This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing
machine learning techniques. Mixed Integer Programming is a general technique for …

Complete tree search is a highly effective method for tackling Mixed-Integer Programming
(MIP) problems, and over the years, a plethora of branching heuristics have been introduced …

Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate
bound improvements on the optimal solution. Modern MILP solvers rely on a variety of …

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 …

Primal heuristics are an important component of state-of-the-art codes for mixed integer
programming. In this paper, we focus on primal heuristics that only employ computationally …

A lot of problems arising in Combinatorial Optimization and Operations Research can be
formulated as Mixed Integer Programs (MIP). Although MIP-solving is an NP-hard …

Cutting planes are essential for solving mixed-integer linear problems (MILPs), because
they facilitate bound improvements on the optimal solution value. For selecting cuts, modern …

This paper studies how to design abstractions of large-scale combinatorial optimization
problems that can leverage existing state-of-the-art solvers in general-purpose ways, and …

State-of-the-art solvers for mixed integer programming (MIP) problems are highly
parameterized, and finding parameter settings that achieve high performance for specific …

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 classes …