The Applied and Numerical Harmonic Analysis (ANHA) book series aims to provide the
engineering, mathematical, and scientific communities with significant developments in …

A common way to quantify the “distance” between measures is via their discrepancy, also
known as maximum mean discrepancy (MMD). Discrepancies are related to Sinkhorn …

Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-
smooth Riesz kernels show a rich structure as singular measures can become absolutely …

The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an
optimization-driven method that has been recently introduced for accelerated 2D MRI using …

Sliced optimal transport, which is basically a Radon transform followed by one-dimensional
optimal transport, became popular in various applications due to its efficient computation. In …

Entropy-regularized optimal transport and its multi-marginal generalization have attracted
increasing attention in various applications, in particular due to efficient Sinkhorn-like …

Most commonly used $ f $-divergences of measures, eg, the Kullback-Leibler divergence,
are subject to limitations regarding the support of the involved measures. A remedy consists …

The aim of this paper is twofold. Based on the geometric Wasserstein tangent space, we first
introduce Wasserstein steepest descent flows. These are locally absolutely continuous …

Gromov–Wasserstein distances are generalization of Wasserstein distances, which are
invariant under distance preserving transformations. Although a simplified version of optimal …

This paper provides results on Wasserstein gradient flows between measures on the real
line. Utilizing the isometric embedding of the Wasserstein space P 2 (R) into the Hilbert …