Well-Posed Uniform Solvability of Convex Optimization Problems on a Uniform Differentiable Closed Convex Set

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-12-12 DOI:10.1007/s00245-024-10206-6
Shaoqiang Shang
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Abstract

In this paper, we first give the definition of uniformly differentiable set and give the definitions of sets \(P(A,\eta , r)\) and \(P_{A,\delta }(f)\). Secondly, we prove that if the set A is bounded closed convex set, then A is uniformly differentiable if and only if for any \(\varepsilon , \eta , r>0\), there exists \(\delta =\delta (\varepsilon ,\eta ,r )>0\) such that \(\Vert x-y\Vert <\varepsilon \) whenever \(f\in P(A,\eta , r)\), \(y\in P_{A,\delta }(f)\) and \(x\in P_{A}(f)\). Moreover, we also prove that if A is a bounded closed convex set in a finite-dimensional space X, then A is differentiable if and only if A is uniformly differentiable. Finally, we give some examples of uniformly differentiable set. Therefore, we extend some conclusions (SIAM J. Optim. Vol. 30, No. 1, pp. 490–512).

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一致可微闭凸集上凸优化问题的适定一致可解性
本文首先给出了一致可微集的定义,并给出了集合\(P(A,\eta , r)\)和\(P_{A,\delta }(f)\)的定义。其次,证明了如果集合A是有界闭凸集,则A是一致可微的当且仅当对于任意\(\varepsilon , \eta , r>0\),存在\(\delta =\delta (\varepsilon ,\eta ,r )>0\)使得\(\Vert x-y\Vert <\varepsilon \)无论\(f\in P(A,\eta , r)\), \(y\in P_{A,\delta }(f)\), \(x\in P_{A}(f)\)。此外,我们还证明了如果A是有限维空间X中的有界闭凸集,则当且仅当A一致可微时,A是可微的。最后给出了一致可微集的一些例子。因此,我们推广了一些结论(SIAM J. Optim。第30卷第1期,490-512页)。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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