On regularization schemes for data-driven optimization based on Cressie–Read divergence and CVaR

IF 1.6 3区数学 Q2 MATHEMATICS, APPLIED

Optimization Pub Date : 2024-04-29 DOI:10.1080/02331934.2024.2345356

Wei Ni

引用次数: 0

Abstract

This paper presents several regularization schemes for data-driven optimization problems. Data-driven optimization is a challenging task because the model or the distribution describing the inheren...

查看原文本刊更多论文

基于 Cressie-Read 发散和 CVaR 的数据驱动优化正则化方案

本文介绍了几种针对数据驱动优化问题的正则化方案。数据驱动的优化是一项具有挑战性的任务，因为描述固有数据的模型或分布是由数据驱动的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Optimization 数学-应用数学

CiteScore

4.50

自引率

9.10%

发文量

146

审稿时长

4.5 months

期刊介绍： Optimization publishes refereed, theoretical and applied papers on the latest developments in fields such as linear, nonlinear, stochastic, parametric, discrete and dynamic programming, control theory and game theory. A special section is devoted to review papers on theory and methods in interesting areas of mathematical programming and optimization techniques. The journal also publishes conference proceedings, book reviews and announcements. All published research articles in this journal have undergone rigorous peer review, based on initial editor screening and anonymous refereeing by independent expert referees.