MOORE-PENROSE INVERSE SIMPLEX ALGORITHMS BASED ON SUCCESSIVE LINEAR SUBPROGRAMMING APPROACH

摘要

Generalized simplex variants based on successive linear subprogramming approach (SLS) are described in this paper. In stead of inverse matrix, these variants employ Moore-Penrose inverse. They are respectively characterized by different pivoting rules, Numerical results of limited tests show encouraging performance of these variants.