Practical large-scale linear programming using primal-dual hybrid gradient
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 …
to the high levels of accuracy that are expected in traditional LP applications. In addition, it …
Transactive energy for EV owners and aggregators: mechanism and algorithms
The increasing penetration of electric vehicles (EVs) brings new flexibility in power grid
operation. As EVs serve the main purpose of transportation, EV owners will charge their …
operation. As EVs serve the main purpose of transportation, EV owners will charge their …
An ADMM-based interior-point method for large-scale linear programming
In this paper, we propose a new framework to implement interior point method (IPM) in order
to solve some very large-scale linear programs (LPs). Traditional IPMs typically use …
to solve some very large-scale linear programs (LPs). Traditional IPMs typically use …
On the geometry and refined rate of primal–dual hybrid gradient for linear programming
H Lu, J Yang - Mathematical Programming, 2024 - Springer
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 …
linear programming (LP). PDHG is the base algorithm of a new general-purpose first-order …
Simple and fast algorithm for binary integer and online linear programming
In this paper, we develop a simple and fast online algorithm for solving a class of binary
integer linear programs (LPs) arisen in the general resource allocation problem. The …
integer linear programs (LPs) arisen in the general resource allocation problem. The …
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
J Lindbäck, Z Wang… - Advances in Neural …, 2024 - proceedings.neurips.cc
We present an efficient algorithm for regularized optimal transport. In contrast toprevious
methods, we use the Douglas-Rachford splitting technique to developan efficient solver that …
methods, we use the Douglas-Rachford splitting technique to developan efficient solver that …
An adaptive primal-dual framework for nonsmooth convex minimization
We propose a new self-adaptive and double-loop smoothing algorithm to solve composite,
nonsmooth, and constrained convex optimization problems. Our algorithm is based on …
nonsmooth, and constrained convex optimization problems. Our algorithm is based on …
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
B Li, L Yang, Y Chen, S Wang, Q Chen, H Mao… - arXiv preprint arXiv …, 2024 - arxiv.org
Solving large-scale linear programming (LP) problems is an important task in various areas
such as communication networks, power systems, finance and logistics. Recently, two …
such as communication networks, power systems, finance and logistics. Recently, two …
A fast and accurate splitting method for optimal transport: Analysis and implementation
VV Mai, J Lindbäck, M Johansson - arXiv preprint arXiv:2110.11738, 2021 - arxiv.org
We develop a fast and reliable method for solving large-scale optimal transport (OT)
problems at an unprecedented combination of speed and accuracy. Built on the celebrated …
problems at an unprecedented combination of speed and accuracy. Built on the celebrated …
Learning product graphs underlying smooth graph signals
Real-world data is often times associated with irregular structures that can analytically be
represented as graphs. Having access to this graph, which is sometimes trivially evident …
represented as graphs. Having access to this graph, which is sometimes trivially evident …