22 citations · 50 across the 9 of their papers we have counts for
11 papers
PDLP: A Practical First-Order Method for Large-Scale Linear Programming
David Applegate, Mateo Díaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) designed to solve large-scale LP problems. PDLP is based on the primal-dual hybrid gradient (PDHG) metho…
Stars: Tera-Scale Graph Building for Clustering and Graph Learning
CJ Carey, Jonathan Halcrow, Rajesh Jayaram +3
A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, c…
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
David Applegate, Mateo Díaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addit…
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we…
Massively Parallel Computation via Remote Memory Access
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the Massively Parallel Computation (MPC) model. At a high level, the AMPC model stre…
Bernstein-like Concentration and Moment Inequalities for Polynomials of Independent Random Variables: Multilinear Case
Warren Schudy, Maxim Sviridenko
We show that the probability that a multilinear polynomial of independent random variables exceeds its mean by is at most for sufficiently small $…