7 citations · 11 across the 4 of their papers we have counts for
6 papers
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…
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…
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…