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Machine Learning for Cutting Planes in Integer Programming: A Survey
Arnaud Deza, Elias B. Khalil
We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite the availability of various cla…
Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto
Weimin Huang, Elias B. Khalil
The concept of walkable urban development has gained increased attention due to its public health, economic, and environmental sustainability benefits. Unfortunately, land zoning a…
Multi-Task Predict-then-Optimize
Bo Tang, Elias B. Khalil
The predict-then-optimize framework arises in a wide variety of applications where the unknown cost coefficients of an optimization problem are first predicted based on contextual…
PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
Bo Tang, Elias B. Khalil
In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be est…
A Deep Reinforcement Learning Framework For Column Generation
Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil +2
Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-sca…
Neur2SP: Neural Two-Stage Stochastic Programming
Justin Dumouchelle, Rahul Patel, Elias B. Khalil +1
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used cl…