The current industrial scenario has witnessed the application of several artificial intelligence-
based technologies for mining and processing IoMT-based big data. An emerging …

We provide theoretical analyses for two algorithms that solve the regularized optimal
transport (OT) problem between two discrete probability measures with at most $ n $ atoms …

We study first order methods to compute the barycenter of a probability distribution $ P $
over the space of probability measures with finite second moment. We develop a framework …

Motivated by recent increased interest in optimization algorithms for non-convex
optimization in application to training deep neural networks and other optimization problems …

We study the complexity of approximating the Wasserstein barycenter of $ m $ discrete
measures, or histograms of size $ n $, by contrasting two alternative approaches that use …

We study the complexity of approximating the multimarginal optimal transport (MOT)
distance, a generalization of the classical optimal transport distance, considered here …

Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a
robust variant of the Wasserstein distance. Recent work suggests that this quantity is more …

We consider stochastic convex optimization problems with affine constraints and develop
several methods using either primal or dual approach to solve it. In the primal case, we use …

We study first-order optimization algorithms for computing the barycenter of Gaussian
distributions with respect to the optimal transport metric. Although the objective is …

We study the fixed-support Wasserstein barycenter problem (FS-WBP), which consists in
computing the Wasserstein barycenter of $ m $ discrete probability measures supported on …