Accelerated Algorithm for Sparse Regularized Partial Optimal Transport
This work proposes a new framework to solve sparse regularized Partial Optimal Transport (POT) using smooth and strongly convex penalties like quadratic or elastic-net regularization. It reformulates the problem by adding an exterior quadratic penalty on marginal constraints, allowing for efficient gradient-based updates. A dedicated accelerated algorithm achieves an $\epsilon$-approximate solution quickly, providing a scalable method for obtaining sparse transport plans.