QPDAS: Dual Active Set Solver for Mixed Constraint Quadratic Programming

TL;DR

This paper introduces QPDAS, a dual active set solver for mixed constraint quadratic programming, featuring novel linear system factorization and iterative refinement techniques for improved accuracy and efficiency.

Contribution

It proposes a new factorization method for linear systems in active set algorithms and demonstrates how iterative refinement enhances solution accuracy in quadratic programming.

Findings

Effective linear system factorization method

Iterative refinement improves solution accuracy

Applicable to semi-definite quadratic problems

Abstract

We present a method for solving the general mixed constrained convex quadratic programming problem using an active set method on the dual problem. The approach is similar to existing active set methods, but we present a new way of solving the linear systems arising in the algorithm. There are two main contributions; we present a new way of factorizing the linear systems, and show how iterative refinement can be used to achieve good accuracy and to solve both types of sub-problems that arise from semi-definite problems.