1.3k citations · 2.4k across the 25 of their papers we have counts for
26 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…
JuMP 1.0: Recent improvements to a modeling language for mathematical optimization
Miles Lubin, Oscar Dowson, Joaquim Dias Garcia +3
JuMP is an algebraic modeling language embedded in the Julia programming language. JuMP allows users to model optimization problems of a variety of kinds, including linear programm…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
ChunJun Cao
In this note, I review a recent approach to quantum gravity that "gravitizes" quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insigh…
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…
Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness
David Applegate, Oliver Hinder, Haihao Lu +1
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 s…
Shapes and recession cones in mixed-integer convex representability
Ilias Zadik, Miles Lubin, Juan Pablo Vielma
Mixed-integer convex representable (MICP-R) sets are those sets that can be represented exactly through a mixed-integer convex programming formulation. Following up on recent work…
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…
A generic adaptive restart scheme with applications to saddle point algorithms
Oliver Hinder, Miles Lubin
We provide a simple and generic adaptive restart scheme for convex optimization that is able to achieve worst-case bounds matching (up to constant multiplicative factors) optimal r…
MathOptInterface: a data structure for mathematical optimization problems
Benoit Legat, Oscar Dowson, Joaquim Dias Garcia +1
We introduce MathOptInterface, an abstract data structure for representing mathematical optimization problems based on combining pre-defined functions and sets. MathOptInterface is…
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
Aditya Paliwal, Felix Gimeno, Vinod Nair +4
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based wor…
Outer Approximation With Conic Certificates For Mixed-Integer Convex Problems
Chris Coey, Miles Lubin, Juan Pablo Vielma
A mixed-integer convex (MI-convex) optimization problem is one that becomes convex when all integrality constraints are relaxed. We present a branch-and-bound LP outer approximatio…
Chance Constraints for Improving the Security of AC Optimal Power Flow
Miles Lubin, Yury Dvorkin, Line Roald
This paper presents a scalable method for improving the solutions of AC Optimal Power Flow (AC OPF) with respect to deviations in predicted power injections from wind and other unc…
PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations
Carleton Coffrin, Russell Bent, Kaarthik Sundar +2
In recent years, the power system research community has seen an explosion of novel methods for formulating and solving power network optimization problems. These emerging methods…
Mixed-integer convex representability
Miles Lubin, Juan Pablo Vielma, Ilias Zadik
Motivated by recent advances in solution methods for mixed-integer convex optimization (MICP), we study the fundamental and open question of which sets can be represented exactly a…
Mixed-integer convex representability
Miles Lubin, Ilias Zadik, Juan Pablo Vielma
We consider the question of which nonconvex sets can be represented exactly as the feasible sets of mixed-integer convex optimization problems. We state the first complete characte…
Forward-Mode Automatic Differentiation in Julia
Jarrett Revels, Miles Lubin, Theodore Papamarkou
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently develop…
Polyhedral approximation in mixed-integer convex optimization
Miles Lubin, Emre Yamangil, Russell Bent +1
Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances i…
Unit Commitment with N-1 Security and Wind Uncertainty
Kaarthik Sundar, Harsha Nagarajan, Miles Lubin +4
As renewable wind energy penetration rates continue to increase, one of the major challenges facing grid operators is the question of how to control transmission grids in a reliabl…
Extended Formulations in Mixed-integer Convex Programming
Miles Lubin, Emre Yamangil, Russell Bent +1
We present a unifying framework for generating extended formulations for the polyhedral outer approximations used in algorithms for mixed-integer convex programming (MICP). Extende…
JuMP: A Modeling Language for Mathematical Optimization
Iain Dunning, Joey Huchette, Miles Lubin
JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and no…
Two-sided linear chance constraints and extensions
Miles Lubin, Daniel Bienstock, Juan Pablo Vielma
We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to mode…
Extended Formulations in Mixed Integer Conic Quadratic Programming
Juan Pablo Vielma, Iain Dunning, Joey Huchette +1
In this paper we consider the use of extended formulations in LP-based algorithms for mixed integer conic quadratic programming (MICQP). Extended formulations have been used by Vie…
Uncertainty Sets For Wind Power Generation
Yury Dvorkin, Miles Lubin, Scott Backhaus +1
As penetration of wind power generation increases, system operators must account for its stochastic nature in a reliable and cost-efficient manner. These conflicting objectives can…