OT Solver

Optimal transport (OT) solvers aim to efficiently find optimal mappings between probability distributions, a crucial task in various fields. Current research focuses on improving the speed and scalability of OT solvers, particularly through entropic regularization, decentralized algorithms, and neural network-based approaches like OT-Net, which addresses limitations of existing methods in handling discontinuous maps and large datasets. These advancements are significant because they enable the application of OT to increasingly complex problems in machine learning, data analysis, and other areas requiring efficient comparison and manipulation of probability distributions.