187 citations · 208 across the 6 of their papers we have counts for
7 papers
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy
We analyze a stochastic approximation algorithm for decision-dependent problems, wherein the data distribution used by the algorithm evolves along the iterate sequence. The primary…
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…
Optimal Convergence Rates for the Proximal Bundle Method
Mateo Díaz, Benjamin Grimmer
We study convergence rates of the classic proximal bundle method for a variety of nonsmooth convex optimization problems. We show that, without any modification, this algorithm ada…
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
David Applegate, Mateo Díaz, Haihao Lu +1
We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The lit…
The nonsmooth landscape of blind deconvolution
Mateo Díaz
The blind deconvolution problem aims to recover a rank-one matrix from a set of rank-one linear measurements. Recently, Charisopulos et al. introduced a nonconvex nonsmooth formula…
Compressed sensing of data with a known distribution
Mateo Díaz, Mauricio Junca, Felipe Rincón +1
Compressed sensing is a technique for recovering an unknown sparse signal from a small number of linear measurements. When the measurement matrix is random, the number of measureme…
JUNO Conceptual Design Report
T. Adam, F. An, G. An +347
The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km awa…