Low-level LAPACK functions (scipy.linalg.lapack)

This module contains low-level functions from the LAPACK library.

Added in version 0.12.0.

Note

The common overwrite_<> option in many routines, allows the input arrays to be overwritten to avoid extra memory allocation. However this requires the array to satisfy two conditions which are memory order and the data type to match exactly the order and the type expected by the routine.

As an example, if you pass a double precision float array to any S.... routine which expects single precision arguments, f2py will create an intermediate array to match the argument types and overwriting will be performed on that intermediate array.

Similarly, if a C-contiguous array is passed, f2py will pass a FORTRAN-contiguous array internally. Please make sure that these details are satisfied. More information can be found in the f2py documentation.

Warning

These functions do little to no error checking. It is possible to cause crashes by misusing them, so prefer using the higher-level routines in scipy.linalg.

Note

Prefer using get_lapack_funcs to importing the bare functions directly. If you do, for example, from scipy.linalg.lapack import dsysv, the dsysv function may be either LP64 or ILP64, depending on how SciPy is built.

The following is more robust:

>>> from scipy.linalg.lapack import get_lapack_funcs
>>> dsysv = get_lapack_funcs('sysv', dtype='float64', ilp64='preferred')
>>> dsysv.int_dtype
dtype('int32')    # may vary

Finding functions

get_lapack_funcs(names[, arrays, dtype, ilp64])

Return available LAPACK function objects from names.

All functions

sgbcon(kl, ku, ab, ipiv, anorm[, norm, ldab])	Estimate the reciprocal condition number of a banded matrix from its gbtrf factorization (LAPACK sgbcon).
dgbcon(kl, ku, ab, ipiv, anorm[, norm, ldab])	Estimate the reciprocal condition number of a banded matrix from its gbtrf factorization (LAPACK dgbcon).
cgbcon(kl, ku, ab, ipiv, anorm[, norm, ldab])	Estimate the reciprocal condition number of a banded matrix from its gbtrf factorization (LAPACK cgbcon).
zgbcon(kl, ku, ab, ipiv, anorm[, norm, ldab])	Estimate the reciprocal condition number of a banded matrix from its gbtrf factorization (LAPACK zgbcon).
sgbsv(kl, ku, ab, b[, overwrite_ab, overwrite_b])	Solve a @ x = b for a banded a by LU factorization with partial pivoting (LAPACK sgbsv).
dgbsv(kl, ku, ab, b[, overwrite_ab, overwrite_b])	Solve a @ x = b for a banded a by LU factorization with partial pivoting (LAPACK dgbsv).
cgbsv(kl, ku, ab, b[, overwrite_ab, overwrite_b])	Solve a @ x = b for a banded a by LU factorization with partial pivoting (LAPACK cgbsv).
zgbsv(kl, ku, ab, b[, overwrite_ab, overwrite_b])	Solve a @ x = b for a banded a by LU factorization with partial pivoting (LAPACK zgbsv).
sgbtrf(ab, kl, ku[, m, n, ldab, overwrite_ab])	Compute the LU factorization of a banded matrix with partial pivoting (LAPACK sgbtrf).
dgbtrf(ab, kl, ku[, m, n, ldab, overwrite_ab])	Compute the LU factorization of a banded matrix with partial pivoting (LAPACK dgbtrf).
cgbtrf(ab, kl, ku[, m, n, ldab, overwrite_ab])	Compute the LU factorization of a banded matrix with partial pivoting (LAPACK cgbtrf).
zgbtrf(ab, kl, ku[, m, n, ldab, overwrite_ab])	Compute the LU factorization of a banded matrix with partial pivoting (LAPACK zgbtrf).
sgbtrs(ab, kl, ku, b, ipiv[, trans, n, ...])	Solve a banded system using the factorization from gbtrf (LAPACK sgbtrs).
dgbtrs(ab, kl, ku, b, ipiv[, trans, n, ...])	Solve a banded system using the factorization from gbtrf (LAPACK dgbtrs).
cgbtrs(ab, kl, ku, b, ipiv[, trans, n, ...])	Solve a banded system using the factorization from gbtrf (LAPACK cgbtrs).
zgbtrs(ab, kl, ku, b, ipiv[, trans, n, ...])	Solve a banded system using the factorization from gbtrf (LAPACK zgbtrs).
sgebal(a[, scale, permute, overwrite_a])	Balance a general matrix to improve eigenvalue accuracy (LAPACK sgebal).
dgebal(a[, scale, permute, overwrite_a])	Balance a general matrix to improve eigenvalue accuracy (LAPACK dgebal).
cgebal(a[, scale, permute, overwrite_a])	Balance a general matrix to improve eigenvalue accuracy (LAPACK cgebal).
zgebal(a[, scale, permute, overwrite_a])	Balance a general matrix to improve eigenvalue accuracy (LAPACK zgebal).
sgecon(a, anorm[, norm])	Estimate the reciprocal condition number of a factorized matrix (LAPACK sgecon).
dgecon(a, anorm[, norm])	Estimate the reciprocal condition number of a factorized matrix (LAPACK dgecon).
cgecon(a, anorm[, norm])	Estimate the reciprocal condition number of a factorized matrix (LAPACK cgecon).
zgecon(a, anorm[, norm])	Estimate the reciprocal condition number of a factorized matrix (LAPACK zgecon).
sgeequ(a)	Compute row and column scale factors that equilibrate a matrix (LAPACK sgeequ).
dgeequ(a)	Compute row and column scale factors that equilibrate a matrix (LAPACK dgeequ).
cgeequ(a)	Compute row and column scale factors that equilibrate a matrix (LAPACK cgeequ).
zgeequ(a)	Compute row and column scale factors that equilibrate a matrix (LAPACK zgeequ).
sgeequb(a)	Compute equilibration factors restricted to powers of the radix (LAPACK sgeequb).
dgeequb(a)	Compute equilibration factors restricted to powers of the radix (LAPACK dgeequb).
cgeequb(a)	Compute equilibration factors restricted to powers of the radix (LAPACK cgeequb).
zgeequb(a)	Compute equilibration factors restricted to powers of the radix (LAPACK zgeequb).
sgees(sselect, a[, compute_v, sort_t, ...])	Compute the Schur factorization of a general matrix (LAPACK sgees).
dgees(dselect, a[, compute_v, sort_t, ...])	Compute the Schur factorization of a general matrix (LAPACK dgees).
cgees(cselect, a[, compute_v, sort_t, ...])	Compute the Schur factorization of a general matrix (LAPACK cgees).
zgees(zselect, a[, compute_v, sort_t, ...])	Compute the Schur factorization of a general matrix (LAPACK zgees).
sgeev(a[, compute_vl, compute_vr, lwork, ...])	Compute the eigenvalues and eigenvectors of a general matrix (LAPACK sgeev).
dgeev(a[, compute_vl, compute_vr, lwork, ...])	Compute the eigenvalues and eigenvectors of a general matrix (LAPACK dgeev).
cgeev(a[, compute_vl, compute_vr, lwork, ...])	Compute the eigenvalues and eigenvectors of a general matrix (LAPACK cgeev).
zgeev(a[, compute_vl, compute_vr, lwork, ...])	Compute the eigenvalues and eigenvectors of a general matrix (LAPACK zgeev).
sgeev_lwork(n[, compute_vl, compute_vr])	Query the optimal lwork for sgeev.
dgeev_lwork(n[, compute_vl, compute_vr])	Query the optimal lwork for dgeev.
cgeev_lwork(n[, compute_vl, compute_vr])	Query the optimal lwork for cgeev.
zgeev_lwork(n[, compute_vl, compute_vr])	Query the optimal lwork for zgeev.
sgehrd(a[, lo, hi, lwork, overwrite_a])	Reduce a matrix to upper Hessenberg form (LAPACK sgehrd).
dgehrd(a[, lo, hi, lwork, overwrite_a])	Reduce a matrix to upper Hessenberg form (LAPACK dgehrd).
cgehrd(a[, lo, hi, lwork, overwrite_a])	Reduce a matrix to upper Hessenberg form (LAPACK cgehrd).
zgehrd(a[, lo, hi, lwork, overwrite_a])	Reduce a matrix to upper Hessenberg form (LAPACK zgehrd).
sgehrd_lwork(n[, lo, hi])	Query the optimal lwork for sgehrd.
dgehrd_lwork(n[, lo, hi])	Query the optimal lwork for dgehrd.
cgehrd_lwork(n[, lo, hi])	Query the optimal lwork for cgehrd.
zgehrd_lwork(n[, lo, hi])	Query the optimal lwork for zgehrd.
sgejsv(a[, joba, jobu, jobv, jobr, jobt, ...])	Singular value decomposition by the Jacobi method, for high relative accuracy (LAPACK sgejsv).
dgejsv(a[, joba, jobu, jobv, jobr, jobt, ...])	Singular value decomposition by the Jacobi method, for high relative accuracy (LAPACK dgejsv).
sgels(a, b[, trans, lwork, overwrite_a, ...])	Solve an overdetermined or underdetermined system by QR or LQ factorization (LAPACK sgels).
dgels(a, b[, trans, lwork, overwrite_a, ...])	Solve an overdetermined or underdetermined system by QR or LQ factorization (LAPACK dgels).
cgels(a, b[, trans, lwork, overwrite_a, ...])	Solve an overdetermined or underdetermined system by QR or LQ factorization (LAPACK cgels).
zgels(a, b[, trans, lwork, overwrite_a, ...])	Solve an overdetermined or underdetermined system by QR or LQ factorization (LAPACK zgels).
sgels_lwork(m, n, nrhs[, trans])	Query the optimal lwork for sgels.
dgels_lwork(m, n, nrhs[, trans])	Query the optimal lwork for dgels.
cgels_lwork(m, n, nrhs[, trans])	Query the optimal lwork for cgels.
zgels_lwork(m, n, nrhs[, trans])	Query the optimal lwork for zgels.
sgelsd(a, b, lwork, size_iwork[, cond, ...])	Solve a least-squares problem by divide-and-conquer SVD (LAPACK sgelsd).
dgelsd(a, b, lwork, size_iwork[, cond, ...])	Solve a least-squares problem by divide-and-conquer SVD (LAPACK dgelsd).
cgelsd(a, b, lwork, size_rwork, size_iwork)	Solve a least-squares problem by divide-and-conquer SVD (LAPACK cgelsd).
zgelsd(a, b, lwork, size_rwork, size_iwork)	Solve a least-squares problem by divide-and-conquer SVD (LAPACK zgelsd).
sgelsd_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for sgelsd.
dgelsd_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for dgelsd.
cgelsd_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for cgelsd.
zgelsd_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for zgelsd.
sgelss(a, b[, cond, lwork, overwrite_a, ...])	Solve a least-squares problem by singular value decomposition (LAPACK sgelss).
dgelss(a, b[, cond, lwork, overwrite_a, ...])	Solve a least-squares problem by singular value decomposition (LAPACK dgelss).
cgelss(a, b[, cond, lwork, overwrite_a, ...])	Solve a least-squares problem by singular value decomposition (LAPACK cgelss).
zgelss(a, b[, cond, lwork, overwrite_a, ...])	Solve a least-squares problem by singular value decomposition (LAPACK zgelss).
sgelss_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for sgelss.
dgelss_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for dgelss.
cgelss_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for cgelss.
zgelss_lwork(m, n, nrhs[, cond, lwork])	Query the optimal lwork for zgelss.
sgelsy(a, b, jptv, cond, lwork[, ...])	Solve a least-squares problem by complete orthogonal factorization (LAPACK sgelsy).
dgelsy(a, b, jptv, cond, lwork[, ...])	Solve a least-squares problem by complete orthogonal factorization (LAPACK dgelsy).
cgelsy(a, b, jptv, cond, lwork[, ...])	Solve a least-squares problem by complete orthogonal factorization (LAPACK cgelsy).
zgelsy(a, b, jptv, cond, lwork[, ...])	Solve a least-squares problem by complete orthogonal factorization (LAPACK zgelsy).
sgelsy_lwork(m, n, nrhs, cond[, lwork])	Query the optimal lwork for sgelsy.
dgelsy_lwork(m, n, nrhs, cond[, lwork])	Query the optimal lwork for dgelsy.
cgelsy_lwork(m, n, nrhs, cond[, lwork])	Query the optimal lwork for cgelsy.
zgelsy_lwork(m, n, nrhs, cond[, lwork])	Query the optimal lwork for zgelsy.
sgeqp3(a[, lwork, overwrite_a])	Compute a QR factorization with column pivoting (LAPACK sgeqp3).
dgeqp3(a[, lwork, overwrite_a])	Compute a QR factorization with column pivoting (LAPACK dgeqp3).
cgeqp3(a[, lwork, overwrite_a])	Compute a QR factorization with column pivoting (LAPACK cgeqp3).
zgeqp3(a[, lwork, overwrite_a])	Compute a QR factorization with column pivoting (LAPACK zgeqp3).
sgeqrf(a[, lwork, overwrite_a])	Compute a QR factorization (LAPACK sgeqrf).
dgeqrf(a[, lwork, overwrite_a])	Compute a QR factorization (LAPACK dgeqrf).
cgeqrf(a[, lwork, overwrite_a])	Compute a QR factorization (LAPACK cgeqrf).
zgeqrf(a[, lwork, overwrite_a])	Compute a QR factorization (LAPACK zgeqrf).
sgeqrf_lwork(m, n)	Query the optimal lwork for sgeqrf.
dgeqrf_lwork(m, n)	Query the optimal lwork for dgeqrf.
cgeqrf_lwork(m, n)	Query the optimal lwork for cgeqrf.
zgeqrf_lwork(m, n)	Query the optimal lwork for zgeqrf.
sgeqrfp(a[, lwork, overwrite_a])	Compute a QR factorization whose R has a nonnegative diagonal (LAPACK sgeqrfp).
dgeqrfp(a[, lwork, overwrite_a])	Compute a QR factorization whose R has a nonnegative diagonal (LAPACK dgeqrfp).
cgeqrfp(a[, lwork, overwrite_a])	Compute a QR factorization whose R has a nonnegative diagonal (LAPACK cgeqrfp).
zgeqrfp(a[, lwork, overwrite_a])	Compute a QR factorization whose R has a nonnegative diagonal (LAPACK zgeqrfp).
sgeqrfp_lwork(m, n)	Query the optimal lwork for sgeqrfp.
dgeqrfp_lwork(m, n)	Query the optimal lwork for dgeqrfp.
cgeqrfp_lwork(m, n)	Query the optimal lwork for cgeqrfp.
zgeqrfp_lwork(m, n)	Query the optimal lwork for zgeqrfp.
sgerqf(a[, lwork, overwrite_a])	Compute an RQ factorization a = r @ q (LAPACK sgerqf).
dgerqf(a[, lwork, overwrite_a])	Compute an RQ factorization a = r @ q (LAPACK dgerqf).
cgerqf(a[, lwork, overwrite_a])	Compute an RQ factorization a = r @ q (LAPACK cgerqf).
zgerqf(a[, lwork, overwrite_a])	Compute an RQ factorization a = r @ q (LAPACK zgerqf).
sgesdd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by divide-and-conquer (LAPACK sgesdd).
dgesdd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by divide-and-conquer (LAPACK dgesdd).
cgesdd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by divide-and-conquer (LAPACK cgesdd).
zgesdd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by divide-and-conquer (LAPACK zgesdd).
sgesdd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for sgesdd.
dgesdd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for dgesdd.
cgesdd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for cgesdd.
zgesdd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for zgesdd.
sgesv(a, b[, overwrite_a, overwrite_b])	Solve a @ x = b by LU factorization with partial pivoting (LAPACK sgesv).
dgesv(a, b[, overwrite_a, overwrite_b])	Solve a @ x = b by LU factorization with partial pivoting (LAPACK dgesv).
cgesv(a, b[, overwrite_a, overwrite_b])	Solve a @ x = b by LU factorization with partial pivoting (LAPACK cgesv).
zgesv(a, b[, overwrite_a, overwrite_b])	Solve a @ x = b by LU factorization with partial pivoting (LAPACK zgesv).
sgesvd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by QR iteration (LAPACK sgesvd).
dgesvd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by QR iteration (LAPACK dgesvd).
cgesvd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by QR iteration (LAPACK cgesvd).
zgesvd(a[, compute_uv, full_matrices, ...])	Compute the singular value decomposition a = u @ diag(s) @ vt by QR iteration (LAPACK zgesvd).
sgesvd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for sgesvd.
dgesvd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for dgesvd.
cgesvd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for cgesvd.
zgesvd_lwork(m, n[, compute_uv, full_matrices])	Query the optimal lwork for zgesvd.
sgesvx(a, b[, fact, trans, af, ipiv, equed, ...])	Solve a @ x = b with equilibration, condition estimation and error bounds (LAPACK sgesvx).
dgesvx(a, b[, fact, trans, af, ipiv, equed, ...])	Solve a @ x = b with equilibration, condition estimation and error bounds (LAPACK dgesvx).
cgesvx(a, b[, fact, trans, af, ipiv, equed, ...])	Solve a @ x = b with equilibration, condition estimation and error bounds (LAPACK cgesvx).
zgesvx(a, b[, fact, trans, af, ipiv, equed, ...])	Solve a @ x = b with equilibration, condition estimation and error bounds (LAPACK zgesvx).
sgetrf(a[, overwrite_a])	Compute the LU factorization a = p @ l @ u with partial pivoting (LAPACK sgetrf).
dgetrf(a[, overwrite_a])	Compute the LU factorization a = p @ l @ u with partial pivoting (LAPACK dgetrf).
cgetrf(a[, overwrite_a])	Compute the LU factorization a = p @ l @ u with partial pivoting (LAPACK cgetrf).
zgetrf(a[, overwrite_a])	Compute the LU factorization a = p @ l @ u with partial pivoting (LAPACK zgetrf).
sgetc2(a[, overwrite_a])	Compute the LU factorization with complete pivoting (LAPACK sgetc2).
dgetc2(a[, overwrite_a])	Compute the LU factorization with complete pivoting (LAPACK dgetc2).
cgetc2(a[, overwrite_a])	Compute the LU factorization with complete pivoting (LAPACK cgetc2).
zgetc2(a[, overwrite_a])	Compute the LU factorization with complete pivoting (LAPACK zgetc2).
sgetri(lu, piv[, lwork, overwrite_lu])	Invert a matrix from its getrf factorization (LAPACK sgetri).
dgetri(lu, piv[, lwork, overwrite_lu])	Invert a matrix from its getrf factorization (LAPACK dgetri).
cgetri(lu, piv[, lwork, overwrite_lu])	Invert a matrix from its getrf factorization (LAPACK cgetri).
zgetri(lu, piv[, lwork, overwrite_lu])	Invert a matrix from its getrf factorization (LAPACK zgetri).
sgetri_lwork(n)	Query the optimal lwork for sgetri.
dgetri_lwork(n)	Query the optimal lwork for dgetri.
cgetri_lwork(n)	Query the optimal lwork for cgetri.
zgetri_lwork(n)	Query the optimal lwork for zgetri.
sgetrs(lu, piv, b[, trans, overwrite_b])	Solve a system already factorized by getrf (LAPACK sgetrs).
dgetrs(lu, piv, b[, trans, overwrite_b])	Solve a system already factorized by getrf (LAPACK dgetrs).
cgetrs(lu, piv, b[, trans, overwrite_b])	Solve a system already factorized by getrf (LAPACK cgetrs).
zgetrs(lu, piv, b[, trans, overwrite_b])	Solve a system already factorized by getrf (LAPACK zgetrs).
sgesc2(lu, rhs, ipiv, jpiv[, overwrite_rhs])	Solve a system factorized by getc2, scaling to avoid overflow (LAPACK sgesc2).
dgesc2(lu, rhs, ipiv, jpiv[, overwrite_rhs])	Solve a system factorized by getc2, scaling to avoid overflow (LAPACK dgesc2).
cgesc2(lu, rhs, ipiv, jpiv[, overwrite_rhs])	Solve a system factorized by getc2, scaling to avoid overflow (LAPACK cgesc2).
zgesc2(lu, rhs, ipiv, jpiv[, overwrite_rhs])	Solve a system factorized by getc2, scaling to avoid overflow (LAPACK zgesc2).
sgges(sselect, a, b[, jobvsl, jobvsr, ...])	Compute the generalized Schur factorization of a matrix pair (LAPACK sgges).
dgges(dselect, a, b[, jobvsl, jobvsr, ...])	Compute the generalized Schur factorization of a matrix pair (LAPACK dgges).
cgges(cselect, a, b[, jobvsl, jobvsr, ...])	Compute the generalized Schur factorization of a matrix pair (LAPACK cgges).
zgges(zselect, a, b[, jobvsl, jobvsr, ...])	Compute the generalized Schur factorization of a matrix pair (LAPACK zgges).
sggev(a, b[, compute_vl, compute_vr, lwork, ...])	Solve the generalized eigenproblem a @ v = (alpha / beta) * b @ v (LAPACK sggev).
dggev(a, b[, compute_vl, compute_vr, lwork, ...])	Solve the generalized eigenproblem a @ v = (alpha / beta) * b @ v (LAPACK dggev).
cggev(a, b[, compute_vl, compute_vr, lwork, ...])	Solve the generalized eigenproblem a @ v = (alpha / beta) * b @ v (LAPACK cggev).
zggev(a, b[, compute_vl, compute_vr, lwork, ...])	Solve the generalized eigenproblem a @ v = (alpha / beta) * b @ v (LAPACK zggev).
sgglse(a, b, c, d[, lwork, overwrite_a, ...])	Solve an equality-constrained least squares problem (LAPACK sgglse).
dgglse(a, b, c, d[, lwork, overwrite_a, ...])	Solve an equality-constrained least squares problem (LAPACK dgglse).
cgglse(a, b, c, d[, lwork, overwrite_a, ...])	Solve an equality-constrained least squares problem (LAPACK cgglse).
zgglse(a, b, c, d[, lwork, overwrite_a, ...])	Solve an equality-constrained least squares problem (LAPACK zgglse).
sgglse_lwork(m, n, p)	Query the optimal workspace for sgglse (LAPACK sgglse with lwork = -1).
dgglse_lwork(m, n, p)	Query the optimal workspace for dgglse (LAPACK dgglse with lwork = -1).
cgglse_lwork(m, n, p)	Query the optimal workspace for cgglse (LAPACK cgglse with lwork = -1).
zgglse_lwork(m, n, p)	Query the optimal workspace for zgglse (LAPACK zgglse with lwork = -1).
sgtsv(dl, d, du, b[, overwrite_dl, ...])	Solve a @ x = b for a tridiagonal a by Gaussian elimination with partial pivoting (LAPACK sgtsv).
dgtsv(dl, d, du, b[, overwrite_dl, ...])	Solve a @ x = b for a tridiagonal a by Gaussian elimination with partial pivoting (LAPACK dgtsv).
cgtsv(dl, d, du, b[, overwrite_dl, ...])	Solve a @ x = b for a tridiagonal a by Gaussian elimination with partial pivoting (LAPACK cgtsv).
zgtsv(dl, d, du, b[, overwrite_dl, ...])	Solve a @ x = b for a tridiagonal a by Gaussian elimination with partial pivoting (LAPACK zgtsv).
sgtsvx(dl, d, du, b[, fact, trans, dlf, df, ...])	Solve a tridiagonal system with condition estimation and error bounds (LAPACK sgtsvx).
dgtsvx(dl, d, du, b[, fact, trans, dlf, df, ...])	Solve a tridiagonal system with condition estimation and error bounds (LAPACK dgtsvx).
cgtsvx(dl, d, du, b[, fact, trans, dlf, df, ...])	Solve a tridiagonal system with condition estimation and error bounds (LAPACK cgtsvx).
zgtsvx(dl, d, du, b[, fact, trans, dlf, df, ...])	Solve a tridiagonal system with condition estimation and error bounds (LAPACK zgtsvx).
chbevd(ab[, compute_v, lower, ldab, lrwork, ...])	Eigenvalues and eigenvectors of a Hermitian band matrix, by divide and conquer (LAPACK chbevd).
zhbevd(ab[, compute_v, lower, ldab, lrwork, ...])	Eigenvalues and eigenvectors of a Hermitian band matrix, by divide and conquer (LAPACK zhbevd).
chbevx(ab, vl, vu, il, iu[, ldab, ...])	Selected eigenvalues and eigenvectors of a Hermitian band matrix (LAPACK chbevx).
zhbevx(ab, vl, vu, il, iu[, ldab, ...])	Selected eigenvalues and eigenvectors of a Hermitian band matrix (LAPACK zhbevx).
checon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a Hermitian indefinite matrix from its Bunch-Kaufman factorization (LAPACK checon).
zhecon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a Hermitian indefinite matrix from its Bunch-Kaufman factorization (LAPACK zhecon).
cheequb(a[, lower])	Compute scale factors that equilibrate a Hermitian matrix (LAPACK cheequb).
zheequb(a[, lower])	Compute scale factors that equilibrate a Hermitian matrix (LAPACK zheequb).
cheev(a[, compute_v, lower, lwork, overwrite_a])	Compute the eigenvalues and optionally the eigenvectors of a Hermitian matrix (LAPACK cheev).
zheev(a[, compute_v, lower, lwork, overwrite_a])	Compute the eigenvalues and optionally the eigenvectors of a Hermitian matrix (LAPACK zheev).
cheev_lwork(n[, lower])	Query the optimal lwork for cheev.
zheev_lwork(n[, lower])	Query the optimal lwork for zheev.
cheevd(a[, compute_v, lower, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a Hermitian matrix by divide-and-conquer (LAPACK cheevd).
zheevd(a[, compute_v, lower, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a Hermitian matrix by divide-and-conquer (LAPACK zheevd).
cheevd_lwork(n[, compute_v, lower])	Query the optimal workspace sizes for cheevd.
zheevd_lwork(n[, compute_v, lower])	Query the optimal workspace sizes for zheevd.
cheevr(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a Hermitian matrix by the MRRR algorithm (LAPACK cheevr).
zheevr(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a Hermitian matrix by the MRRR algorithm (LAPACK zheevr).
cheevr_lwork(n[, lower])	Query the optimal workspace sizes for cheevr.
zheevr_lwork(n[, lower])	Query the optimal workspace sizes for zheevr.
cheevx(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a Hermitian matrix (LAPACK cheevx).
zheevx(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a Hermitian matrix (LAPACK zheevx).
cheevx_lwork(n[, lower])	Query the optimal lwork for cheevx.
zheevx_lwork(n[, lower])	Query the optimal lwork for zheevx.
chegst(a, b[, itype, lower, overwrite_a])	Reduce a generalized Hermitian-definite problem to standard form (LAPACK chegst).
zhegst(a, b[, itype, lower, overwrite_a])	Reduce a generalized Hermitian-definite problem to standard form (LAPACK zhegst).
chegv(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized Hermitian-definite eigenproblem (LAPACK chegv).
zhegv(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized Hermitian-definite eigenproblem (LAPACK zhegv).
chegv_lwork(n[, uplo])	Query the optimal lwork for chegv.
zhegv_lwork(n[, uplo])	Query the optimal lwork for zhegv.
chegvd(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized Hermitian-definite eigenproblem by divide-and-conquer (LAPACK chegvd).
zhegvd(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized Hermitian-definite eigenproblem by divide-and-conquer (LAPACK zhegvd).
chegvx(a, b[, itype, jobz, range, uplo, vl, ...])	Compute selected eigenvalues of the generalized Hermitian-definite problem (LAPACK chegvx).
zhegvx(a, b[, itype, jobz, range, uplo, vl, ...])	Compute selected eigenvalues of the generalized Hermitian-definite problem (LAPACK zhegvx).
chegvx_lwork(n[, uplo])	Query the optimal lwork for chegvx.
zhegvx_lwork(n[, uplo])	Query the optimal lwork for zhegvx.
chesv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a Hermitian indefinite a by Bunch-Kaufman factorization (LAPACK chesv).
zhesv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a Hermitian indefinite a by Bunch-Kaufman factorization (LAPACK zhesv).
chesv_lwork(n[, lower])	Query the optimal lwork for chesv.
zhesv_lwork(n[, lower])	Query the optimal lwork for zhesv.
chesvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a Hermitian indefinite system with condition estimation and error bounds (LAPACK chesvx).
zhesvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a Hermitian indefinite system with condition estimation and error bounds (LAPACK zhesvx).
chesvx_lwork(n[, lower])	Query the optimal lwork for chesvx.
zhesvx_lwork(n[, lower])	Query the optimal lwork for zhesvx.
chetrd(a[, lower, lwork, overwrite_a])	Reduce a Hermitian matrix to real symmetric tridiagonal form by a unitary similarity transformation (LAPACK chetrd).
zhetrd(a[, lower, lwork, overwrite_a])	Reduce a Hermitian matrix to real symmetric tridiagonal form by a unitary similarity transformation (LAPACK zhetrd).
chetrd_lwork(n[, lower])	Query the optimal lwork for chetrd.
zhetrd_lwork(n[, lower])	Query the optimal lwork for zhetrd.
chetrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a Hermitian matrix (LAPACK chetrf).
zhetrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a Hermitian matrix (LAPACK zhetrf).
chetrf_lwork(n[, lower])	Query the optimal lwork for chetrf.
zhetrf_lwork(n[, lower])	Query the optimal lwork for zhetrf.
chetri(a, ipiv[, lower, overwrite_a])	Invert a Hermitian indefinite matrix from the factorization computed by hetrf (LAPACK chetri).
zhetri(a, ipiv[, lower, overwrite_a])	Invert a Hermitian indefinite matrix from the factorization computed by hetrf (LAPACK zhetri).
chetrs(a, ipiv, b[, lower, overwrite_b])	Solve a Hermitian indefinite system using the factorization from hetrf (LAPACK chetrs).
zhetrs(a, ipiv, b[, lower, overwrite_b])	Solve a Hermitian indefinite system using the factorization from hetrf (LAPACK zhetrs).
chfrk(n, k, alpha, a, beta, c[, transr, ...])	Hermitian rank-k update of a matrix held in rectangular full packed (RFP) storage (LAPACK chfrk).
zhfrk(n, k, alpha, a, beta, c[, transr, ...])	Hermitian rank-k update of a matrix held in rectangular full packed (RFP) storage (LAPACK zhfrk).
slamch(cmach)	Machine parameters for this precision (LAPACK slamch).
dlamch(cmach)	Machine parameters for this precision (LAPACK dlamch).
slangb(norm, kl, ku, ab[, ldab])	Compute a norm of a banded matrix (LAPACK slangb).
dlangb(norm, kl, ku, ab[, ldab])	Compute a norm of a banded matrix (LAPACK dlangb).
clangb(norm, kl, ku, ab[, ldab])	Compute a norm of a banded matrix (LAPACK clangb).
zlangb(norm, kl, ku, ab[, ldab])	Compute a norm of a banded matrix (LAPACK zlangb).
slange(norm, a)	Norm of a general matrix (LAPACK slange).
dlange(norm, a)	Norm of a general matrix (LAPACK dlange).
clange(norm, a)	Norm of a general matrix (LAPACK clange).
zlange(norm, a)	Norm of a general matrix (LAPACK zlange).
slantr(norm, a[, uplo, diag])	Norm of a triangular or trapezoidal matrix (LAPACK slantr).
dlantr(norm, a[, uplo, diag])	Norm of a triangular or trapezoidal matrix (LAPACK dlantr).
clantr(norm, a[, uplo, diag])	Norm of a triangular or trapezoidal matrix (LAPACK clantr).
zlantr(norm, a[, uplo, diag])	Norm of a triangular or trapezoidal matrix (LAPACK zlantr).
slarf(v, tau, c, work[, side, incv, overwrite_c])	Apply an elementary reflector to a matrix (LAPACK slarf).
dlarf(v, tau, c, work[, side, incv, overwrite_c])	Apply an elementary reflector to a matrix (LAPACK dlarf).
clarf(v, tau, c, work[, side, incv, overwrite_c])	Apply an elementary reflector to a matrix (LAPACK clarf).
zlarf(v, tau, c, work[, side, incv, overwrite_c])	Apply an elementary reflector to a matrix (LAPACK zlarf).
slarfg(n, alpha, x[, incx, overwrite_x])	Generate an elementary reflector (LAPACK slarfg).
dlarfg(n, alpha, x[, incx, overwrite_x])	Generate an elementary reflector (LAPACK dlarfg).
clarfg(n, alpha, x[, incx, overwrite_x])	Generate an elementary reflector (LAPACK clarfg).
zlarfg(n, alpha, x[, incx, overwrite_x])	Generate an elementary reflector (LAPACK zlarfg).
slartg(f, g)	Generate a Givens rotation (LAPACK slartg).
dlartg(f, g)	Generate a Givens rotation (LAPACK dlartg).
clartg(f, g)	Generate a Givens rotation (LAPACK clartg).
zlartg(f, g)	Generate a Givens rotation (LAPACK zlartg).
slasd4(i, d, z[, rho])	Compute one root of the secular equation of a rank-one modified diagonal matrix (LAPACK slasd4).
dlasd4(i, d, z[, rho])	Compute one root of the secular equation of a rank-one modified diagonal matrix (LAPACK dlasd4).
slaswp(a, piv[, k1, k2, off, inc, overwrite_a])	Apply a sequence of row interchanges to a matrix (LAPACK slaswp).
dlaswp(a, piv[, k1, k2, off, inc, overwrite_a])	Apply a sequence of row interchanges to a matrix (LAPACK dlaswp).
claswp(a, piv[, k1, k2, off, inc, overwrite_a])	Apply a sequence of row interchanges to a matrix (LAPACK claswp).
zlaswp(a, piv[, k1, k2, off, inc, overwrite_a])	Apply a sequence of row interchanges to a matrix (LAPACK zlaswp).
slauum(c[, lower, overwrite_c])	Multiply a triangular matrix by its own adjoint (LAPACK slauum).
dlauum(c[, lower, overwrite_c])	Multiply a triangular matrix by its own adjoint (LAPACK dlauum).
clauum(c[, lower, overwrite_c])	Multiply a triangular matrix by its own adjoint (LAPACK clauum).
zlauum(c[, lower, overwrite_c])	Multiply a triangular matrix by its own adjoint (LAPACK zlauum).
sorcsd(x11, x12, x21, x22[, compute_u1, ...])	CS decomposition of a partitioned orthogonal matrix (LAPACK sorcsd).
dorcsd(x11, x12, x21, x22[, compute_u1, ...])	CS decomposition of a partitioned orthogonal matrix (LAPACK dorcsd).
sorcsd_lwork(m, p, q)	Query the workspace sorcsd needs (LAPACK sorcsd with lwork = -1).
dorcsd_lwork(m, p, q)	Query the workspace dorcsd needs (LAPACK dorcsd with lwork = -1).
sorghr(a, tau[, lo, hi, lwork, overwrite_a])	Generate the orthogonal/unitary matrix of a Hessenberg reduction (LAPACK sorghr).
dorghr(a, tau[, lo, hi, lwork, overwrite_a])	Generate the orthogonal/unitary matrix of a Hessenberg reduction (LAPACK dorghr).
sorghr_lwork(n[, lo, hi])	Query the optimal workspace for sorghr (LAPACK sorghr with lwork = -1).
dorghr_lwork(n[, lo, hi])	Query the optimal workspace for dorghr (LAPACK dorghr with lwork = -1).
sorgqr(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a QR factorization (LAPACK sorgqr).
dorgqr(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a QR factorization (LAPACK dorgqr).
sorgrq(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a RQ factorization (LAPACK sorgrq).
dorgrq(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a RQ factorization (LAPACK dorgrq).
sormqr(side, trans, a, tau, c, lwork[, ...])	Multiply a matrix by the Q of a QR factorization (LAPACK sormqr).
dormqr(side, trans, a, tau, c, lwork[, ...])	Multiply a matrix by the Q of a QR factorization (LAPACK dormqr).
sormrz(a, tau, c[, side, trans, lwork, ...])	Multiply a matrix by the Q of an RZ factorization (LAPACK sormrz).
dormrz(a, tau, c[, side, trans, lwork, ...])	Multiply a matrix by the Q of an RZ factorization (LAPACK dormrz).
sormrz_lwork(m, n[, side, trans])	Query the optimal workspace for sormrz (LAPACK sormrz with lwork = -1).
dormrz_lwork(m, n[, side, trans])	Query the optimal workspace for dormrz (LAPACK dormrz with lwork = -1).
spbsv(ab, b[, lower, ldab, overwrite_ab, ...])	Solve a positive definite band system by Cholesky factorization (LAPACK spbsv).
dpbsv(ab, b[, lower, ldab, overwrite_ab, ...])	Solve a positive definite band system by Cholesky factorization (LAPACK dpbsv).
cpbsv(ab, b[, lower, ldab, overwrite_ab, ...])	Solve a positive definite band system by Cholesky factorization (LAPACK cpbsv).
zpbsv(ab, b[, lower, ldab, overwrite_ab, ...])	Solve a positive definite band system by Cholesky factorization (LAPACK zpbsv).
spbtrf(ab[, lower, ldab, overwrite_ab])	Compute the Cholesky factorization of a positive definite band matrix (LAPACK spbtrf).
dpbtrf(ab[, lower, ldab, overwrite_ab])	Compute the Cholesky factorization of a positive definite band matrix (LAPACK dpbtrf).
cpbtrf(ab[, lower, ldab, overwrite_ab])	Compute the Cholesky factorization of a positive definite band matrix (LAPACK cpbtrf).
zpbtrf(ab[, lower, ldab, overwrite_ab])	Compute the Cholesky factorization of a positive definite band matrix (LAPACK zpbtrf).
spbtrs(ab, b[, lower, ldab, overwrite_b])	Solve a positive definite band system using a Cholesky factorization already computed by pbtrf (LAPACK spbtrs).
dpbtrs(ab, b[, lower, ldab, overwrite_b])	Solve a positive definite band system using a Cholesky factorization already computed by pbtrf (LAPACK dpbtrs).
cpbtrs(ab, b[, lower, ldab, overwrite_b])	Solve a positive definite band system using a Cholesky factorization already computed by pbtrf (LAPACK cpbtrs).
zpbtrs(ab, b[, lower, ldab, overwrite_b])	Solve a positive definite band system using a Cholesky factorization already computed by pbtrf (LAPACK zpbtrs).
spftrf(n, a[, transr, uplo, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix in rectangular full packed (RFP) storage (LAPACK spftrf).
dpftrf(n, a[, transr, uplo, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix in rectangular full packed (RFP) storage (LAPACK dpftrf).
cpftrf(n, a[, transr, uplo, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix in rectangular full packed (RFP) storage (LAPACK cpftrf).
zpftrf(n, a[, transr, uplo, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix in rectangular full packed (RFP) storage (LAPACK zpftrf).
spftri(n, a[, transr, uplo, overwrite_a])	Invert a positive definite matrix in rectangular full packed (RFP) storage from its Cholesky factorization (LAPACK spftri).
dpftri(n, a[, transr, uplo, overwrite_a])	Invert a positive definite matrix in rectangular full packed (RFP) storage from its Cholesky factorization (LAPACK dpftri).
cpftri(n, a[, transr, uplo, overwrite_a])	Invert a positive definite matrix in rectangular full packed (RFP) storage from its Cholesky factorization (LAPACK cpftri).
zpftri(n, a[, transr, uplo, overwrite_a])	Invert a positive definite matrix in rectangular full packed (RFP) storage from its Cholesky factorization (LAPACK zpftri).
spftrs(n, a, b[, transr, uplo, overwrite_b])	Solve a positive definite system in rectangular full packed (RFP) storage using a Cholesky factorization already computed by pftrf (LAPACK spftrs).
dpftrs(n, a, b[, transr, uplo, overwrite_b])	Solve a positive definite system in rectangular full packed (RFP) storage using a Cholesky factorization already computed by pftrf (LAPACK dpftrs).
cpftrs(n, a, b[, transr, uplo, overwrite_b])	Solve a positive definite system in rectangular full packed (RFP) storage using a Cholesky factorization already computed by pftrf (LAPACK cpftrs).
zpftrs(n, a, b[, transr, uplo, overwrite_b])	Solve a positive definite system in rectangular full packed (RFP) storage using a Cholesky factorization already computed by pftrf (LAPACK zpftrs).
spocon(a, anorm[, uplo])	Estimate the reciprocal condition number of a positive definite matrix from its Cholesky factorization (LAPACK spocon).
dpocon(a, anorm[, uplo])	Estimate the reciprocal condition number of a positive definite matrix from its Cholesky factorization (LAPACK dpocon).
cpocon(a, anorm[, uplo])	Estimate the reciprocal condition number of a positive definite matrix from its Cholesky factorization (LAPACK cpocon).
zpocon(a, anorm[, uplo])	Estimate the reciprocal condition number of a positive definite matrix from its Cholesky factorization (LAPACK zpocon).
spstrf(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, blocked (LAPACK spstrf).
dpstrf(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, blocked (LAPACK dpstrf).
cpstrf(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, blocked (LAPACK cpstrf).
zpstrf(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, blocked (LAPACK zpstrf).
spstf2(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, unblocked (LAPACK spstf2).
dpstf2(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, unblocked (LAPACK dpstf2).
cpstf2(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, unblocked (LAPACK cpstf2).
zpstf2(a[, tol, lower, overwrite_a])	Compute the pivoted Cholesky factorization of a positive semidefinite matrix, unblocked (LAPACK zpstf2).
sposv(a, b[, lower, overwrite_a, overwrite_b])	Solve a @ x = b for a positive definite a by Cholesky factorization (LAPACK sposv).
dposv(a, b[, lower, overwrite_a, overwrite_b])	Solve a @ x = b for a positive definite a by Cholesky factorization (LAPACK dposv).
cposv(a, b[, lower, overwrite_a, overwrite_b])	Solve a @ x = b for a positive definite a by Cholesky factorization (LAPACK cposv).
zposv(a, b[, lower, overwrite_a, overwrite_b])	Solve a @ x = b for a positive definite a by Cholesky factorization (LAPACK zposv).
sposvx(a, b[, fact, af, equed, s, lower, ...])	Solve a @ x = b for a positive definite a with equilibration, condition estimation and error bounds (LAPACK sposvx).
dposvx(a, b[, fact, af, equed, s, lower, ...])	Solve a @ x = b for a positive definite a with equilibration, condition estimation and error bounds (LAPACK dposvx).
cposvx(a, b[, fact, af, equed, s, lower, ...])	Solve a @ x = b for a positive definite a with equilibration, condition estimation and error bounds (LAPACK cposvx).
zposvx(a, b[, fact, af, equed, s, lower, ...])	Solve a @ x = b for a positive definite a with equilibration, condition estimation and error bounds (LAPACK zposvx).
spotrf(a[, lower, clean, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix (LAPACK spotrf).
dpotrf(a[, lower, clean, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix (LAPACK dpotrf).
cpotrf(a[, lower, clean, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix (LAPACK cpotrf).
zpotrf(a[, lower, clean, overwrite_a])	Compute the Cholesky factorization of a positive definite matrix (LAPACK zpotrf).
spotri(c[, lower, overwrite_c])	Invert a positive definite matrix from the factorization computed by potrf (LAPACK spotri).
dpotri(c[, lower, overwrite_c])	Invert a positive definite matrix from the factorization computed by potrf (LAPACK dpotri).
cpotri(c[, lower, overwrite_c])	Invert a positive definite matrix from the factorization computed by potrf (LAPACK cpotri).
zpotri(c[, lower, overwrite_c])	Invert a positive definite matrix from the factorization computed by potrf (LAPACK zpotri).
spotrs(c, b[, lower, overwrite_b])	Solve a positive definite system using the factorization from potrf (LAPACK spotrs).
dpotrs(c, b[, lower, overwrite_b])	Solve a positive definite system using the factorization from potrf (LAPACK dpotrs).
cpotrs(c, b[, lower, overwrite_b])	Solve a positive definite system using the factorization from potrf (LAPACK cpotrs).
zpotrs(c, b[, lower, overwrite_b])	Solve a positive definite system using the factorization from potrf (LAPACK zpotrs).
sppcon(n, ap, anorm[, lower])	Estimate the reciprocal condition number of a positive definite matrix in packed storage, from its Cholesky factorization (LAPACK sppcon).
dppcon(n, ap, anorm[, lower])	Estimate the reciprocal condition number of a positive definite matrix in packed storage, from its Cholesky factorization (LAPACK dppcon).
cppcon(n, ap, anorm[, lower])	Estimate the reciprocal condition number of a positive definite matrix in packed storage, from its Cholesky factorization (LAPACK cppcon).
zppcon(n, ap, anorm[, lower])	Estimate the reciprocal condition number of a positive definite matrix in packed storage, from its Cholesky factorization (LAPACK zppcon).
sppsv(n, ap, b[, lower, overwrite_b])	Solve a positive definite system whose matrix is in packed storage, by Cholesky factorization (LAPACK sppsv).
dppsv(n, ap, b[, lower, overwrite_b])	Solve a positive definite system whose matrix is in packed storage, by Cholesky factorization (LAPACK dppsv).
cppsv(n, ap, b[, lower, overwrite_b])	Solve a positive definite system whose matrix is in packed storage, by Cholesky factorization (LAPACK cppsv).
zppsv(n, ap, b[, lower, overwrite_b])	Solve a positive definite system whose matrix is in packed storage, by Cholesky factorization (LAPACK zppsv).
spptrf(n, ap[, lower, overwrite_ap])	Compute the Cholesky factorization of a positive definite matrix in packed storage (LAPACK spptrf).
dpptrf(n, ap[, lower, overwrite_ap])	Compute the Cholesky factorization of a positive definite matrix in packed storage (LAPACK dpptrf).
cpptrf(n, ap[, lower, overwrite_ap])	Compute the Cholesky factorization of a positive definite matrix in packed storage (LAPACK cpptrf).
zpptrf(n, ap[, lower, overwrite_ap])	Compute the Cholesky factorization of a positive definite matrix in packed storage (LAPACK zpptrf).
spptri(n, ap[, lower, overwrite_ap])	Invert a positive definite matrix in packed storage from its Cholesky factorization (LAPACK spptri).
dpptri(n, ap[, lower, overwrite_ap])	Invert a positive definite matrix in packed storage from its Cholesky factorization (LAPACK dpptri).
cpptri(n, ap[, lower, overwrite_ap])	Invert a positive definite matrix in packed storage from its Cholesky factorization (LAPACK cpptri).
zpptri(n, ap[, lower, overwrite_ap])	Invert a positive definite matrix in packed storage from its Cholesky factorization (LAPACK zpptri).
spptrs(n, ap, b[, lower, overwrite_b])	Solve a positive definite system in packed storage using a Cholesky factorization already computed by pptrf (LAPACK spptrs).
dpptrs(n, ap, b[, lower, overwrite_b])	Solve a positive definite system in packed storage using a Cholesky factorization already computed by pptrf (LAPACK dpptrs).
cpptrs(n, ap, b[, lower, overwrite_b])	Solve a positive definite system in packed storage using a Cholesky factorization already computed by pptrf (LAPACK cpptrs).
zpptrs(n, ap, b[, lower, overwrite_b])	Solve a positive definite system in packed storage using a Cholesky factorization already computed by pptrf (LAPACK zpptrs).
sptsv(d, e, b[, overwrite_d, overwrite_e, ...])	Solve a @ x = b for a positive definite tridiagonal a by its L @ D @ L.conj().T factorization (LAPACK sptsv).
dptsv(d, e, b[, overwrite_d, overwrite_e, ...])	Solve a @ x = b for a positive definite tridiagonal a by its L @ D @ L.conj().T factorization (LAPACK dptsv).
cptsv(d, e, b[, overwrite_d, overwrite_e, ...])	Solve a @ x = b for a positive definite tridiagonal a by its L @ D @ L.conj().T factorization (LAPACK cptsv).
zptsv(d, e, b[, overwrite_d, overwrite_e, ...])	Solve a @ x = b for a positive definite tridiagonal a by its L @ D @ L.conj().T factorization (LAPACK zptsv).
sptsvx(d, e, b[, fact, df, ef])	Solve a positive definite tridiagonal system with condition estimation and error bounds (LAPACK sptsvx).
dptsvx(d, e, b[, fact, df, ef])	Solve a positive definite tridiagonal system with condition estimation and error bounds (LAPACK dptsvx).
cptsvx(d, e, b[, fact, df, ef])	Solve a positive definite tridiagonal system with condition estimation and error bounds (LAPACK cptsvx).
zptsvx(d, e, b[, fact, df, ef])	Solve a positive definite tridiagonal system with condition estimation and error bounds (LAPACK zptsvx).
spttrf(d, e[, overwrite_d, overwrite_e])	Compute the L @ D @ L.conj().T factorization of a positive definite tridiagonal matrix (LAPACK spttrf).
dpttrf(d, e[, overwrite_d, overwrite_e])	Compute the L @ D @ L.conj().T factorization of a positive definite tridiagonal matrix (LAPACK dpttrf).
cpttrf(d, e[, overwrite_d, overwrite_e])	Compute the L @ D @ L.conj().T factorization of a positive definite tridiagonal matrix (LAPACK cpttrf).
zpttrf(d, e[, overwrite_d, overwrite_e])	Compute the L @ D @ L.conj().T factorization of a positive definite tridiagonal matrix (LAPACK zpttrf).
spttrs(d, e, b[, overwrite_b])	Solve a positive definite tridiagonal system using the factorization from pttrf (LAPACK spttrs).
dpttrs(d, e, b[, overwrite_b])	Solve a positive definite tridiagonal system using the factorization from pttrf (LAPACK dpttrs).
cpttrs(d, e, b[, lower, overwrite_b])	Solve a positive definite tridiagonal system using the factorization from pttrf (LAPACK cpttrs).
zpttrs(d, e, b[, lower, overwrite_b])	Solve a positive definite tridiagonal system using the factorization from pttrf (LAPACK zpttrs).
spteqr(d, e, z[, compute_z, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a positive definite tridiagonal matrix, to high relative accuracy (LAPACK spteqr).
dpteqr(d, e, z[, compute_z, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a positive definite tridiagonal matrix, to high relative accuracy (LAPACK dpteqr).
cpteqr(d, e, z[, compute_z, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a positive definite tridiagonal matrix, to high relative accuracy (LAPACK cpteqr).
zpteqr(d, e, z[, compute_z, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a positive definite tridiagonal matrix, to high relative accuracy (LAPACK zpteqr).
crot(x, y, c, s[, n, offx, incx, offy, ...])	Apply a plane rotation to a pair of complex vectors (LAPACK crot).
zrot(x, y, c, s[, n, offx, incx, offy, ...])	Apply a plane rotation to a pair of complex vectors (LAPACK zrot).
ssbev(ab[, compute_v, lower, ldab, overwrite_ab])	Eigenvalues and eigenvectors of a symmetric band matrix (LAPACK ssbev).
dsbev(ab[, compute_v, lower, ldab, overwrite_ab])	Eigenvalues and eigenvectors of a symmetric band matrix (LAPACK dsbev).
ssbevd(ab[, compute_v, lower, ldab, liwork, ...])	Eigenvalues and eigenvectors of a symmetric band matrix, by divide and conquer (LAPACK ssbevd).
dsbevd(ab[, compute_v, lower, ldab, liwork, ...])	Eigenvalues and eigenvectors of a symmetric band matrix, by divide and conquer (LAPACK dsbevd).
ssbevx(ab, vl, vu, il, iu[, ldab, ...])	Selected eigenvalues and eigenvectors of a symmetric band matrix (LAPACK ssbevx).
dsbevx(ab, vl, vu, il, iu[, ldab, ...])	Selected eigenvalues and eigenvectors of a symmetric band matrix (LAPACK dsbevx).
ssfrk(n, k, alpha, a, beta, c[, transr, ...])	Symmetric rank-k update of a matrix held in rectangular full packed (RFP) storage (LAPACK ssfrk).
dsfrk(n, k, alpha, a, beta, c[, transr, ...])	Symmetric rank-k update of a matrix held in rectangular full packed (RFP) storage (LAPACK dsfrk).
sstebz(d, e, range, vl, vu, il, iu, tol, order)	Compute selected eigenvalues of a symmetric tridiagonal matrix by bisection (LAPACK sstebz).
dstebz(d, e, range, vl, vu, il, iu, tol, order)	Compute selected eigenvalues of a symmetric tridiagonal matrix by bisection (LAPACK dstebz).
sstein(d, e, w, iblock, isplit)	Compute eigenvectors of a symmetric tridiagonal matrix by inverse iteration, for eigenvalues supplied by stebz (LAPACK sstein).
dstein(d, e, w, iblock, isplit)	Compute eigenvectors of a symmetric tridiagonal matrix by inverse iteration, for eigenvalues supplied by stebz (LAPACK dstein).
sstemr(d, e, range, vl, vu, il, iu[, ...])	Compute selected eigenvalues and optionally eigenvectors of a symmetric tridiagonal matrix by the MRRR algorithm (LAPACK sstemr).
dstemr(d, e, range, vl, vu, il, iu[, ...])	Compute selected eigenvalues and optionally eigenvectors of a symmetric tridiagonal matrix by the MRRR algorithm (LAPACK dstemr).
sstemr_lwork(d, e, range, vl, vu, il, iu[, ...])	Query the optimal lwork and liwork for sstemr.
dstemr_lwork(d, e, range, vl, vu, il, iu[, ...])	Query the optimal lwork and liwork for dstemr.
ssterf(d, e[, overwrite_d, overwrite_e])	Compute all eigenvalues of a symmetric tridiagonal matrix by the Pal-Walker- Kahan QL/QR variant, without eigenvectors (LAPACK ssterf).
dsterf(d, e[, overwrite_d, overwrite_e])	Compute all eigenvalues of a symmetric tridiagonal matrix by the Pal-Walker- Kahan QL/QR variant, without eigenvectors (LAPACK dsterf).
sstev(d, e[, compute_v, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a symmetric tridiagonal matrix (LAPACK sstev).
dstev(d, e[, compute_v, overwrite_d, ...])	Compute the eigenvalues and optionally the eigenvectors of a symmetric tridiagonal matrix (LAPACK dstev).
sstevd(d, e[, compute_v, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a symmetric tridiagonal matrix by divide-and-conquer (LAPACK sstevd).
dstevd(d, e[, compute_v, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a symmetric tridiagonal matrix by divide-and-conquer (LAPACK dstevd).
ssycon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a symmetric indefinite matrix from its Bunch-Kaufman factorization (LAPACK ssycon).
dsycon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a symmetric indefinite matrix from its Bunch-Kaufman factorization (LAPACK dsycon).
csycon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a symmetric indefinite matrix from its Bunch-Kaufman factorization (LAPACK csycon).
zsycon(a, ipiv, anorm[, lower])	Estimate the reciprocal condition number of a symmetric indefinite matrix from its Bunch-Kaufman factorization (LAPACK zsycon).
ssyconv(a, ipiv[, lower, way, overwrite_a])	Convert the Bunch-Kaufman factorization between its packed and its expanded storage (LAPACK ssyconv).
dsyconv(a, ipiv[, lower, way, overwrite_a])	Convert the Bunch-Kaufman factorization between its packed and its expanded storage (LAPACK dsyconv).
csyconv(a, ipiv[, lower, way, overwrite_a])	Convert the Bunch-Kaufman factorization between its packed and its expanded storage (LAPACK csyconv).
zsyconv(a, ipiv[, lower, way, overwrite_a])	Convert the Bunch-Kaufman factorization between its packed and its expanded storage (LAPACK zsyconv).
ssyequb(a[, lower])	Compute scale factors that equilibrate a symmetric matrix (LAPACK ssyequb).
dsyequb(a[, lower])	Compute scale factors that equilibrate a symmetric matrix (LAPACK dsyequb).
csyequb(a[, lower])	Compute scale factors that equilibrate a symmetric matrix (LAPACK csyequb).
zsyequb(a[, lower])	Compute scale factors that equilibrate a symmetric matrix (LAPACK zsyequb).
ssyev(a[, compute_v, lower, lwork, overwrite_a])	Compute the eigenvalues and optionally the eigenvectors of a real symmetric matrix (LAPACK ssyev).
dsyev(a[, compute_v, lower, lwork, overwrite_a])	Compute the eigenvalues and optionally the eigenvectors of a real symmetric matrix (LAPACK dsyev).
ssyev_lwork(n[, lower])	Query the optimal lwork for ssyev.
dsyev_lwork(n[, lower])	Query the optimal lwork for dsyev.
ssyevd(a[, compute_v, lower, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a real symmetric matrix by divide-and-conquer (LAPACK ssyevd).
dsyevd(a[, compute_v, lower, lwork, liwork, ...])	Compute the eigenvalues and optionally the eigenvectors of a real symmetric matrix by divide-and-conquer (LAPACK dsyevd).
ssyevd_lwork(n[, compute_v, lower])	Query the optimal workspace sizes for ssyevd.
dsyevd_lwork(n[, compute_v, lower])	Query the optimal workspace sizes for dsyevd.
ssyevr(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a real symmetric matrix by the MRRR algorithm (LAPACK ssyevr).
dsyevr(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a real symmetric matrix by the MRRR algorithm (LAPACK dsyevr).
ssyevr_lwork(n[, lower])	Query the optimal workspace sizes for ssyevr.
dsyevr_lwork(n[, lower])	Query the optimal workspace sizes for dsyevr.
ssyevx(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a real symmetric matrix (LAPACK ssyevx).
dsyevx(a[, compute_v, range, lower, vl, vu, ...])	Compute selected eigenvalues and optionally eigenvectors of a real symmetric matrix (LAPACK dsyevx).
ssyevx_lwork(n[, lower])	Query the optimal lwork for ssyevx.
dsyevx_lwork(n[, lower])	Query the optimal lwork for dsyevx.
ssygst(a, b[, itype, lower, overwrite_a])	Reduce a generalized symmetric-definite problem to standard form (LAPACK ssygst).
dsygst(a, b[, itype, lower, overwrite_a])	Reduce a generalized symmetric-definite problem to standard form (LAPACK dsygst).
ssygv(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized symmetric-definite eigenproblem (LAPACK ssygv).
dsygv(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized symmetric-definite eigenproblem (LAPACK dsygv).
ssygv_lwork(n[, uplo])	Query the optimal lwork for ssygv.
dsygv_lwork(n[, uplo])	Query the optimal lwork for dsygv.
ssygvd(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized symmetric-definite eigenproblem by divide-and-conquer (LAPACK ssygvd).
dsygvd(a, b[, itype, jobz, uplo, lwork, ...])	Solve the generalized symmetric-definite eigenproblem by divide-and-conquer (LAPACK dsygvd).
ssygvx(a, b[, itype, jobz, range, uplo, vl, ...])	Compute selected eigenvalues of the generalized symmetric-definite problem (LAPACK ssygvx).
dsygvx(a, b[, itype, jobz, range, uplo, vl, ...])	Compute selected eigenvalues of the generalized symmetric-definite problem (LAPACK dsygvx).
ssygvx_lwork(n[, uplo])	Query the optimal lwork for ssygvx.
dsygvx_lwork(n[, uplo])	Query the optimal lwork for dsygvx.
ssysv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a symmetric indefinite a by Bunch-Kaufman factorization (LAPACK ssysv).
dsysv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a symmetric indefinite a by Bunch-Kaufman factorization (LAPACK dsysv).
csysv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a symmetric indefinite a by Bunch-Kaufman factorization (LAPACK csysv).
zsysv(a, b[, lwork, lower, overwrite_a, ...])	Solve a @ x = b for a symmetric indefinite a by Bunch-Kaufman factorization (LAPACK zsysv).
ssysv_lwork(n[, lower])	Query the optimal lwork for ssysv.
dsysv_lwork(n[, lower])	Query the optimal lwork for dsysv.
csysv_lwork(n[, lower])	Query the optimal lwork for csysv.
zsysv_lwork(n[, lower])	Query the optimal lwork for zsysv.
ssysvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a symmetric indefinite system with condition estimation and error bounds (LAPACK ssysvx).
dsysvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a symmetric indefinite system with condition estimation and error bounds (LAPACK dsysvx).
csysvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a symmetric indefinite system with condition estimation and error bounds (LAPACK csysvx).
zsysvx(a, b[, af, ipiv, lwork, factored, ...])	Solve a symmetric indefinite system with condition estimation and error bounds (LAPACK zsysvx).
ssysvx_lwork(n[, lower])	Query the optimal lwork for ssysvx.
dsysvx_lwork(n[, lower])	Query the optimal lwork for dsysvx.
csysvx_lwork(n[, lower])	Query the optimal lwork for csysvx.
zsysvx_lwork(n[, lower])	Query the optimal lwork for zsysvx.
ssytf2(a[, lower, overwrite_a])	Compute the unblocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK ssytf2).
dsytf2(a[, lower, overwrite_a])	Compute the unblocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK dsytf2).
csytf2(a[, lower, overwrite_a])	Compute the unblocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK csytf2).
zsytf2(a[, lower, overwrite_a])	Compute the unblocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK zsytf2).
ssytrd(a[, lower, lwork, overwrite_a])	Reduce a real symmetric matrix to real symmetric tridiagonal form by an orthogonal similarity transformation (LAPACK ssytrd).
dsytrd(a[, lower, lwork, overwrite_a])	Reduce a real symmetric matrix to real symmetric tridiagonal form by an orthogonal similarity transformation (LAPACK dsytrd).
ssytrd_lwork(n[, lower])	Query the optimal lwork for ssytrd.
dsytrd_lwork(n[, lower])	Query the optimal lwork for dsytrd.
ssytrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK ssytrf).
dsytrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK dsytrf).
csytrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK csytrf).
zsytrf(a[, lower, lwork, overwrite_a])	Compute the blocked Bunch-Kaufman factorization of a symmetric matrix (LAPACK zsytrf).
ssytrf_lwork(n[, lower])	Query the optimal lwork for ssytrf.
dsytrf_lwork(n[, lower])	Query the optimal lwork for dsytrf.
csytrf_lwork(n[, lower])	Query the optimal lwork for csytrf.
zsytrf_lwork(n[, lower])	Query the optimal lwork for zsytrf.
ssytri(a, ipiv[, lower, overwrite_a])	Invert a symmetric indefinite matrix from the factorization computed by sytrf (LAPACK ssytri).
dsytri(a, ipiv[, lower, overwrite_a])	Invert a symmetric indefinite matrix from the factorization computed by sytrf (LAPACK dsytri).
csytri(a, ipiv[, lower, overwrite_a])	Invert a symmetric indefinite matrix from the factorization computed by sytrf (LAPACK csytri).
zsytri(a, ipiv[, lower, overwrite_a])	Invert a symmetric indefinite matrix from the factorization computed by sytrf (LAPACK zsytri).
ssytrs(a, ipiv, b[, lower, overwrite_b])	Solve a symmetric indefinite system using the factorization from sytrf (LAPACK ssytrs).
dsytrs(a, ipiv, b[, lower, overwrite_b])	Solve a symmetric indefinite system using the factorization from sytrf (LAPACK dsytrs).
csytrs(a, ipiv, b[, lower, overwrite_b])	Solve a symmetric indefinite system using the factorization from sytrf (LAPACK csytrs).
zsytrs(a, ipiv, b[, lower, overwrite_b])	Solve a symmetric indefinite system using the factorization from sytrf (LAPACK zsytrs).
stbtrs(ab, b[, uplo, trans, diag, overwrite_b])	Solve a triangular band system (LAPACK stbtrs).
dtbtrs(ab, b[, uplo, trans, diag, overwrite_b])	Solve a triangular band system (LAPACK dtbtrs).
ctbtrs(ab, b[, uplo, trans, diag, overwrite_b])	Solve a triangular band system (LAPACK ctbtrs).
ztbtrs(ab, b[, uplo, trans, diag, overwrite_b])	Solve a triangular band system (LAPACK ztbtrs).
stfsm(alpha, a, b[, transr, side, uplo, ...])	Solve a triangular system whose matrix is in rectangular full packed (RFP) storage (LAPACK stfsm).
dtfsm(alpha, a, b[, transr, side, uplo, ...])	Solve a triangular system whose matrix is in rectangular full packed (RFP) storage (LAPACK dtfsm).
ctfsm(alpha, a, b[, transr, side, uplo, ...])	Solve a triangular system whose matrix is in rectangular full packed (RFP) storage (LAPACK ctfsm).
ztfsm(alpha, a, b[, transr, side, uplo, ...])	Solve a triangular system whose matrix is in rectangular full packed (RFP) storage (LAPACK ztfsm).
stfttp(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to packed (TP) storage (LAPACK stfttp).
dtfttp(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to packed (TP) storage (LAPACK dtfttp).
ctfttp(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to packed (TP) storage (LAPACK ctfttp).
ztfttp(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to packed (TP) storage (LAPACK ztfttp).
stfttr(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to full (TR) storage (LAPACK stfttr).
dtfttr(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to full (TR) storage (LAPACK dtfttr).
ctfttr(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to full (TR) storage (LAPACK ctfttr).
ztfttr(n, arf[, transr, uplo])	Convert a triangle from rectangular full packed (RFP) to full (TR) storage (LAPACK ztfttr).
stgexc(a, b, q, z, ifst, ilst[, wantq, ...])	Reorder a generalized Schur decomposition by moving one diagonal block (LAPACK stgexc).
dtgexc(a, b, q, z, ifst, ilst[, wantq, ...])	Reorder a generalized Schur decomposition by moving one diagonal block (LAPACK dtgexc).
ctgexc(a, b, q, z, ifst, ilst[, wantq, ...])	Reorder a generalized Schur decomposition by moving one diagonal block (LAPACK ctgexc).
ztgexc(a, b, q, z, ifst, ilst[, wantq, ...])	Reorder a generalized Schur decomposition by moving one diagonal block (LAPACK ztgexc).
stgsen(select, a, b, q, z[, ijob, wantq, ...])	Reorder a generalized Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK stgsen).
dtgsen(select, a, b, q, z[, ijob, wantq, ...])	Reorder a generalized Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK dtgsen).
ctgsen(select, a, b, q, z[, ijob, wantq, ...])	Reorder a generalized Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK ctgsen).
ztgsen(select, a, b, q, z[, ijob, wantq, ...])	Reorder a generalized Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK ztgsen).
stgsen_lwork(select, a[, ijob])	Query the workspace tgsen needs for this select and ijob (LAPACK stgsen with lwork = -1).
dtgsen_lwork(select, a[, ijob])	Query the workspace tgsen needs for this select and ijob (LAPACK dtgsen with lwork = -1).
ctgsen_lwork(select, a, b[, ijob])	Query the workspace tgsen needs for this select and ijob (LAPACK ctgsen with lwork = -1).
ztgsen_lwork(select, a, b[, ijob])	Query the workspace tgsen needs for this select and ijob (LAPACK ztgsen with lwork = -1).
stgsyl(a, b, c, d, e, f[, trans, ijob, ...])	Solve the generalized Sylvester equations (LAPACK stgsyl).
dtgsyl(a, b, c, d, e, f[, trans, ijob, ...])	Solve the generalized Sylvester equations (LAPACK dtgsyl).
stpttf(n, ap[, transr, uplo])	Convert a triangle from packed (TP) to rectangular full packed (RFP) storage (LAPACK stpttf).
dtpttf(n, ap[, transr, uplo])	Convert a triangle from packed (TP) to rectangular full packed (RFP) storage (LAPACK dtpttf).
ctpttf(n, ap[, transr, uplo])	Convert a triangle from packed (TP) to rectangular full packed (RFP) storage (LAPACK ctpttf).
ztpttf(n, ap[, transr, uplo])	Convert a triangle from packed (TP) to rectangular full packed (RFP) storage (LAPACK ztpttf).
stpttr(n, ap[, uplo])	Convert a triangle from packed (TP) to full (TR) storage (LAPACK stpttr).
dtpttr(n, ap[, uplo])	Convert a triangle from packed (TP) to full (TR) storage (LAPACK dtpttr).
ctpttr(n, ap[, uplo])	Convert a triangle from packed (TP) to full (TR) storage (LAPACK ctpttr).
ztpttr(n, ap[, uplo])	Convert a triangle from packed (TP) to full (TR) storage (LAPACK ztpttr).
strcon(a[, norm, uplo, diag])	Estimate the reciprocal condition number of a triangular matrix (LAPACK strcon).
dtrcon(a[, norm, uplo, diag])	Estimate the reciprocal condition number of a triangular matrix (LAPACK dtrcon).
ctrcon(a[, norm, uplo, diag])	Estimate the reciprocal condition number of a triangular matrix (LAPACK ctrcon).
ztrcon(a[, norm, uplo, diag])	Estimate the reciprocal condition number of a triangular matrix (LAPACK ztrcon).
strexc(a, q, ifst, ilst[, wantq, ...])	Reorder a Schur decomposition by moving one diagonal block (LAPACK strexc).
dtrexc(a, q, ifst, ilst[, wantq, ...])	Reorder a Schur decomposition by moving one diagonal block (LAPACK dtrexc).
ctrexc(a, q, ifst, ilst[, wantq, ...])	Reorder a Schur decomposition by moving one diagonal block (LAPACK ctrexc).
ztrexc(a, q, ifst, ilst[, wantq, ...])	Reorder a Schur decomposition by moving one diagonal block (LAPACK ztrexc).
strsen(select, t, q[, job, wantq, lwork, ...])	Reorder a Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK strsen).
dtrsen(select, t, q[, job, wantq, lwork, ...])	Reorder a Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK dtrsen).
ctrsen(select, t, q[, job, wantq, lwork, ...])	Reorder a Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK ctrsen).
ztrsen(select, t, q[, job, wantq, lwork, ...])	Reorder a Schur decomposition to gather selected eigenvalues into a leading cluster (LAPACK ztrsen).
strsen_lwork(select, t[, job])	Query the workspace trsen needs for this select and job (LAPACK strsen with lwork = -1).
dtrsen_lwork(select, t[, job])	Query the workspace trsen needs for this select and job (LAPACK dtrsen with lwork = -1).
ctrsen_lwork(select, t[, job])	Query the workspace trsen needs for this select and job (LAPACK ctrsen with lwork = -1).
ztrsen_lwork(select, t[, job])	Query the workspace trsen needs for this select and job (LAPACK ztrsen with lwork = -1).
strsyl(a, b, c[, trana, tranb, isgn, ...])	Solve the Sylvester equation op(a) @ x + isgn * x @ op(b) = scale * c (LAPACK strsyl).
dtrsyl(a, b, c[, trana, tranb, isgn, ...])	Solve the Sylvester equation op(a) @ x + isgn * x @ op(b) = scale * c (LAPACK dtrsyl).
ctrsyl(a, b, c[, trana, tranb, isgn, ...])	Solve the Sylvester equation op(a) @ x + isgn * x @ op(b) = scale * c (LAPACK ctrsyl).
ztrsyl(a, b, c[, trana, tranb, isgn, ...])	Solve the Sylvester equation op(a) @ x + isgn * x @ op(b) = scale * c (LAPACK ztrsyl).
strtri(c[, lower, unitdiag, overwrite_c])	Invert a triangular matrix (LAPACK strtri).
dtrtri(c[, lower, unitdiag, overwrite_c])	Invert a triangular matrix (LAPACK dtrtri).
ctrtri(c[, lower, unitdiag, overwrite_c])	Invert a triangular matrix (LAPACK ctrtri).
ztrtri(c[, lower, unitdiag, overwrite_c])	Invert a triangular matrix (LAPACK ztrtri).
strtrs(a, b[, lower, trans, unitdiag, lda, ...])	Solve a triangular system (LAPACK strtrs).
dtrtrs(a, b[, lower, trans, unitdiag, lda, ...])	Solve a triangular system (LAPACK dtrtrs).
ctrtrs(a, b[, lower, trans, unitdiag, lda, ...])	Solve a triangular system (LAPACK ctrtrs).
ztrtrs(a, b[, lower, trans, unitdiag, lda, ...])	Solve a triangular system (LAPACK ztrtrs).
strttf(a[, transr, uplo])	Convert a triangle from full (TR) to rectangular full packed (RFP) storage (LAPACK strttf).
dtrttf(a[, transr, uplo])	Convert a triangle from full (TR) to rectangular full packed (RFP) storage (LAPACK dtrttf).
ctrttf(a[, transr, uplo])	Convert a triangle from full (TR) to rectangular full packed (RFP) storage (LAPACK ctrttf).
ztrttf(a[, transr, uplo])	Convert a triangle from full (TR) to rectangular full packed (RFP) storage (LAPACK ztrttf).
strttp(a[, uplo])	Convert a triangle from full (TR) to packed (TP) storage (LAPACK strttp).
dtrttp(a[, uplo])	Convert a triangle from full (TR) to packed (TP) storage (LAPACK dtrttp).
ctrttp(a[, uplo])	Convert a triangle from full (TR) to packed (TP) storage (LAPACK ctrttp).
ztrttp(a[, uplo])	Convert a triangle from full (TR) to packed (TP) storage (LAPACK ztrttp).
stzrzf(a[, lwork, overwrite_a])	Reduce an upper trapezoidal matrix to upper triangular form (LAPACK stzrzf).
dtzrzf(a[, lwork, overwrite_a])	Reduce an upper trapezoidal matrix to upper triangular form (LAPACK dtzrzf).
ctzrzf(a[, lwork, overwrite_a])	Reduce an upper trapezoidal matrix to upper triangular form (LAPACK ctzrzf).
ztzrzf(a[, lwork, overwrite_a])	Reduce an upper trapezoidal matrix to upper triangular form (LAPACK ztzrzf).
stzrzf_lwork(m, n)	Query the optimal workspace for stzrzf (LAPACK stzrzf with lwork = -1).
dtzrzf_lwork(m, n)	Query the optimal workspace for dtzrzf (LAPACK dtzrzf with lwork = -1).
ctzrzf_lwork(m, n)	Query the optimal workspace for ctzrzf (LAPACK ctzrzf with lwork = -1).
ztzrzf_lwork(m, n)	Query the optimal workspace for ztzrzf (LAPACK ztzrzf with lwork = -1).
cunghr(a, tau[, lo, hi, lwork, overwrite_a])	Generate the orthogonal/unitary matrix of a Hessenberg reduction (LAPACK cunghr).
zunghr(a, tau[, lo, hi, lwork, overwrite_a])	Generate the orthogonal/unitary matrix of a Hessenberg reduction (LAPACK zunghr).
cunghr_lwork(n[, lo, hi])	Query the optimal workspace for cunghr (LAPACK cunghr with lwork = -1).
zunghr_lwork(n[, lo, hi])	Query the optimal workspace for zunghr (LAPACK zunghr with lwork = -1).
cungqr(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a QR factorization (LAPACK cungqr).
zungqr(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a QR factorization (LAPACK zungqr).
cungrq(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a RQ factorization (LAPACK cungrq).
zungrq(a, tau[, lwork, overwrite_a])	Generate the orthogonal/unitary factor Q of a RQ factorization (LAPACK zungrq).
cunmqr(side, trans, a, tau, c, lwork[, ...])	Multiply a matrix by the Q of a QR factorization (LAPACK cunmqr).
zunmqr(side, trans, a, tau, c, lwork[, ...])	Multiply a matrix by the Q of a QR factorization (LAPACK zunmqr).
sgeqrt(nb, a[, overwrite_a])	Blocked QR factorization in the compact WY representation (LAPACK sgeqrt).
dgeqrt(nb, a[, overwrite_a])	Blocked QR factorization in the compact WY representation (LAPACK dgeqrt).
cgeqrt(nb, a[, overwrite_a])	Blocked QR factorization in the compact WY representation (LAPACK cgeqrt).
zgeqrt(nb, a[, overwrite_a])	Blocked QR factorization in the compact WY representation (LAPACK zgeqrt).
sgemqrt(v, t, c[, side, trans, overwrite_c])	Multiply a matrix by the Q of a compact-WY QR factorization (LAPACK sgemqrt).
dgemqrt(v, t, c[, side, trans, overwrite_c])	Multiply a matrix by the Q of a compact-WY QR factorization (LAPACK dgemqrt).
cgemqrt(v, t, c[, side, trans, overwrite_c])	Multiply a matrix by the Q of a compact-WY QR factorization (LAPACK cgemqrt).
zgemqrt(v, t, c[, side, trans, overwrite_c])	Multiply a matrix by the Q of a compact-WY QR factorization (LAPACK zgemqrt).
sgttrf(dl, d, du[, overwrite_dl, ...])	Compute the LU factorization of a tridiagonal matrix with partial pivoting (LAPACK sgttrf).
dgttrf(dl, d, du[, overwrite_dl, ...])	Compute the LU factorization of a tridiagonal matrix with partial pivoting (LAPACK dgttrf).
cgttrf(dl, d, du[, overwrite_dl, ...])	Compute the LU factorization of a tridiagonal matrix with partial pivoting (LAPACK cgttrf).
zgttrf(dl, d, du[, overwrite_dl, ...])	Compute the LU factorization of a tridiagonal matrix with partial pivoting (LAPACK zgttrf).
sgttrs(dl, d, du, du2, ipiv, b[, trans, ...])	Solve a tridiagonal system using the factorization from gttrf (LAPACK sgttrs).
dgttrs(dl, d, du, du2, ipiv, b[, trans, ...])	Solve a tridiagonal system using the factorization from gttrf (LAPACK dgttrs).
cgttrs(dl, d, du, du2, ipiv, b[, trans, ...])	Solve a tridiagonal system using the factorization from gttrf (LAPACK cgttrs).
zgttrs(dl, d, du, du2, ipiv, b[, trans, ...])	Solve a tridiagonal system using the factorization from gttrf (LAPACK zgttrs).
sgtcon(dl, d, du, du2, ipiv, anorm[, norm])	Estimate the reciprocal condition number of a tridiagonal matrix from its gttrf factorization (LAPACK sgtcon).
dgtcon(dl, d, du, du2, ipiv, anorm[, norm])	Estimate the reciprocal condition number of a tridiagonal matrix from its gttrf factorization (LAPACK dgtcon).
cgtcon(dl, d, du, du2, ipiv, anorm[, norm])	Estimate the reciprocal condition number of a tridiagonal matrix from its gttrf factorization (LAPACK cgtcon).
zgtcon(dl, d, du, du2, ipiv, anorm[, norm])	Estimate the reciprocal condition number of a tridiagonal matrix from its gttrf factorization (LAPACK zgtcon).
stpqrt(l, nb, a, b[, overwrite_a, overwrite_b])	Blocked QR factorization of a triangular-pentagonal matrix (LAPACK stpqrt).
dtpqrt(l, nb, a, b[, overwrite_a, overwrite_b])	Blocked QR factorization of a triangular-pentagonal matrix (LAPACK dtpqrt).
ctpqrt(l, nb, a, b[, overwrite_a, overwrite_b])	Blocked QR factorization of a triangular-pentagonal matrix (LAPACK ctpqrt).
ztpqrt(l, nb, a, b[, overwrite_a, overwrite_b])	Blocked QR factorization of a triangular-pentagonal matrix (LAPACK ztpqrt).
stpmqrt(l, v, t, a, b[, side, trans, ...])	Multiply a matrix by the Q of a triangular-pentagonal QR factorization (LAPACK stpmqrt).
dtpmqrt(l, v, t, a, b[, side, trans, ...])	Multiply a matrix by the Q of a triangular-pentagonal QR factorization (LAPACK dtpmqrt).
ctpmqrt(l, v, t, a, b[, side, trans, ...])	Multiply a matrix by the Q of a triangular-pentagonal QR factorization (LAPACK ctpmqrt).
ztpmqrt(l, v, t, a, b[, side, trans, ...])	Multiply a matrix by the Q of a triangular-pentagonal QR factorization (LAPACK ztpmqrt).
cuncsd(x11, x12, x21, x22[, compute_u1, ...])	CS decomposition of a partitioned unitary matrix (LAPACK cuncsd).
zuncsd(x11, x12, x21, x22[, compute_u1, ...])	CS decomposition of a partitioned unitary matrix (LAPACK zuncsd).
cuncsd_lwork(m, p, q)	Query the workspace cuncsd needs (LAPACK cuncsd with lwork = -1).
zuncsd_lwork(m, p, q)	Query the workspace zuncsd needs (LAPACK zuncsd with lwork = -1).
cunmrz(a, tau, c[, side, trans, lwork, ...])	Multiply a matrix by the Q of an RZ factorization (LAPACK cunmrz).
zunmrz(a, tau, c[, side, trans, lwork, ...])	Multiply a matrix by the Q of an RZ factorization (LAPACK zunmrz).
cunmrz_lwork(m, n[, side, trans])	Query the optimal workspace for cunmrz (LAPACK cunmrz with lwork = -1).
zunmrz_lwork(m, n[, side, trans])	Query the optimal workspace for zunmrz (LAPACK zunmrz with lwork = -1).
ilaver()	Version of the LAPACK library this module is linked against (LAPACK ilaver).