BAK

back transform eigenvectors of a balanced matrix

BAL

balance a matrix to attempt to improve subsequence eigenvalue / eigenvector computation

BRD

reduce a matrix to bidiagonal form

CON

estimate the condition number

EBZ

compute eigenvalues via bisection

EIN

compute eigenvectors via inverse iteration

EQR

compute eigenvalues and eigenvectors

EQU

compute equilibration scale factors

ERF

compute eigenvalues via QR

ES

compute eigenvalues and Schur factorization

ESX

compute eigenvalues, Schur factorization, and various condition numbers

EV

compute eigenvalues and eigenvectors

EVC

compute eigenvectors of a quasitriangular matrix

EVX

compute eigenvalues, eigenvectors, condition numbers with high accuracy

EXC

move a 1×1 or 2×2 block on the diagonal of a matrix in Schur canonical form to another place

GBR

generate U and V after reduction of A to bidiagonal form

GHR

generate a matrix after reduction to upper Hessenberg form

GLQ

generate Q from elementary reflectors computed during an LQ factorization

GQL

generate Q from elementary reflectors computed during a QL factorization

GQR

generate Q from elementary reflectors computed during a QR factorization

GRQ

generate Q from elementary reflectors computed during an RQ factorization

GST

reduce a matrix for a generalized eigenproblem to a matrix for a standard eigenproblem

GTR

generate Q from elementary reflectors computed during a reduction to tridiagonal form

GV

solve generalized eigenproblem

HRD

reduce to upper Hessenberg form

LQF

LQ factorization

LS

solve a linear least squares problem using QR or LQ factorization

LSS

solve a linear least squares problem using the singular value decomposition

LSX

solve a linear least squares problem using complete orthogonal factorization

MBR

multiply a matrix by U and V after reduction of A to bidiagonal form

MHR

multiply a matrix by Q using elementary reflectors computed during a Hessenberg reduction

MLQ

multiply a matrix by Q using elementary reflectors computed during an LQ factorization

MQL

multiply a matrix by Q using elementary reflectors computed during a QL factorization

MQR

multiply a matrix by Q using elementary reflectors computed during a QR factorization

MRQ

multiply a matrix by Q using elementary reflectors computed during an RQ factorization

MTR

multiply a matrix by Q using elementary reflectors computed during a tridiagonal reduction

QLF

QL factorization

QPF

QR factorization with column pivoting

QRF

QR factorization

RFS

refine the solution to a linear system

RQF

RQ factorization

SEN

move a set of eigenvalues of a matrix in Schur form and compute various condition numbers

SNA

compute condition numbers for a matrix in Schur canonical form

SQR

compute singular values of a bidiagonal matrix

SV

solve a linear system, nonexpert driver

SV

solve a linear system, expert driver

SVD

singular value decomposition

SYL

solve the Sylvester equation AX ± XB = C

TRD

reduce a matrix to real symmetric tridiagonal form

TRF

factor a matrix

TRI

compute inverse of a matrix

TRS

solve a triangular or LUfactored system
