论文标题
镜子sindhorn：在运输多台面上的快速在线优化
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
论文作者
论文摘要
最佳传输是机器学习中的重要工具，可以通过传输多型线性程序来捕获数据的几何特性。我们提出了一种单环优化算法，用于利用Sinkhorn矩阵缩放和镜像下降的原理，以最大程度地减少这些域上的一般凸目标。所提出的算法对噪声非常强大，可用于在线环境中。我们为凸目标和实验结果提供了理论保证，该结果表明了对合成和现实世界数据的有效性。
Optimal transport is an important tool in machine learning, allowing to capture geometric properties of the data through a linear program on transport polytopes. We present a single-loop optimization algorithm for minimizing general convex objectives on these domains, utilizing the principles of Sinkhorn matrix scaling and mirror descent. The proposed algorithm is robust to noise, and can be used in an online setting. We provide theoretical guarantees for convex objectives and experimental results showcasing it effectiveness on both synthetic and real-world data.
