Parallelizable Gradient-Based Optimization For Multi-Objective MaxCut

2026-08-25

AI Summary

本文针对多目标MaxCut问题，提出一种结合邻接二次公式和线性标量化的方法，开发出可并行化的梯度优化框架MO-QUCO，实现高效求解。

Abstract

Multi-objective combinatorial optimization arises in a wide range of problems and applications, including the canonical multi-objective MaxCut problem. Differentiable single-instance quadratic methods have recently achieved remarkable performance in single-objective combinatorial optimization. In this paper, we develop a differentiable framework for multi-objective MaxCut by combining an adjacency-based quadratic formulation with linear scalarization, thereby reducing the problem to a preference-conditioned single-objective signed-weight MaxCut problem. Theoretically, we characterize the stationary points of the resulting signed-weight formulation and show how they induce preference-conditioned fixed points on the Pareto front. Computationally, unlike conventional heuristics and branch-and-bound methods, our approach is GPU-parallelizable and can therefore benefit from substantial performance speedups. We term our algorithm Multi-objective QUadratic Combinatorial Optimization (MO-QUCO) and its parallelized variant pMO-QUCO. Empirically, across different multi-layered (and weight distributions) graphs, we show that both our CPU-only and GPU-based algorithms outperform SOTA exact and heuristic methods in terms of wall-clock runtime and objective quality. Despite operating under different computational settings, MO-QUCO also outperforms the SOTA quantum method.

Problem

• multi-objective MaxCut
• combinatorial optimization
• differentiable framework

Innovation

• Gradient-Based Optimization
• Multi-Objective MaxCut
• GPU-Parallelizable
• Linear Scalarization
• Pareto Front