LA_GELSS and LA_GELSD compute the minimum-norm least
squares solution to one or more real or complex linear systems
using the singular value decomposition of . Matrix
is rectangular and may be rank-deficient.
The vectors and corresponding solution vectors are
the columns of matrices denoted and , respectively.
The effective rank of is determined by treating as zero those singular values which are less than times the largest singular value. In addition to , the routines also return the right singular vectors and, optionally, the rank and singular values of .
LA_GELSD combines the singular value decomposition with a divide and conquer technique. For large matrices it is often much faster than LA_GELSS but uses more workspace.