Efficient Preconditioners for Solving Dynamical Optimal Transport via Interior Point Methods

IF 3 2区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Scientific Computing Pub Date : 2024-05-02 DOI:10.1137/23m1570430
Enrico Facca, Gabriele Todeschi, Andrea Natale, Michele Benzi
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Abstract

SIAM Journal on Scientific Computing, Volume 46, Issue 3, Page A1397-A1422, June 2024.
Abstract. In this paper, we address the numerical solution of the quadratic optimal transport problem in its dynamical form, the so-called Benamou-Brenier formulation. When solved using interior point methods, the main computational bottleneck is the solution of large saddle point linear systems arising from the associated Newton-Raphson scheme. The main purpose of this paper is to design efficient preconditioners to solve these linear systems via iterative methods. Among the proposed preconditioners, we introduce one based on the partial commutation of the operators that compose the dual Schur complement of these saddle point linear systems, which we refer to as the [math]-preconditioner. A series of numerical tests show that the [math]-preconditioner is the most efficient among those presented, despite a performance deterioration in the last steps of the interior point method. It is in fact the only one having a CPU time that scales only slightly worse than linearly with respect to the number of unknowns used to discretize the problem.
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通过内点法求解动态优化传输的高效预处理器
SIAM 科学计算期刊》,第 46 卷第 3 期,第 A1397-A1422 页,2024 年 6 月。 摘要本文以动态形式,即所谓的 Benamou-Brenier 公式,讨论二次优化运输问题的数值求解。使用内点法求解时,主要的计算瓶颈是求解相关牛顿-拉斐森方案所产生的大型鞍点线性系统。本文的主要目的是设计高效的前置条件器,通过迭代法求解这些线性系统。在提出的预处理中,我们引入了一种基于组成这些鞍点线性系统的对偶舒尔补码的算子的部分换向的预处理,我们称之为 [math] 预处理。一系列数值测试表明,[math]-preconditioner 是所介绍的方法中最有效的,尽管在内部点法的最后几步性能有所下降。事实上,它是唯一一个 CPU 时间与问题离散化所用未知数数量的线性关系仅略微差一点的预处理器。
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来源期刊
CiteScore
5.50
自引率
3.20%
发文量
209
审稿时长
1 months
期刊介绍: The purpose of SIAM Journal on Scientific Computing (SISC) is to advance computational methods for solving scientific and engineering problems. SISC papers are classified into three categories: 1. Methods and Algorithms for Scientific Computing: Papers in this category may include theoretical analysis, provided that the relevance to applications in science and engineering is demonstrated. They should contain meaningful computational results and theoretical results or strong heuristics supporting the performance of new algorithms. 2. Computational Methods in Science and Engineering: Papers in this section will typically describe novel methodologies for solving a specific problem in computational science or engineering. They should contain enough information about the application to orient other computational scientists but should omit details of interest mainly to the applications specialist. 3. Software and High-Performance Computing: Papers in this category should concern the novel design and development of computational methods and high-quality software, parallel algorithms, high-performance computing issues, new architectures, data analysis, or visualization. The primary focus should be on computational methods that have potentially large impact for an important class of scientific or engineering problems.
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