3 citations · 3 across the 8 of their papers we have counts for
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Computing All Optimal Partial -Wasserstein Matchings on the Line
Sebastian Angrick, Jacobus Conradi, Mónika Csikós +5
For , the -Wasserstein distance measures the minimum cost of transporting probability mass between distributions, where moving unit mass between two points costs the $p…
Geometric Bipartite Matching Based Exact Algorithms for Server Problems
Sharath Raghvendra, Pouyan Shirzadian, Rachita Sowle
For any given metric space, obtaining an offline optimal solution to the classical -server problem can be reduced to solving a minimum-cost partial bipartite matching between tw…
Fast and Accurate Approximations of the Optimal Transport in Semi-Discrete and Discrete Settings
Pankaj K. Agarwal, Sharath Raghvendra, Pouyan Shirzadian +1
Given a -dimensional continuous (resp. discrete) probability distribution and a discrete distribution , the semi-discrete (resp. discrete) Optimal Transport (OT) problem…
Improved Approximate Rips Filtrations with Shifted Integer Lattices and Cubical Complexes
Aruni Choudhary, Michael Kerber, Sharath Raghvendra
Rips complexes are important structures for analyzing topological features of metric spaces. Unfortunately, generating these complexes is expensive because of a combinatorial explo…
An Time -Approximation Algorithm for RMS Matching in a Plane
Nathaniel Lahn, Sharath Raghvendra
The 2-Wasserstein distance (or RMS distance) is a useful measure of similarity between probability distributions that has exciting applications in machine learning. For discrete di…
A Weighted Approach to the Maximum Cardinality Bipartite Matching Problem with Applications in Geometric Settings
Nathaniel Lahn, Sharath Raghvendra
We present a weighted approach to compute a maximum cardinality matching in an arbitrary bipartite graph. Our main result is a new algorithm that takes as input a weighted bipartit…