Large optimal transport problems can be approached via domain decomposition, i.e. by iteratively solving small partial problems independently and in parallel. Convergence to the global minimizers under suitable assumptions has been shown in the unregularized and entropy regularized setting and its computational efficiency has been demonstrated experimentally. An accurate theoretical understanding of its convergence speed in geometric settings is still lacking. In this article we work towards such an understanding by deriving, via $\Gamma$-convergence, an asymptotic description of the algorithm in the limit of infinitely fine partition cells. The limit trajectory of couplings is described by a continuity equation on the product space where the momentum is purely horizontal and driven by the gradient of the cost function. Convergence hinges on a regularity assumption that we investigate in detail. Global optimality of the limit trajectories remains an interesting open problem, even when global optimality is established at finite scales. Our result provides insights about the efficiency of the domain decomposition algorithm at finite resolutions and in combination with coarse-to-fine schemes.
翻译:大的最佳运输问题可以通过域分解处理,即通过独立和平行地迭接地解决小部分问题。在不正规和正辛酸的正规化环境及其计算效率中,从不正规和正辛酸的固定环境及其计算效率的实验中可以看出,在适当假设下与全球最小化的最小化者的趋同。对于其在几何环境中的趋同速度,仍然缺乏准确的理论理解。在本篇文章中,我们致力于通过在无限细微的分区细胞限度内对算法的无症状描述来达成这种理解。在产品空间上,对合并的极限轨迹通过一个连续方程式来描述,其动力纯粹是水平的,由成本函数的梯度驱动。趋同取决于我们详细调查的正常性假设。全球限制轨迹的最佳性仍然是一个令人感兴趣的开放问题,即使全球最佳性是在有限的尺度上确立。我们的结果提供了对在有限分辨率上和与微粒化计划相结合的域分解算法的效率的洞察。