Computes the minimum-norm solution to a linear least squares problem using the singular value decomposition of A and a divide and conquer method.
The routine computes the minimum-norm solution to a real linear least squares problem:
using the singular value decomposition (SVD) of
nmatrix which may be rank-deficient.
Several right hand side vectors
band solution vectors
xcan be handled in a single call; they are stored as the columns of the
nrhsright hand side matrix
The problem is solved in three steps:
- Reduce the coefficient matrix A to bidiagonal form with Householder transformations, reducing the original problem into a "bidiagonal least squares problem" (BLS).
- Solve the BLS using a divide and conquer approach.
- Apply back all the Householder transformations to solve the original least squares problem.
The effective rank of
Ais determined by treating as zero those singular values which are less than
rcondtimes the largest singular value.
- The number of rows of the matrixINTEGER.A().m≥0
- The number of columns of the matrixINTEGER.A().n≥0
- The number of right-hand sides; the number of columns inINTEGER.B(nrhs<