Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

State-of-the-art Mixed Integer Linear Programming (MILP) solvers combine systematic tree
search with a plethora of hard-coded heuristics, such as branching rules. While approaches …

In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …

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 …

Mixed-integer linear programming (MILP) has become a cornerstone of operations research.
This is driven by the enhanced efficiency of modern solvers, which can today find globally …

We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …

Recent work has shown that not only decision trees (DTs) may not be interpretable but also
proposed a polynomial-time algorithm for computing one PI-explanation of a DT. This paper …

Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of
great importance for many practical applications. In this respect, the refinement heuristic …