Cutting plane methods with gradient-based heuristics

TL;DR

This paper introduces a hybrid cutting plane method that uses gradient-based heuristics to generate tighter cuts near the optimal solution, improving convergence and efficiency in nonlinear discrete optimization.

Contribution

It proposes a novel hybrid approach combining cutting plane methods with gradient heuristics to enhance solution quality and computational speed in binary convex optimization problems.

Findings

Improved solution quality compared to traditional cutting plane methods

Faster convergence demonstrated in numerical experiments

Enhanced computational efficiency across different solvers

Abstract

Cutting plane methods, particularly outer approximation, are a well-established approach for solving nonlinear discrete optimization problems without relaxing the integrality of decision variables. While powerful in theory, their computational performance can be highly variable. Recent research has shown that constructing cutting planes at the projection of infeasible points onto the feasible set can significantly improve the performance of cutting plane approaches. Motivated by this, we examine whether constructing cuts at feasible points closer to the optimal solution set could further enhance the effectiveness of cutting plane methods. We propose a hybrid method that combines the global convergence guarantees of cutting plane methods with the local exploration capabilities of first-order optimization techniques. Specifically, we use projected gradient methods as a heuristic to identify promising regions of the solution space and generate tighter, more informative cuts. We focus on binary optimization problems with convex differentiable objective functions, where projection operations can be efficiently computed via mixed-integer linear programming. By constructing cuts at points closer to the optimal solution set and eliminating non-optimal regions, the algorithm achieves better approximation of the feasible region and faster convergence. Numerical experiments confirm that our approach improves both the quality of the solution and computational efficiency across different solver configurations. This framework provides a flexible foundation for further extensions to more general discrete domains and offers a promising heuristic to the toolkit for nonlinear discrete optimization.