A Hybrid Relaxation-Heuristic Framework for Solving MIP with Binary Variables

TL;DR

This paper introduces a hybrid framework combining relaxation and heuristic methods to efficiently solve large and complex MIQP problems with binary variables, demonstrating state-of-the-art results in portfolio optimization.

Contribution

It presents a novel two-phase hybrid approach integrating relaxation techniques with heuristic optimization to improve MIQP solution quality and computational efficiency.

Findings

Achieves state-of-the-art performance on benchmark portfolio optimization problems.

Effectively solves larger and more complex MIP instances.

Demonstrates robustness and flexibility of the hybrid framework.

Abstract

Mixed-Integer Programming (MIP), particularly Mixed-Integer Linear Programming (MILP) and Mixed-Integer Quadratic Programming (MIQP), has found extensive applications in domains such as portfolio optimization and network flow control, which inclusion of integer variables or cardinality constraints renders these problems NP-hard, posing significant computational challenges. While traditional approaches have explored approximation methods like heuristics and relaxation techniques (e.g. Lagrangian dual relaxation), the integration of these strategies within a unified hybrid framework remains underexplored. In this paper, we propose a generalized hybrid framework to address MIQP problems with binary variables, which consists of two phases: (1) a Mixed Relaxation Phase, which employs Linear Relaxation, Duality Relaxation, and Augmented Relaxation with randomized sampling to generate a diverse pre-solution pool, and (2) a Heuristic Optimization Phase, which refines the pool using Genetic Algorithms and Variable Neighborhood Search (VNS) to approximate binary solutions effectively. Becuase of the page limit, we will only detailedly evaluate the proposed framework on portfolio optimization problems using benchmark datasets from the OR Library, where the experimental results demonstrate state-of-the-art performance, highlighting the framework's ability to solve larger and more complex MIP problems efficiently. This study offers a robust and flexible methodology that bridges relaxation techniques and heuristic optimization, advancing the practical solvability of challenging MIP problems.