Recently, machine learning, particularly message-passing graph neural networks (MPNNs),
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …

Learning to optimize is a rapidly growing area that aims to solve optimization problems or
improve existing optimization algorithms using machine learning (ML). In particular, the …

Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …

In this paper, we propose a graph classification approach for automatically determining
whether to use a monolithic or a decomposition-based solution method. In this approach, an …

In an era of digital ubiquity, efficient resource management and decision-making are
paramount across numerous industries. To this end, we present a comprehensive study on …

Large-scale LP problems from industry usually contain much redundancy that severely hurts
the efficiency and reliability of solving LPs, making presolve (ie, the problem simplification …

Cutting planes (cuts) play an important role in solving mixed-integer linear programs
(MILPs), which formulate many important real-world applications. Cut selection heavily …

The simplex method, introduced by Dantzig more than half a century ago, is still to date one
of the most efficient methods for solving large-scale linear programming (LP) problems …

There have been significant efforts devoted to developing advanced mixed-integer linear
programming (MILP) solvers, which are powerful tools for solving various real-world …

Two-stage Stochastic Programming (2SP) is a standard framework for modeling decision-
making problems under uncertainty. While numerous methods exist, solving such problems …