scipy.linalg.lapack.sgejsv

scipy.linalg.lapack.sgejsv(a, joba=4, jobu=0, jobv=0, jobr=1, jobt=0, jobp=1, lwork=..., overwrite_a=0) = <flapack function sgejsv>

Singular value decomposition by the Jacobi method, for high relative accuracy (LAPACK sgejsv).

LAPACK ships no complex counterpart, so only the real flavors exist.

Parameters:

andarray

Matrix of shape (m, n) with m >= n.

jobaint, optional

Accuracy level, an index into 'CEFGAR': 0 through 5. Default is 4.

jobuint, optional

Left singular vectors, an index into 'UFWN': 0 through 3. Default is 0.

jobvint, optional

Right singular vectors, an index into 'VJWN': 0 through 3. jobv of 1 requires jobu below 2. Default is 0.

jobrint, optional

If nonzero, extreme columns are pruned. Default is 1.

jobtint, optional

If nonzero, the transpose may be factored instead. Default is 0.

jobpint, optional

If nonzero, denormal values are flushed. Default is 1.

lworkint, optional

Workspace size, at least 7. Default is the largest of the several bounds the routine documents.

overwrite_aint, optional

If nonzero, a may be overwritten in place. Default is 0.

Returns:

svandarray

Singular values, length n.

undarray

Left singular vectors; empty when jobu and jobt ask for none.

vndarray

Right singular vectors; empty when jobv and jobt ask for none.

workoutndarray

The first seven workspace entries, which carry the scaling diagnostics.

iworkoutndarray

The first three integer workspace entries: numerical rank, nullity and the count of accurate singular values.

infoint

0 on success; if negative, the -info-th argument had an illegal value.