Sparsity-preserving sor algorithms for separable quadratic and linear programming

Abstract

The main purpose of this work is to give explicit sparsity-preserving SOR (successive overrelaxation) algorithms for the solution of separable quadratic and linear programming problems. The principal and computationally-distinguishing feature of the present SOR algorithms is that they preserve the sparsity structure of the problem and do not require the computation of the product of the constraint matrix by its transpose as is the case in earlier SOR algorithms for linear and quadratic programming.