Author

Alvaro Velasquez

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Preprint Aug 2026

Parallelizable Gradient-Based Optimization For Multi-Objective MaxCut

This paper develops 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.

Jing-Hang Huang, Alvaro Velasquez, Jia Liu et al. · 0 citations