English(EN) Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guarantees

新框架通过学习优化来加速定点迭代

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一个新的优化定点迭代的框架，定点迭代是解决数学问题的常用方法。该框架被称为“学习优化”（L2O），它通过引入对标准 Krasnosel'skii-Mann 迭代精心选择的扰动来实现。目标是在保持收敛保证的同时，提高一系列问题的平均情况性能。该方法已被证明可以加速结构单调包含问题的求解，并通过增强的 Douglas-Rachford 分裂算法在最佳逼近问题上得到了验证。 AI

影响这项研究可能导致更高效的求解器，用于解决人工智能和机器学习中遇到的复杂数学问题。

排序理由该集群包含一篇研究论文，详细介绍了解决定点问题的新算法框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Andrea Martin, Giuseppe Belgioioso · 2026-10-12 04:00

学习加速 Krasnosel'skii-Mann 定点迭代并附带保证

arXiv:2601.07665v2 Announce Type: replace-cross Abstract: We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel's…

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学习加速 Krasnosel'skii-Mann 定点迭代并附带保证

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