nd x.
sposv		sposv (3p)	- compute the solution to a real system of linear equations  A * X = B,
sposvx		sposvx (3p)	- 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,
spotf2		spotf2 (3p)	- compute the Cholesky factorization of a real symmetric positive definite matrix A
spotrf		spotrf (3p)	- compute the Cholesky factorization of a real symmetric positive definite matrix A
spotri		spotri (3p)	- 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
spotrs		spotrs (3p)	- 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
sppco		sppco (3p)	- 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.
sppcon		sppcon (3p)	- 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
sppdi		sppdi (3p)	- compute the determinant and inverse of a symmetric positive definite matrix A in packed storage, which has been Cholesky-factored by xPPCO or xPPFA.
sppequ		sppequ (3p)	- 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)
sppfa		sppfa (3p)	- 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.
spprfs		spprfs (3p)	- 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
sppsl		sppsl (3p)	- 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.
sppsv		sppsv (3p)	- compute the solution to a real system of linear equations  A * X = B,
sppsvx		sppsvx (3p)	- 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,
spptrf		spptrf (3p)	- compute the Cholesky factorization of a real symmetric positive definite matrix A stored in packed format
spptri		spptri (3p)	- 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
spptrs		spptrs (3p)	- 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
sptcon		sptcon (3p)	- 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
spteqr		spteqr (3p)	- 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
sptrfs		sptrfs (3p)	- 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
sptsl		sptsl (3p)	- solve the linear system Ax = b for a symmetric positive definite tridiagonal matrix A and vectors b and x.
sptsv		sptsv (3p)	- compute the solution to a real system of linear equations A*X = B, where A is an N-by-N symmetric positive definite tridiagementary reflectors
slarfx		slarfx (3p)	- applie a real elementary reflector H to a real m by n matrix C, from either the left or the right
slargv		slargv (3p)	- generate a vector of real plane rotations, determined by elements of the real vectors x and y
slarnv		slarnv (3p)	- return a vector of n random real numbers from a uniform or normal distribution
slartg		slartg (3p)	- generate a plane rotation so that   [ CS SN ] 
slartv		slartv (3p)	- applie a vector of real plane rotations to elements of the real vectors x and y
slaruv		slaruv (3p)	- return a vector of n random real numbers from a uniform (0,1)
slas2		slas2 (3p)	- compute the singular values of the 2-by-2 matrix  [ F G ]  [ 0 H ]
slascl		slascl (3p)	- multiply the M by N real matrix A by the real scalar CTO/CFROM
slaset		slaset (3p)	- initialize an m-by-n matrix A to BETA on the diagonal and ALPHA on the offdiagonals
slasq1		slasq1 (3p)	- SLASQ1 computes the singular values of a real N-by-N bidiagonal  matrix with diagonal D and off-diagonal E
slasq2		slasq2 (3p)	- 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
slasq3		slasq3 (3p)	- SLASQ3 is the workhorse of the whole bidiagonal SVD algorithm
slasq4		slasq4 (3p)	- SLASQ4 estimates TAU, the smallest eigenvalue of a matrix
slasr		slasr (3p)	- 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,
slasrt		slasrt (3p)	- the numbers in D in increasing order (if ID = 'I') or in decreasing order (if ID = 'D' )
slassq		slassq (3p)	- return the values scl and smsq such that   ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq,
slasv2		slasv2 (3p)	- compute the singular value decomposition of a 2-by-2 triangular matrix  [ F G ]  [ 0 H ]
slaswp		slaswp (3p)	- perform a series of row interchanges on the matrix A
slasy2		slasy2 (3p)	- solve for the