Machine Learning for Optimization-Based Separation: the Case of Mixed-Integer Rounding Cuts

Mixed-Integer Rounding (MIR) cuts are effective at improving the dual bound in Mixed-Integer Linear Programming (MIP). However, in practice, MIR cuts are separated heuristically rather than using optimization as the latter is prohibitively expensive. We present a hybrid cut generation framework in which we train a Machine Learning (ML) model to inform cut generation for … Read more

Neur2BiLO: Neural Bilevel Optimization

Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness.  While exact solvers have been proposed for mixed-integer~linear bilevel optimization, they tend to scale poorly with problem … Read more

Neur2RO: Neural Two-Stage Robust Optimization

Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, … Read more

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

In deterministic optimization, it is typically assumed that all parameters of the problem are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from historical data. A typical predict-then-optimize approach separates predictions and optimization into two stages. Recently, end-to-end predict-then-optimize has become an attractive alternative. In this … Read more

Neur2SP: Neural Two-stage Stochastic Programming

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 applied and studied class of stochastic programming models. Solving 2SPs exactly requires evaluation of an expected value function that is computationally intractable. Additionally, having a mixed-integer linear program (MIP) or a nonlinear … Read more