新算法解决了非凸多目标双层优化问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员开发了新的无Hessian算法MOMEHA和MB-MOMEHA，以解决多目标双层优化问题，特别是那些具有非凸下层的优化问题。这些方法利用Moreau包络将问题转化为具有包络约束的单层优化问题。这些算法通过单循环和无Hessian的计算效率，并结合平滑加权Tchebycheff标量化，保持了计算效率。在少样本元学习和神经架构搜索上的实验表明，这些新方法在帕累托前沿质量方面优于现有方法。 AI

影响这些算法可以提高元学习和神经架构搜索等AI应用的效率。

排序理由该集群包含一篇详细介绍特定优化问题新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yicong Jiang, Feihu Huang · 2026-08-14 04:00

面向非凸下层问题的多目标双层优化的无Hessian方法

arXiv:2608.12704v1 Announce Type: cross Abstract: Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently some works have been begun to study the multi-objective bilevel optimization, the…

本文如何被排名

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面向非凸下层问题的多目标双层优化的无Hessian方法

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