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English(EN) Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT

图粗化方法提升MAX-CUT优化性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的方法，将图粗化与粒子多项式松弛（PPR）相结合，以改进非凸MAX-CUT问题的优化。该方法利用组合多网格（CMG）为PPR提供热启动，从而高效地生成结构感知型初始解。该组合流水线在基准实例上达到了已知最优值的较高百分比，并通过减少内存需求有效地扩展到更大的图，显示出显著的改进。 AI

影响 这项研究引入了一种新颖的优化技术，可以提高解决复杂图计算问题的效率和可扩展性，可能对依赖此类计算的领域产生影响。

排序理由 详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CL 阅读 →

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图粗化方法提升MAX-CUT优化性能

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详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · M. Harper Langston, Pierre-David Letourneau, Dalton Jones, Harris Teague, Peter Scott, Mingu Lee, Richard Lethin, Chris Lott, Roland Memisevic, Fatih Porikli ·

    图粗化使计算受限的非凸MAX-CUT问题能够逃离局部最优

    arXiv:2610.19043v1 Announce Type: cross Abstract: Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle Polynomial Relaxation (PPR), a moment-based nonconvex continuous reformulation po…

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