Convergence proof for the GenCol algorithm in the case of two-marginal optimal transport

IF 2.2 2区 数学 Q1 MATHEMATICS, APPLIED
Gero Friesecke, Maximilian Penka
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引用次数: 0

Abstract

The recently introduced Genetic Column Generation (GenCol) algorithm has been numerically observed to efficiently and accurately compute high-dimensional optimal transport (OT) plans for general multi-marginal problems, but theoretical results on the algorithm have hitherto been lacking. The algorithm solves the OT linear program on a dynamically updated low-dimensional submanifold consisting of sparse plans. The submanifold dimension exceeds the sparse support of optimal plans only by a fixed factor β \beta . Here we prove that for β 2 \beta \geq 2 and in the two-marginal case, GenCol always converges to an exact solution, for arbitrary costs and marginals. The proof relies on the concept of c-cyclical monotonicity. As an offshoot, GenCol rigorously reduces the data complexity of numerically solving two-marginal OT problems from O ( 2 ) O(\ell ^2) to O ( ) O(\ell ) without any loss in accuracy, where \ell is the number of discretization points for a single marginal. At the end of the paper we also present some insights into the convergence behavior in the multi-marginal case.

双边际最优运输情况下 GenCol 算法的收敛性证明
最近推出的遗传列生成(GenCol)算法已被数值观测到,可以高效、准确地计算一般多边际问题的高维最优运输(OT)计划,但迄今为止还缺乏有关该算法的理论成果。该算法在由稀疏计划组成的动态更新的低维子平面上求解 OT 线性程序。子平面的维度仅以固定系数 β \beta 的方式超出最优计划的稀疏支持。在这里,我们将证明对于 β ≥ 2 \beta \geq 2 和双边际情况,GenCol 总是收敛于精确解,适用于任意成本和边际。证明依赖于 c 周期单调性的概念。作为一个分支,GenCol 严格地将数值求解双边际 OT 问题的数据复杂度从 O ( ℓ 2 ) O(\ell ^2) 降低到 O ( ℓ ) O(\ell),并且没有任何精度损失,其中 ℓ \ell 是单个边际的离散点数。在本文的最后,我们还提出了对多边际情况下收敛行为的一些见解。
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 Computation
Mathematics of Computation 数学-应用数学
CiteScore
3.90
自引率
5.00%
发文量
55
审稿时长
7.0 months
期刊介绍: All articles submitted to this journal are peer-reviewed. The AMS has a single blind peer-review process in which the reviewers know who the authors of the manuscript are, but the authors do not have access to the information on who the peer reviewers are. This journal is devoted to research articles of the highest quality in computational mathematics. Areas covered include numerical analysis, computational discrete mathematics, including number theory, algebra and combinatorics, and related fields such as stochastic numerical methods. Articles must be of significant computational interest and contain original and substantial mathematical analysis or development of computational methodology.
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