesx.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues, the Schur form T, and, optionally, the matrix of Schur vectors Z	[ zgeesx ]
zgeev.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ zgeev ]
zgeevx.l	compute for an N-by-N complex nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors	[ zgeevx ]
zgefa.l	compute the LU factorization of a general matrix A. It is typical to follow a call to xGEFA with a call to xGESL to solve Ax = b or to xGEDI to compute the determinant of A.	[ zgefa ]
zgegs.l	compute for a pair of N-by-N complex nonsymmetric matrices A,	[ zgegs ]
zgegv.l	compute for a pair of N-by-N complex nonsymmetric matrices A and B, the generalized eigenvalues (alpha, beta), and optionally,	[ zgegv ]
zgehd2.l	reduce a complex general matrix A to upper Hessenberg form H by a unitary similarity transformation	[ zgehd2 ]
zgehrd.l	reduce a complex general matrix A to upper Hessenberg form H by a unitary similarity transformation	[ zgehrd ]
zgelq2.l	compute an LQ factorization of a complex m by n matrix A	[ zgelq2 ]
zgelqf.l	compute an LQ factorization of a complex M-by-N matrix A	[ zgelqf ]
zgels.l	solve overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A	[ zgels ]
zgelss.l	compute the minimum norm solution to a complex linear least squares problem	[ zgelss ]
zgelsx.l	compute the minimum-norm solution to a complex linear least squares problem	[ zgelsx ]
zgemm.l	perform one of the matrix-matrix operations C := alpha∗op( A )∗op( B ) + beta∗C	[ zgemm ]
zgemv.l	perform one of the matrix-vector operations y := alpha∗A∗x + beta∗y, or y := alpha∗A’∗x + beta∗y, or y := alpha∗conjg( A’ )∗x + beta∗y	[ zgemv ]
zgeql2.l	compute a QL factorization of a complex m by n matrix A	[ zgeql2 ]
zgeqlf.l	compute a QL factorization of a complex M-by-N matrix A	[ zgeqlf ]
zgeqpf.l	compute a QR factorization with column pivoting of a complex M-by-N matrix A	[ zgeqpf ]
zgeqr2.l	compute a QR factorization of a complex m by n matrix A	[ zgeqr2 ]
zgeqrf.l	compute a QR factorization of a complex M-by-N matrix A	[ zgeqrf ]
zgerc.l	perform the rank 1 operation A := alpha∗x∗conjg( y’ ) + A	[ zgerc ]
zgerfs.l	improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solution	[ zgerfs ]
zgerq2.l	compute an RQ factorization of a complex m by n matrix A	[ zgerq2 ]
zgerqf.l	compute an RQ factorization of a complex M-by-N matrix A	[ zgerqf ]
zgeru.l	perform the rank 1 operation A := alpha∗x∗y’ + A	[ zgeru ]
zgesl.l	solve the linear system Ax = b for a general matrix A, which has been LU- factored by xGECO or xGEFA, and vectors b and x.	[ zgesl ]
zgesv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zgesv ]
zgesvd.l	compute the singular value decomposition (SVD) of a complex M-by-N matrix A, optionally computing the left and/or right singular vectors	[ zgesvd ]
zgesvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ zgesvx ]
zgetf2.l	compute an LU factorization of a general m-by-n matrix A using partial pivoting with row interchanges	[ zgetf2 ]
zgetrf.l	compute an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges	[ zgetrf ]
zgetri.l	compute the inverse of a matrix using the LU factorization computed by ZGETRF	[ zgetri ]
zgetrs.l	solve a system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B with a general N-by-N matrix A using the LU factorization computed by ZGETRF	[ zgetrs ]
zggbak.l	form the right or left eigenvectors of a complex generalized eigenvalue problem A∗x = lambda∗B∗x, by backward transformation on the computed eigenvectors of the balanced pair of matrices output by ZGGBAL	[ zggbak ]
zggbal.l	balance a pair of general complex matrices (A,B)	[ zggbal ]
zggglm.l	solve a general Gauss-Markov linear model (GLM) problem	[ zggglm ]
zgghrd.l	reduce a pair of complex matrices (A,B) to generalized upper Hessenberg form using unitary transformations, where A is a general matrix and B is upper triangular	[ zgghrd ]
zgglse.l	solve the linear equality-constrained least squares (LSE) problem	[ zgglse ]
zggqrf.l	compute a generalized QR factorization of an N-by-M matrix A and an N-by-P matrix B	[ zggqrf ]
zggrqf.l	compute a generalized RQ factorization of an M-by-N matrix A and a P-by-N matrix B	[ zggrqf ]
zggsvd.l	compute the generalized singular value decomposition (GSVD) of an M-by-N complex matrix A and P-by-N complex matrix B	[ zggsvd ]
zggsvp.l	compute unitary matrices U, V and Q such that N-K-L K L U’∗A∗Q = K ( 0 A12 A13 ) if M-K-L >= 0	[ zggsvp ]
zgtcon.l	estimate the reciprocal of the condition number of a complex tridiagonal matrix A using the LU factorization as computed by ZGTTRF	[ zgtcon ]
zgtrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is tridiagonal, and provides error bounds and backward error estimates for the solution	[ zgtrfs ]
zgtsl.l	solve the linear system Ax = b for a tridiagonal matrix A and vectors b and x.	[ zgtsl ]
zgtsv.l	solve the equation A∗X = B,	[ zgtsv ]
zgtsvx.l	use the LU factorization to compute the solution to a complex system of linear equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ zgtsvx ]
zgttrf.l	compute an LU factorization of a complex tridiagonal matrix A using elimination with partial pivoting and row interchanges	[ zgttrf ]
zgttrs.l	solve one of the systems of equations A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ zgttrs ]
zhbev.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ zhbev ]
zhbevd.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ zhbevd ]
zhbevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian band matrix A	[ zhbevx ]
zhbgst.l	reduce a complex Hermitian-definite banded generalized eigenproblem A∗x = lambda∗B∗x to standard form C∗y = lambda∗y,	[ zhbgst ]
zhbgv.l	compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite banded eigenproblem, of the form A∗x=(lambda)∗B∗x	[ zhbgv ]
zhbmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ zhbmv ]
zhbtrd.l	reduce a complex Hermitian band matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ zhbtrd ]
zhecon.l	estimate the reciprocal of the condition number of a complex Hermitian matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHETRF	[ zhecon ]
zheev.l	compute all eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ zheev ]
zheevd.l	compute all eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ zheevd ]
zheevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A	[ zheevx ]
zhegs2.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form	[ zhegs2 ]
zhegst.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form	[ zhegst ]
zhegv.l	compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ zhegv ]
zhemm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ zhemm ]
zhemv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ zhemv ]
zher.l	perform the hermitian rank 1 operation A := alpha∗x∗conjg( x’ ) + A	[ zher ]
zher2.l	perform the hermitian rank 2 operation A := alpha∗x∗conjg( y’ ) + conjg( alpha )∗y∗conjg( x’ ) + A	[ zher2 ]
zher2k.l	perform one of the hermitian rank 2k operations C := alpha∗A∗conjg( B’ ) + conjg( alpha )∗B∗conjg( A’ ) + beta∗C or C := alpha∗conjg( A’ )∗B + conjg( alpha )∗conjg( B’ )∗A + beta∗C	[ zher2k ]
zherfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian indefinite, and provides error bounds and backward error estimates for the solution	[ zherfs ]
zherk.l	perform one of the hermitian rank k operations C := alpha∗A∗conjg( A’ ) + beta∗C or C := alpha∗conjg( A’ )∗A + beta∗C	[ zherk ]
zhesv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zhesv ]
zhesvx.l	use the diagonal pivoting factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ zhesvx ]
zhetd2.l	reduce a complex Hermitian matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ zhetd2 ]
zhetf2.l	compute the factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ zhetf2 ]
zhetrd.l	reduce a complex Hermitian matrix A to real symmetric tridiagonal form T by a unitary similarity transformation	[ zhetrd ]
zhetrf.l	compute the factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ zhetrf ]
zhetri.l	compute the inverse of a complex Hermitian indefinite matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHETRF	[ zhetri ]
zhetrs.l	solve a system of linear equations A∗X = B with a complex Hermitian matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHETRF	[ zhetrs ]
zhgeqz.l	implement a single-shift version of the QZ method for finding the generalized eigenvalues w(i)=ALPHA(i)/BETA(i) of the equation det( A - w(i) B ) = 0 If JOB=’S’, then the pair (A,B) is simultaneously reduced to Schur form (i.e., A and B are both upper triangular) by applying one unitary tranformation (usually called Q) on the left and another (usually called Z) on the right	[ zhgeqz ]
zhico.l	compute the UDU factorization and condition number of a Hermitian matrix A. If the condition number is not needed then xHIFA is slightly faster. It is typical to follow a call to xHICO with a call to xHISL to solve Ax = b or to xHIDI to compute the determinant, inverse, and inertia of A.	[ zhico ]
zhidi.l	compute the determinant, inertia, and inverse of a Hermitian matrix A, which has been UDU-factored by xHICO or xHIFA.	[ zhidi ]
zhifa.l	compute the UDU factorization of a Hermitian matrix A. It is typical to follow a call to xHIFA with a call to xHISL to solve Ax = b or to xHIDI to compute the determinant, inverse, and inertia of A.	[ zhifa ]
zhisl.l	solve the linear system Ax = b for a Hermitian matrix A, which has been UDU-factored by xHICO or xHIFA, and vectors b and x.	[ zhisl ]
zhpco.l	compute the UDU factorization and condition number of a Hermitian matrix A in packed storage. If the condition number is not needed then xHPFA is slightly faster. It is typical to follow a call to xHPCO with a call to xHPSL to solve Ax = b or to xHPDI to compute the determinant, inverse, and inertia of A.	[ zhpco ]
zhpcon.l	estimate the reciprocal of the condition number of a complex Hermitian packed matrix A using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHPTRF	[ zhpcon ]
zhpdi.l	compute the determinant, inertia, and inverse of a Hermitian matrix A in packed storage, which has been UDU-factored by xHPCO or xHPFA.	[ zhpdi ]
zhpev.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix in packed storage	[ zhpev ]
zhpevd.l	compute all the eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A in packed storage	[ zhpevd ]
zhpevx.l	compute selected eigenvalues and, optionally, eigenvectors of a complex Hermitian matrix A in packed storage	[ zhpevx ]
zhpfa.l	compute the UDU factorization of a Hermitian matrix A in packed storage. It is typical to follow a call to xHPFA with a call to xHPSL to solve Ax = b or to xHPDI to compute the determinant, inverse, and inertia of A.	[ zhpfa ]
zhpgst.l	reduce a complex Hermitian-definite generalized eigenproblem to standard form, using packed storage	[ zhpgst ]
zhpgv.l	compute all the eigenvalues and, optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A∗x=(lambda)∗B∗x, A∗Bx=(lambda)∗x, or B∗A∗x=(lambda)∗x	[ zhpgv ]
zhpmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y	[ zhpmv ]
zhpr.l	perform the hermitian rank 1 operation A := alpha∗x∗conjg( x’ ) + A	[ zhpr ]
zhpr2.l	perform the hermitian rank 2 operation A := alpha∗x∗conjg( y’ ) + conjg( alpha )∗y∗conjg( x’ ) + A	[ zhpr2 ]
zhprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian indefinite and packed, and provides error bounds and backward error estimates for the solution	[ zhprfs ]
zhpsl.l	solve the linear system Ax = b for a Hermitian matrix A in packed storage, which has been UDU-factored by xHPCO or xHPFA, and vectors b and x.	[ zhpsl ]
zhpsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zhpsv ]
zhpsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B, where A is an N-by-N Hermitian matrix stored in packed format and X and B are N-by-NRHS matrices	[ zhpsvx ]
zhptrd.l	reduce a complex Hermitian matrix A stored in packed form to real symmetric tridiagonal form T by a unitary similarity transformation	[ zhptrd ]
zhptrf.l	compute the factorization of a complex Hermitian packed matrix A using the Bunch-Kaufman diagonal pivoting method	[ zhptrf ]
zhptri.l	compute the inverse of a complex Hermitian indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHPTRF	[ zhptri ]
zhptrs.l	solve a system of linear equations A∗X = B with a complex Hermitian matrix A stored in packed format using the factorization A = U∗D∗U∗∗H or A = L∗D∗L∗∗H computed by ZHPTRF	[ zhptrs ]
zhsein.l	use inverse iteration to find specified right and/or left eigenvectors of a complex upper Hessenberg matrix H	[ zhsein ]
zhseqr.l	compute the eigenvalues of a complex upper Hessenberg matrix H, and, optionally, the matrices T and Z from the Schur decomposition H = Z T Z∗∗H, where T is an upper triangular matrix (the Schur form), and Z is the unitary matrix of Schur vectors	[ zhseqr ]
zlabrd.l	reduce the first NB rows and columns of a complex general m by n matrix A to upper or lower real bidiagonal form by a unitary transformation Q’ ∗ A ∗ P, and returns the matrices X and Y which are needed to apply the transformation to the unreduced part of A	[ zlabrd ]
zlacgv.l	conjugate a complex vector of length N	[ zlacgv ]
zlacon.l	estimate the 1-norm of a square, complex matrix A	[ zlacon ]
zlacpy.l	copie all or part of a two-dimensional matrix A to another matrix B	[ zlacpy ]
zlacrm.l	perform a very simple matrix-matrix multiplication	[ zlacrm ]
zlacrt.l	applie a plane rotation, where the cos and sin (C and S) are complex and the vectors CX and CY are complex	[ zlacrt ]
zladiv.l	:= X / Y, where X and Y are complex	[ zladiv ]
zlaed0.l	the divide and conquer method, ZLAED0 computes all eigenvalues of a symmetric tridiagonal matrix which is one diagonal block of those from reducing a dense or band Hermitian matrix and corresponding eigenvectors of the dense or band matrix	[ zlaed0 ]
zlaed7.l	compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix	[ zlaed7 ]
zlaed8.l	merge the two sets of eigenvalues together into a single sorted set	[ zlaed8 ]
zlaein.l	use inverse iteration to find a right or left eigenvector corresponding to the eigenvalue W of a complex upper Hessenberg matrix H	[ zlaein ]
zlaesy.l	compute the eigendecomposition of a 2-by-2 symmetric matrix ( ( A, B );( B, C ) ) provided the norm of the matrix of eigenvectors is larger than some threshold value	[ zlaesy ]
zlaev2.l	compute the eigendecomposition of a 2-by-2 Hermitian matrix [ A B ] [ CONJG(B) C ]	[ zlaev2 ]
zlags2.l	compute 2-by-2 unitary matrices U, V and Q, such that if ( UPPER ) then U’∗A∗Q = U’∗( A1 A2 )∗Q = ( x 0 ) ( 0 A3 ) ( x x ) and V’∗B∗Q = V’∗( B1 B2 )∗Q = ( x 0 ) ( 0 B3 ) ( x x ) or if ( .NOT.UPPER ) then U’∗A∗Q = U’∗( A1 0 )∗Q = ( x x ) ( A2 A3 ) ( 0 x ) and V’∗B∗Q = V’∗( B1 0 )∗Q = ( x x ) ( B2 B3 ) ( 0 x ) where U = ( CSU SNU ), V = ( CSV SNV ),	[ zlags2 ]
zlagtm.l	perform a matrix-vector product of the form B := alpha ∗ A ∗ X + beta ∗ B where A is a tridiagonal matrix of order N, B and X are N by NRHS matrices, and alpha and beta are real scalars, each of which may be 0., 1., or -1	[ zlagtm ]
zlahef.l	compute a partial factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method	[ zlahef ]
zlahqr.l	i an auxiliary routine called by ZHSEQR to update the eigenvalues and Schur decomposition already computed by ZHSEQR, by dealing with the Hessenberg submatrix in rows and columns ILO to IHI	[ zlahqr ]
zlahrd.l	reduce the first NB columns of a complex general n-by-(n-k+1) matrix A so that elements below the k-th subdiagonal are zero	[ zlahrd ]
zlaic1.l	applie one step of incremental condition estimation in its simplest version	[ zlaic1 ]
zlangb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n band matrix A, with kl sub-diagonals and ku super-diagonals	[ zlangb ]
zlange.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex matrix A	[ zlange ]
zlangt.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex tridiagonal matrix A	[ zlangt ]
zlanhb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n hermitian band matrix A, with k super-diagonals	[ zlanhb ]
zlanhe.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex hermitian matrix A	[ zlanhe ]
zlanhp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex hermitian matrix A, supplied in packed form	[ zlanhp ]
zlanhs.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a Hessenberg matrix A	[ zlanhs ]
zlanht.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex Hermitian tridiagonal matrix A	[ zlanht ]
zlansb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n symmetric band matrix A, with k super-diagonals	[ zlansb ]
zlansp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex symmetric matrix A, supplied in packed form	[ zlansp ]
zlansy.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex symmetric matrix A	[ zlansy ]
zlantb.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of an n by n triangular band matrix A, with ( k + 1 ) diagonals	[ zlantb ]
zlantp.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a triangular matrix A, supplied in packed form	[ zlantp ]
zlantr.l	return the value of the one norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix A	[ zlantr ]
zlapll.l	two column vectors X and Y, let A = ( X Y )	[ zlapll ]
zlapmt.l	rearrange the columns of the M by N matrix X as specified by the permutation K(1),K(2),...,K(N) of the integers 1,...,N	[ zlapmt ]
zlaqgb.l	equilibrate a general M by N band matrix A with KL subdiagonals and KU superdiagonals using the row and scaling factors in the vectors R and C	[ zlaqgb ]
zlaqge.l	equilibrate a general M by N matrix A using the row and scaling factors in the vectors R and C	[ zlaqge ]
zlaqhb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ zlaqhb ]
zlaqhe.l	equilibrate a Hermitian matrix A using the scaling factors in the vector S	[ zlaqhe ]
zlaqhp.l	equilibrate a Hermitian matrix A using the scaling factors in the vector S	[ zlaqhp ]
zlaqsb.l	equilibrate a symmetric band matrix A using the scaling factors in the vector S	[ zlaqsb ]
zlaqsp.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ zlaqsp ]
zlaqsy.l	equilibrate a symmetric matrix A using the scaling factors in the vector S	[ zlaqsy ]
zlar2v.l	applie a vector of complex plane rotations with real cosines from both sides to a sequence of 2-by-2 complex Hermitian matrices,	[ zlar2v ]
zlarf.l	applie a complex elementary reflector H to a complex M-by-N matrix C, from either the left or the right	[ zlarf ]
zlarfb.l	applie a complex block reflector H or its transpose H’ to a complex M-by-N matrix C, from either the left or the right	[ zlarfb ]
zlarfg.l	generate a complex elementary reflector H of order n, such that H’ ∗ ( alpha ) = ( beta ), H’ ∗ H = I	[ zlarfg ]
zlarft.l	form the triangular factor T of a complex block reflector H of order n, which is defined as a product of k elementary reflectors	[ zlarft ]
zlarfx.l	applie a complex elementary reflector H to a complex m by n matrix C, from either the left or the right	[ zlarfx ]
zlargv.l	generate a vector of complex plane rotations with real cosines, determined by elements of the complex vectors x and y	[ zlargv ]
zlarnv.l	return a vector of n random complex numbers from a uniform or normal distribution	[ zlarnv ]
zlartg.l	generate a plane rotation so that [ CS SN ] [ F ] [ R ] [ __ ]	[ zlartg ]
zlartv.l	applie a vector of complex plane rotations with real cosines to elements of the complex vectors x and y	[ zlartv ]
zlascl.l	multiply the M by N complex matrix A by the real scalar CTO/CFROM	[ zlascl ]
zlaset.l	initialize a 2-D array A to BETA on the diagonal and ALPHA on the offdiagonals	[ zlaset ]
zlasr.l	perform the transformation A := P∗A, when SIDE = ’L’ or ’l’ ( Left-hand side ) A := A∗P’, when SIDE = ’R’ or ’r’ ( Right-hand side ) where A is an m by n complex matrix and P is an orthogonal matrix,	[ zlasr ]
zlassq.l	return the values scl and ssq such that ( scl∗∗2 )∗ssq = x( 1 )∗∗2 +...+ x( n )∗∗2 + ( scale∗∗2 )∗sumsq,	[ zlassq ]
zlaswp.l	perform a series of row interchanges on the matrix A	[ zlaswp ]
zlasyf.l	compute a partial factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ zlasyf ]
zlatbs.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ zlatbs ]
zlatps.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ zlatps ]
zlatrd.l	reduce NB rows and columns of a complex Hermitian matrix A to Hermitian tridiagonal form by a unitary similarity transformation Q’ ∗ A ∗ Q, and returns the matrices V and W which are needed to apply the transformation to the unreduced part of A	[ zlatrd ]
zlatrs.l	solve one of the triangular systems A ∗ x = s∗b, A∗∗T ∗ x = s∗b, or A∗∗H ∗ x = s∗b,	[ zlatrs ]
zlatzm.l	applie a Householder matrix generated by ZTZRQF to a matrix	[ zlatzm ]
zlauu2.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ zlauu2 ]
zlauum.l	compute the product U ∗ U’ or L’ ∗ L, where the triangular factor U or L is stored in the upper or lower triangular part of the array A	[ zlauum ]
zpbco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in banded storage. If the condition number is not needed then xPBFA is slightly faster. It is typical to follow a call to xPBCO with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ zpbco ]
zpbcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite band matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPBTRF	[ zpbcon ]
zpbdi.l	compute the determinant of a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA.	[ zpbdi ]
zpbequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite band matrix A and reduce its condition number (with respect to the two-norm)	[ zpbequ ]
zpbfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in banded storage. It is typical to follow a call to xPBFA with a call to xPBSL to solve Ax = b or to xPBDI to compute the determinant of A.	[ zpbfa ]
zpbrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and banded, and provides error bounds and backward error estimates for the solution	[ zpbrfs ]
zpbsl.l	section solve the linear system Ax = b for a symmetric positive definite matrix A in banded storage, which has been Cholesky-factored by xPBCO or xPBFA, and vectors b and x.	[ zpbsl ]
zpbstf.l	compute a split Cholesky factorization of a complex Hermitian positive definite band matrix A	[ zpbstf ]
zpbsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zpbsv ]
zpbsvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ zpbsvx ]
zpbtf2.l	compute the Cholesky factorization of a complex Hermitian positive definite band matrix A	[ zpbtf2 ]
zpbtrf.l	compute the Cholesky factorization of a complex Hermitian positive definite band matrix A	[ zpbtrf ]
zpbtrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite band matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPBTRF	[ zpbtrs ]
zpoco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A. If the condition number is not needed then xPOFA is slightly faster. It is typical to follow a call to xPOCO with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ zpoco ]
zpocon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPOTRF	[ zpocon ]
zpodi.l	compute the determinant and inverse of a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO, xPOFA, or xQRDC.	[ zpodi ]
zpoequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite matrix A and reduce its condition number (with respect to the two-norm)	[ zpoequ ]
zpofa.l	compute a Cholesky factorization of a symmetric positive definite matrix A. It is typical to follow a call to xPOFA with a call to xPOSL to solve Ax = b or to xPODI to compute the determinant and inverse of A.	[ zpofa ]
zporfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite,	[ zporfs ]
zposl.l	solve the linear system Ax = b for a symmetric positive definite matrix A, which has been Cholesky-factored by xPOCO or xPOFA, and vectors b and x.	[ zposl ]
zposv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zposv ]
zposvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ zposvx ]
zpotf2.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A	[ zpotf2 ]
zpotrf.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A	[ zpotrf ]
zpotri.l	compute the inverse of a complex Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPOTRF	[ zpotri ]
zpotrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPOTRF	[ zpotrs ]
zppco.l	compute a Cholesky factorization and condition number of a symmetric positive definite matrix A in packed storage. If the condition number is not needed then xPPFA is slightly faster. It is typical to follow a call to xPPCO with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ zppco ]
zppcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite packed matrix using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPPTRF	[ zppcon ]
zppdi.l	compute the determinant and inverse of a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA.	[ zppdi ]
zppequ.l	compute row and column scalings intended to equilibrate a Hermitian positive definite matrix A in packed storage and reduce its condition number (with respect to the two-norm)	[ zppequ ]
zppfa.l	compute a Cholesky factorization of a symmetric positive definite matrix A in packed storage. It is typical to follow a call to xPPFA with a call to xPPSL to solve Ax = b or to xPPDI to compute the determinant and inverse of A.	[ zppfa ]
zpprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and packed, and provides error bounds and backward error estimates for the solution	[ zpprfs ]
zppsl.l	solve the linear system Ax = b for a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA, and vectors b and x.	[ zppsl ]
zppsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zppsv ]
zppsvx.l	use the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H to compute the solution to a complex system of linear equations A ∗ X = B,	[ zppsvx ]
zpptrf.l	compute the Cholesky factorization of a complex Hermitian positive definite matrix A stored in packed format	[ zpptrf ]
zpptri.l	compute the inverse of a complex Hermitian positive definite matrix A using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPPTRF	[ zpptri ]
zpptrs.l	solve a system of linear equations A∗X = B with a Hermitian positive definite matrix A in packed storage using the Cholesky factorization A = U∗∗H∗U or A = L∗L∗∗H computed by ZPPTRF	[ zpptrs ]
zptcon.l	compute the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite tridiagonal matrix using the factorization A = L∗D∗L∗∗H or A = U∗∗H∗D∗U computed by ZPTTRF	[ zptcon ]
zpteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric positive definite tridiagonal matrix by first factoring the matrix using DPTTRF and then calling ZBDSQR to compute the singular values of the bidiagonal factor	[ zpteqr ]
zptrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and tridiagonal, and provides error bounds and backward error estimates for the solution	[ zptrfs ]
zptsl.l	solve the linear system Ax = b for a symmetric positive definite tridiagonal matrix A and vectors b and x.	[ zptsl ]
zptsv.l	compute the solution to a complex system of linear equations A∗X = B, where A is an N-by-N Hermitian positive definite tridiagonal matrix, and X and B are N-by-NRHS matrices	[ zptsv ]
zptsvx.l	use the factorization A = L∗D∗L∗∗H to compute the solution to a complex system of linear equations A∗X = B, where A is an N-by-N Hermitian positive definite tridiagonal matrix and X and B are N-by-NRHS matrices	[ zptsvx ]
zpttrf.l	compute the factorization of a complex Hermitian positive definite tridiagonal matrix A	[ zpttrf ]
zpttrs.l	solve a system of linear equations A ∗ X = B with a Hermitian positive definite tridiagonal matrix A using the factorization A = U∗∗H∗D∗U or A = L∗D∗L∗∗H computed by ZPTTRF	[ zpttrs ]
zqrdc.l	compute the QR factorization of a general matrix A. It is typical to follow a call to xQRDC with a call to xQRSL to solve Ax = b or to xPODI to compute the determinant of A.	[ zqrdc ]
zqrsl.l	solve the linear system Ax = b for a general matrix A, which has been QR- factored by xQRDC, and vectors b and x.	[ zqrsl ]
zrot.l	apply a plane rotation, where the cos (C) is real and the sin (S) is complex, and the vectors CX and CY are complex	[ zrot ]
zrotg.l	Construct a Given’s plane rotation	[ ZROTG ]
zscal.l	Compute y := alpha ∗ y	[ ZSCAL ]
zsico.l	compute the UDU factorization and condition number of a symmetric matrix A. If the condition number is not needed then xSIFA is slightly faster. It is typical to follow a call to xSICO with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ zsico ]
zsidi.l	compute the determinant, inertia, and inverse of a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA.	[ zsidi ]
zsifa.l	compute the UDU factorization of a symmetric matrix A. It is typical to follow a call to xSIFA with a call to xSISL to solve Ax = b or to xSIDI to compute the determinant, inverse, and inertia of A.	[ zsifa ]
zsisl.l	solve the linear system Ax = b for a symmetric matrix A, which has been UDU-factored by xSICO or xSIFA, and vectors b and x.	[ zsisl ]
zspco.l	compute the UDU factorization and condition number of a symmetric matrix A in packed storage. If the condition number is not needed then xSPFA is slightly faster. It is typical to follow a call to xSPCO with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ zspco ]
zspcon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex symmetric packed matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSPTRF	[ zspcon ]
zspdi.l	compute the determinant, inertia, and inverse of a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA.	[ zspdi ]
zspfa.l	compute the UDU factorization of a symmetric matrix A in packed storage. It is typical to follow a call to xSPFA with a call to xSPSL to solve Ax = b or to xSPDI to compute the determinant, inverse, and inertia of A.	[ zspfa ]
zspmv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y,	[ zspmv ]
zspr.l	perform the symmetric rank 1 operation A := alpha∗x∗conjg( x’ ) + A,	[ zspr ]
zsprfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite and packed, and provides error bounds and backward error estimates for the solution	[ zsprfs ]
zspsl.l	solve the linear system Ax = b for a symmetric matrix A in packed storage, which has been UDU-factored by xSPCO or xSPFA, and vectors b and x.	[ zspsl ]
zspsv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zspsv ]
zspsvx.l	use the diagonal pivoting factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T to compute the solution to a complex system of linear equations A ∗ X = B, where A is an N-by-N symmetric matrix stored in packed format and X and B are N-by-NRHS matrices	[ zspsvx ]
zsptrf.l	compute the factorization of a complex symmetric matrix A stored in packed format using the Bunch-Kaufman diagonal pivoting method	[ zsptrf ]
zsptri.l	compute the inverse of a complex symmetric indefinite matrix A in packed storage using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSPTRF	[ zsptri ]
zsptrs.l	solve a system of linear equations A∗X = B with a complex symmetric matrix A stored in packed format using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSPTRF	[ zsptrs ]
zstedc.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the divide and conquer method	[ zstedc ]
zstein.l	compute the eigenvectors of a real symmetric tridiagonal matrix T corresponding to specified eigenvalues, using inverse iteration	[ zstein ]
zsteqr.l	compute all eigenvalues and, optionally, eigenvectors of a symmetric tridiagonal matrix using the implicit QL or QR method	[ zsteqr ]
zsvdc.l	compute the singular value decomposition of a general matrix A.	[ zsvdc ]
zswap.l	Exchange vectors x and y.	[ ZSWAP ]
zsycon.l	estimate the reciprocal of the condition number (in the 1-norm) of a complex symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSYTRF	[ zsycon ]
zsymm.l	perform one of the matrix-matrix operations C := alpha∗A∗B + beta∗C or C := alpha∗B∗A + beta∗C	[ zsymm ]
zsymv.l	perform the matrix-vector operation y := alpha∗A∗x + beta∗y,	[ zsymv ]
zsyr.l	perform the symmetric rank 1 operation A := alpha∗x∗( x’ ) + A,	[ zsyr ]
zsyr2k.l	perform one of the symmetric rank 2k operations C := alpha∗A∗B’ + alpha∗B∗A’ + beta∗C or C := alpha∗A’∗B + alpha∗B’∗A + beta∗C	[ zsyr2k ]
zsyrfs.l	improve the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite, and provides error bounds and backward error estimates for the solution	[ zsyrfs ]
zsyrk.l	perform one of the symmetric rank k operations C := alpha∗A∗A’ + beta∗C or C := alpha∗A’∗A + beta∗C	[ zsyrk ]
zsysv.l	compute the solution to a complex system of linear equations A ∗ X = B,	[ zsysv ]
zsysvx.l	use the diagonal pivoting factorization to compute the solution to a complex system of linear equations A ∗ X = B,	[ zsysvx ]
zsytf2.l	compute the factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ zsytf2 ]
zsytrf.l	compute the factorization of a complex symmetric matrix A using the Bunch-Kaufman diagonal pivoting method	[ zsytrf ]
zsytri.l	compute the inverse of a complex symmetric indefinite matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSYTRF	[ zsytri ]
zsytrs.l	solve a system of linear equations A∗X = B with a complex symmetric matrix A using the factorization A = U∗D∗U∗∗T or A = L∗D∗L∗∗T computed by ZSYTRF	[ zsytrs ]
ztbcon.l	estimate the reciprocal of the condition number of a triangular band matrix A, in either the 1-norm or the infinity-norm	[ ztbcon ]
ztbmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ztbmv ]
ztbrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular band coefficient matrix	[ ztbrfs ]
ztbsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ztbsv ]
ztbtrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ztbtrs ]
ztgevc.l	compute some or all of the right and/or left generalized eigenvectors of a pair of complex upper triangular matrices (A,B)	[ ztgevc ]
ztgsja.l	compute the generalized singular value decomposition (GSVD) of two complex upper triangular (or trapezoidal) matrices A and B	[ ztgsja ]
ztpcon.l	estimate the reciprocal of the condition number of a packed triangular matrix A, in either the 1-norm or the infinity-norm	[ ztpcon ]
ztpmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ztpmv ]
ztprfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular packed coefficient matrix	[ ztprfs ]
ztpsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ztpsv ]
ztptri.l	compute the inverse of a complex upper or lower triangular matrix A stored in packed format	[ ztptri ]
ztptrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ztptrs ]
ztrco.l	estimate the condition number of a triangular matrix A. It is typical to follow a call to xTRCO with a call to xTRSL to solve Ax = b or to xTRDI to compute the determinant and inverse of A.	[ ztrco ]
ztrcon.l	estimate the reciprocal of the condition number of a triangular matrix A, in either the 1-norm or the infinity-norm	[ ztrcon ]
ztrdi.l	compute the determinant and inverse of a triangular matrix A.	[ ztrdi ]
ztrevc.l	compute some or all of the right and/or left eigenvectors of a complex upper triangular matrix T	[ ztrevc ]
ztrexc.l	reorder the Schur factorization of a complex matrix A = Q∗T∗Q∗∗H, so that the diagonal element of T with row index IFST is moved to row ILST	[ ztrexc ]
ztrmm.l	perform one of the matrix-matrix operations B := alpha∗op( A )∗B or B := alpha∗B∗op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A’ or op( A ) = conjg( A’ )	[ ztrmm ]
ztrmv.l	perform one of the matrix-vector operations x := A∗x, or x := A’∗x, or x := conjg( A’ )∗x	[ ztrmv ]
ztrrfs.l	provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix	[ ztrrfs ]
ztrsen.l	reorder the Schur factorization of a complex matrix A = Q∗T∗Q∗∗H, so that a selected cluster of eigenvalues appears in the leading positions on the diagonal of the upper triangular matrix T, and the leading columns of Q form an orthonormal basis of the corresponding right invariant subspace	[ ztrsen ]
ztrsl.l	solve the linear system Ax = b for a triangular matrix A and vectors b and x.	[ ztrsl ]
ztrsm.l	solve one of the matrix equations op( A )∗X = alpha∗B, or X∗op( A ) = alpha∗B	[ ztrsm ]
ztrsna.l	estimate reciprocal condition numbers for specified eigenvalues and/or right eigenvectors of a complex upper triangular matrix T (or of any matrix Q∗T∗Q∗∗H with Q unitary)	[ ztrsna ]
ztrsv.l	solve one of the systems of equations A∗x = b, or A’∗x = b, or conjg( A’ )∗x = b	[ ztrsv ]
ztrsyl.l	solve the complex Sylvester matrix equation	[ ztrsyl ]
ztrti2.l	compute the inverse of a complex upper or lower triangular matrix	[ ztrti2 ]
ztrtri.l	compute the inverse of a complex upper or lower triangular matrix A	[ ztrtri ]
ztrtrs.l	solve a triangular system of the form A ∗ X = B, A∗∗T ∗ X = B, or A∗∗H ∗ X = B,	[ ztrtrs ]
ztzrqf.l	reduce the M-by-N ( M<=N ) complex upper trapezoidal matrix A to upper triangular form by means of unitary transformations	[ ztzrqf ]
zung2l.l	generate an m by n complex matrix Q with orthonormal columns,	[ zung2l ]
zung2r.l	generate an m by n complex matrix Q with orthonormal columns,	[ zung2r ]
zungbr.l	generate one of the complex unitary matrices Q or P∗∗H determined by ZGEBRD when reducing a complex matrix A to bidiagonal form	[ zungbr ]
zunghr.l	generate a complex unitary matrix Q which is defined as the product of IHI-ILO elementary reflectors of order N, as returned by ZGEHRD	[ zunghr ]
zungl2.l	generate an m-by-n complex matrix Q with orthonormal rows,	[ zungl2 ]
zunglq.l	generate an M-by-N complex matrix Q with orthonormal rows,	[ zunglq ]
zungql.l	generate an M-by-N complex matrix Q with orthonormal columns,	[ zungql ]
zungqr.l	generate an M-by-N complex matrix Q with orthonormal columns,	[ zungqr ]
zungr2.l	generate an m by n complex matrix Q with orthonormal rows,	[ zungr2 ]
zungrq.l	generate an M-by-N complex matrix Q with orthonormal rows,	[ zungrq ]
zungtr.l	generate a complex unitary matrix Q which is defined as the product of n-1 elementary reflectors of order N, as returned by ZHETRD	[ zungtr ]
zunm2l.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ zunm2l ]
zunm2r.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ zunm2r ]
zunmbr.l	VECT = ’Q’, ZUNMBR overwrites the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmbr ]
zunmhr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmhr ]
zunml2.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ zunml2 ]
zunmlq.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmlq ]
zunmql.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmql ]
zunmqr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmqr ]
zunmr2.l	overwrite the general complex m-by-n matrix C with Q ∗ C if SIDE = ’L’ and TRANS = ’N’, or Q’∗ C if SIDE = ’L’ and TRANS = ’C’, or C ∗ Q if SIDE = ’R’ and TRANS = ’N’, or C ∗ Q’ if SIDE = ’R’ and TRANS = ’C’,	[ zunmr2 ]
zunmrq.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmrq ]
zunmtr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zunmtr ]
zupgtr.l	generate a complex unitary matrix Q which is defined as the product of n-1 elementary reflectors H(i) of order n, as returned by ZHPTRD using packed storage	[ zupgtr ]
zupmtr.l	overwrite the general complex M-by-N matrix C with SIDE = ’L’ SIDE = ’R’ TRANS = ’N’	[ zupmtr ]

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