Bilevel Optimization of the Kantorovich Problem and Its Quadratic Regularization

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Sebastian Hillbrecht, Christian Meyer
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引用次数: 0

Abstract

This paper is concerned with an optimization problem which is governed by the Kantorovich problem of optimal transport. More precisely, we consider a bilevel optimization problem with the underlying problem being the Kantorovich problem. This task can be reformulated as a mathematical problem with complementarity constraints in the space of regular Borel measures. Because of the non-smoothness that is induced by the complementarity constraints, problems of this type are often regularized, e.g., by an entropic regularization. However, in this paper we apply a quadratic regularization to the Kantorovich problem. By doing so, we are able to drastically reduce its dimension while preserving the sparsity structure of the optimal transportation plan as much as possible. As the title indicates, this is the first part of a series of three papers. It deals with the existence of optimal solutions to the bilevel problem and its quadratic regularization, while Parts II and III are devoted to convergence analysis for the vanishing regularization parameters.

Kantorovich问题的双层优化及其二次正则化
本文研究了一类由最优运输的Kantorovich问题支配的优化问题。更准确地说,我们考虑一个双层优化问题,其底层问题是Kantorovich问题。这个任务可以被重新表述为正则Borel测度空间中具有互补约束的数学问题。由于互补约束引起的非光滑性,这类问题通常被正则化,例如,通过熵正则化。然而,在本文中,我们将二次正则化应用于Kantorovich问题。通过这样做,我们能够大幅减少其维度,同时尽可能地保持最优运输计划的稀疏结构。正如标题所示,这是三篇系列论文的第一部分。第二部分和第三部分讨论了双层问题最优解的存在性及其二次正则化问题,第二部分和第三部分讨论了正则化参数消失的收敛性分析。
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
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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