naginterfaces.library.lapackeig.dgelss(a, b, rcond)[source]

dgelss computes the minimum norm solution to a real linear least squares problem

For full information please refer to the NAG Library document for f08ka

afloat, array-like, shape

The matrix .

bfloat, array-like, shape

The right-hand side matrix .


Used to determine the effective rank of . Singular values are treated as zero. If , machine precision is used instead.

afloat, ndarray, shape

The first rows of are overwritten with its right singular vectors, stored row-wise.

bfloat, ndarray, shape

is overwritten by the solution matrix . If and , the residual sum of squares for the solution in the th column is given by the sum of squares of elements in that column.

sfloat, ndarray, shape

The singular values of in decreasing order.


The effective rank of , i.e., the number of singular values which are greater than .

(errno )

On entry, error in parameter .

Constraint: .

(errno )

On entry, error in parameter .

Constraint: .

(errno )

On entry, error in parameter .

Constraint: .

(errno )

The algorithm for computing the SVD failed to converge; off-diagonal elements of an intermediate bidiagonal form did not converge to zero.


dgelss uses the singular value decomposition (SVD) of , where is an matrix which may be rank-deficient.

Several right-hand side vectors and solution vectors can be handled in a single call; they are stored as the columns of the right-hand side matrix and the solution matrix .

The effective rank of is determined by treating as zero those singular values which are less than times the largest singular value.


Anderson, E, Bai, Z, Bischof, C, Blackford, S, Demmel, J, Dongarra, J J, Du Croz, J J, Greenbaum, A, Hammarling, S, McKenney, A and Sorensen, D, 1999, LAPACK Users’ Guide, (3rd Edition), SIAM, Philadelphia,

Golub, G H and Van Loan, C F, 1996, Matrix Computations, (3rd Edition), Johns Hopkins University Press, Baltimore