Enhancements of Discretization Approaches for Non-Convex Mixed-Integer Quadratically Constraint Quadratic Programming: Part I

TL;DR

This paper introduces enhanced MIP relaxation techniques, including HybS and sawtooth relaxations, for non-convex MIQCQPs, demonstrating their theoretical advantages and effectiveness in producing tighter bounds.

Contribution

It presents novel hybrid separable (HybS) and sawtooth MIP relaxations, improving the solution quality of non-convex MIQCQPs compared to existing methods.

Findings

HybS provides tighter dual bounds than previous relaxations.

The sawtooth relaxation effectively handles univariate quadratic terms.

Computational results show significant improvements in relaxation tightness.

Abstract

We study mixed-integer programming (MIP) relaxation techniques for the solution of non convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We present MIP relaxation methods for non convex continuous variable products. In Part I, we consider MIP relaxations based on separable reformulation. The main focus is the introduction of the enhanced separable MIP relaxation for nonconvex quadratic products of the form z=xy, called hybrid separable (HybS). Additionally, we introduce a logarithmic MIP relaxation for univariate quadratic terms, called sawtooth relaxation. We combine the latter with HybS and existing separable reformulations to derive MIP relaxations of MIQCQPs. We provide a comprehensive theoretical analysis of these techniques, underlining the theoretical advantages of HybS compared to its predecessors. We perform a broad computational study to demonstrate the effectiveness of the enhanced MIP relaxation in terms of producing tight dual bounds for MIQCQPs. In Part II, we study MIP relaxations that extend the well-known MIP relaxation normalized multiparametric disaggregation technique (NMDT) and present further theoretical and computational analyses.