Parabolic Regularity of Spectral Functions

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED Mathematics of Operations Research Pub Date : 2024-08-12 DOI:10.1287/moor.2023.0010
Ashkan Mohammadi, Ebrahim Sarabi
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Abstract

This paper is devoted to the study of the second-order variational analysis of spectral functions. It is well-known that spectral functions can be expressed as a composite function of symmetric functions and eigenvalue functions. We establish several second-order properties of spectral functions when their associated symmetric functions enjoy these properties. Our main attention is given to characterize parabolic regularity for this class of functions. It was observed recently that parabolic regularity can play a central rule in ensuring the validity of important second-order variational properties, such as twice epi-differentiability. We demonstrates that for convex spectral functions, their parabolic regularity amounts to that of their symmetric functions. As an important consequence, we calculate the second subderivative of convex spectral functions, which allows us to establish second-order optimality conditions for a class of matrix optimization problems.Funding: The research of A. Mohammadi is funded by a postdoctoral fellowship from Georgetown University. E. Sarabi is partially supported by the U.S. National Science Foundation [Grant DMS 2108546].
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谱函数的抛物线正则性
本文致力于研究谱函数的二阶变分分析。众所周知,谱函数可以表示为对称函数和特征值函数的复合函数。当谱函数的相关对称函数具有这些性质时,我们建立了谱函数的几个二阶性质。我们主要关注这类函数的抛物线正则性。最近观察到,抛物线正则性在确保重要的二阶变分性质(如两次表微分性)的有效性方面发挥着核心作用。我们证明,对于凸谱函数,其抛物线正则性相当于其对称函数的抛物线正则性。一个重要的结果是,我们计算了凸谱函数的二阶次乘法,从而为一类矩阵优化问题建立了二阶最优条件:A. Mohammadi 的研究由乔治城大学博士后奖学金资助。E. Sarabi 得到美国国家科学基金会 [Grant DMS 2108546] 的部分资助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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