A Vectorized Positive Semidefinite Penalty Method for Unconstrained Binary Quadratic Programming

TL;DR

This paper introduces a vectorized positive semidefinite penalty method for efficiently solving unconstrained binary quadratic programming problems, demonstrating superior performance over existing methods through numerical experiments.

Contribution

It proposes a novel vectorized PSDP approach with algorithmic enhancements and convergence analysis, improving computational efficiency and solution quality.

Findings

Outperforms semidefinite relaxation in solution quality

Achieves faster solution times

Provides high-quality solutions in practical applications

Abstract

The unconstrained binary quadratic programming (UBQP) problem is a class of problems of significant importance in many practical applications, such as in combinatorial optimization, circuit design, and other fields. The positive semidefinite penalty (PSDP) method originated from research on semidefinite relaxation, where the introduction of an exact penalty function improves the efficiency and accuracy of problem solving. In this paper, we propose a vectorized PSDP method for solving the UBQP problem, which optimizes computational efficiency by vectorizing matrix variables within a PSDP framework. Algorithmic enhancements in penalty updating and initialization are implemented, along with the introduction of two algorithms that integrate the proximal point algorithm and the projection alternating BB method for subproblem resolution. Properties of the penalty function and algorithm convergence are analyzed. Numerical experiments show the superior performance of the method in providing high-quality solutions and satisfactory solution times compared to the semidefinite relaxation method and other established methods.