Deep learning (DL)-based compressed sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most existing …

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 addition, it …

First-order primal-dual methods are appealing for their low memory overhead, fast iterations,
and effective parallelization. However, they are often slow at finding high accuracy solutions …

We study the convergence behaviors of primal–dual hybrid gradient (PDHG) for solving
linear programming (LP). PDHG is the base algorithm of a new general-purpose first-order …

In this paper, we propose and analyze a new matrix-free primal-dual algorithm, called
restarted Halpern primal-dual hybrid gradient (rHPDHG), for solving linear programming …

Most deep learning-based compressed sensing (DCS) algorithms adopt a single neural
network for signal reconstruction and fail to jointly consider the influences of the sampling …

In this paper, we provide an affirmative answer to the long-standing question: Are GPUs
useful in solving linear programming? We present cuPDLP. jl, a GPU implementation of …

In this paper, we introduce an HPR-LP solver, an implementation of a Halpern Peaceman-
Rachford (HPR) method with semi-proximal terms for solving linear programming (LP). The …

The alternating-direction-method-of-multipliers-based (ADMM-based) interior point method,
or ABIP method, is a hybrid algorithm that effectively combines interior point method (IPM) …

We analyze restarted PDHG on totally unimodular linear programs. In particular, we show
that restarted PDHG finds an ϵ-optimal solution in O (H m 1 2.5 nnz (A) log⁡(H m 2/ϵ)) …