Bilevel Optimization of the Kantorovich Problem and Its Quadratic Regularization

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Sebastian Hillbrecht, Paul Manns, 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 second part in a series of three papers. While the existence of optimal solutions to both the bilevel Kantorovich problem and its regularized counterpart were shown in the first part, this paper deals with the (weak-\(*\)) convergence of solutions to the regularized bilevel problem to solutions of the original bilevel Kantorovich problem for vanishing regularization parameters.

康托洛维奇问题的双层优化及其二次正则化
本文关注的是一个优化问题,它受最优运输的康托洛维奇问题支配。更确切地说,我们考虑的是一个以康托洛维奇问题为基础的双层优化问题。这项任务可以重新表述为一个数学问题,它在正则玻尔量纲空间中具有互补性约束。由于互补性约束所引起的非平稳性,这类问题通常会被正则化,例如通过熵正则化。然而,在本文中,我们对康托洛维奇问题采用了二次正则化。这样,我们就能在尽可能保留最优运输计划稀疏性结构的同时,大幅降低其维度。正如标题所示,这是三篇论文系列中的第二部分。在第一部分中,我们已经证明了双级康托洛维奇问题及其正则化对应问题的最优解的存在,而本文则讨论了在正则化参数消失的情况下,正则化双级问题的解(弱/(*\))向原始双级康托洛维奇问题的解(弱/(*\))收敛的问题。
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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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