Learning to Relax Nonconvex Quadratically Constrained Quadratic Programs

Abstract

Quadratically constrained quadratic programs (QCQPs) are ubiquitous in optimization: Such problems arise in applications from operations research, power systems, signal processing, chemical engineering, and portfolio theory, among others. Despite their flexibility in modeling real-life situations and the recent effort to understand their properties, nonconvex QCQPs are hard to solve in practice. Most of the approaches in the literature are based on either Linear Programming (LP) or Semidefinite Programming (SDP) relaxations, each of which works very well for some problem subclasses but perform poorly on others. In this paper, we develop a relaxation selection procedure for nonconvex QCQPs that can adaptively decide whether an LP- or SDP-based approach is expected to be more beneficial by considering the instance structure. The proposed methodology relies on utilizing machine learning methods that involve features derived from spectral properties and sparsity patterns of data matrices, and once trained appropriately, the prediction model applies to any instance with an arbitrary number of variables and constraints. We develop classification and regression models under different feature-design setups, including a dimension-independent representation, and evaluate them on both synthetically generated instances and benchmark instances from MINLPLib. Our computational results demonstrate the effectiveness of the proposed approach for predicting the more favorable relaxation across diverse QCQP families.