A RANGE-SPACE IMPLEMENTATION FOR LARGE QUADRATIC PROGRAMS WITH SMALL ACTIVE SETS

Large and sparse convex quadratic programming problems are often generated in the course of solving large-scale optimization problems. An important class of these problems has the property that only a small number of constraints are at their bounds at a solution. We describe an implementation of a range-space method designed for efficient solution of these small-active-set problems. The implementation is oriented toward the application area of multidimensional data fitting subject to constraints. Test results are presented for several data-fitting problems.