English(EN) From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization

新优化框架推广了凹性和 DR-次模性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

本文介绍了一个名为上可线性化/可二次化函数的新框架，它推广了凹性和 DR-次模性等概念。作者开发了一种元算法，可以将现有的线性或二次最大化算法转换为能够优化这些新型函数类型的算法。这种方法为解决 DR-次模优化问题提供了一种统一的方法，并扩展到各种反馈设置，改进了最先进的结果，并实现了动态和自适应遗憾保证。 AI

排序理由该集群包含一篇学术论文，详细介绍了用于优化问题的新颖数学框架和算法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

Vaneet Aggarwal

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了用于优化问题的新颖数学框架和算法。[lever_c_demoted from research: ic=1 ai=0.4]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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Story freshness: 43 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mohammad Pedramfar, Vaneet Aggarwal · 2026-09-15 04:00

从线性到可线性化优化：一种应用于平稳和非平稳 DR-次模优化新框架

arXiv:2405.00065v4 Announce Type: replace-cross Abstract: This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various settings, including monotone and non-monotone cases. A general meta-algorithm i…

本文如何被排名

报道来源 [1]

从线性到可线性化优化：一种应用于平稳和非平稳 DR-次模优化新框架

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