bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / linalg / _flapack / dsbevx
lapack_function
scipy.linalg._flapack:dsbevx
source: build-install/usr/lib/python3.14/site-packages/scipy/linalg/_flapack.cpython-314-x86_64-linux-gnu.so
Summary
Selected eigenvalues and eigenvectors of a symmetric band matrix (LAPACK dsbevx).
Parameters
ab: ndarrayThe band of the symmetric/Hermitian matrix, shape
(kd + 1, n): the ordernand the bandwidthkdare both read off ab itself. It is consumed rather than returned.vl: floatLower bound of the half-open interval; used only when range is 1.
vu: floatUpper bound of the half-open interval; used only when range is 1.
il: intIndex of the smallest eigenvalue to return, 1-based; used only when range is 2. This group keeps Fortran indexing, deliberately.
iu: intIndex of the largest eigenvalue to return, 1-based; used only when range is 2.
ldab: int, optionalLeading dimension of ab. It must equal
ab.shape[0]; the argument exists only because the.pyfexposed it. Default isab.shape[0].compute_v: int, optionalIf nonzero, eigenvectors are computed and z is sized for them; otherwise z comes back as a 1x1 placeholder. Default is 1.
range: int, optionalWhich eigenvalues to compute: 0 for all, 1 for those in
(vl, vu], 2 for those with indices il through iu. Default is 0.lower: int, optionalIf nonzero, ab holds the lower band; otherwise the upper. Default is 0.
abstol: float, optionalAbsolute error tolerance. Accuracy is best at twice the underflow threshold,
2 * lamch('S'), rather than at 0. Default is 0.0.mmax: int, optionalHow many eigenvectors z has room for. With
range == 1the count is not known until the call returns, so the default is the whole spectrum; pass a tighter bound to avoid allocating for eigenvectors you will not get.overwrite_ab: int, optionalIf nonzero, ab may be overwritten in place -- and it is consumed either way, never returned. Unlike the rest of this module, the default is 1; pass 0 to keep your array intact.
Returns
w: ndarrayEigenvalues in ascending order, length
n. Real for every flavor.z: ndarrayEigenvectors as columns,
(n, mmax)when compute_v is set.m: intNumber of eigenvalues actually found.
ifail: ndarrayIndices of the eigenvectors that failed to converge; meaningful only when info is positive.
info: int0 on success; if negative, the
-info-th argument had an illegal value.
Aliases
scipy.linalg._flapack.dsbevx