反射器问题的熵最优输运数值方法

IF 0.6 Q4 MATHEMATICS, APPLIED
J. Benamou, W. IJzerman, G. Rukhaia
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引用次数: 6

摘要

点源远场反射器设计问题是非欧氏位移代价的经典最优输运问题之一[Wang, 2004] [Glimm and Oliker, 2003]。这项工作描述了使用熵最优传输和相关的Sinkhorn算法[Cuturi, 2013]来数值解决它。由于反射器建模是基于坎托罗维奇势,因此产生了几个问题。首先,关于离散熵近似的收敛性,这里我们遵循[Berman, 2017]最近的工作,特别是其中施加的离散化要求。其次,对Entropic OT引起的熵偏的校正,如[Ramdas et al., 2017] [Genevay et al., 2018] [Feydy et al., 2018]中所讨论的,是获得合理结果的另一个重要工具。本文回顾了产生所获得的数值结果所需的必要的数学和数值工具,并对其进行了讨论。我们发现,至少在简单的学术案例中,Sinkhorn算法可以适用于解决远场反射器问题。为了得到连续反射器近似,需要对连续势进行Sinkhorn正则扩展。使用Sinkhorn散度[Feydy等人,2018]有助于减轻熵偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An entropic optimal transport numerical approach to the reflector problem
The point source far field reflector design problem is one of the main classic optimal transport problems with a non-euclidean displacement cost [Wang, 2004] [Glimm and Oliker, 2003]. This work describes the use of Entropic Optimal Transport and the associated Sinkhorn algorithm [Cuturi, 2013] to solve it numerically. As the reflector modelling is based on the Kantorovich potentials , several questions arise. First, on the convergence of the discrete entropic approximation and here we follow the recent work of [Berman, 2017] and in particular the imposed discretization requirements therein. Secondly, the correction of the Entropic bias induced by the Entropic OT, as discussed in particular in [Ramdas et al., 2017] [Genevay et al., 2018] [Feydy et al., 2018], is another important tool to achieve reasonable results. The paper reviews the necessary mathematical and numerical tools needed to produce and discuss the obtained numerical results. We find that Sinkhorn algorithm may be adapted, at least in simple academic cases, to the resolution of the far field reflector problem. Sinkhorn canonical extension to continuous potentials is needed to generate continuous reflector approximations. The use of Sinkhorn divergences [Feydy et al., 2018] is useful to mitigate the entropic bias.
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来源期刊
Methods and applications of analysis
Methods and applications of analysis MATHEMATICS, APPLIED-
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33.30%
发文量
3
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