Towards smarter MILP solvers: A data-driven approach to branch-and-bound

Scavuzzo Montaña, L.V.

Towards smarter MILP solvers: A data-driven approach to branch-and-bound

Doctoral thesis (2024)

Authors

L.V. Scavuzzo Montaña Discrete Mathematics and Optimization

Contributors

K.I. Aardal Discrete Mathematics and Optimization (promotor)

N. Yorke-Smith Algorithmics (copromotor)

Research Group

Discrete Mathematics and Optimization

Machine Learning Integer Programming Branch-and-bound

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Published Date

2024

Language

English

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Research Group

Discrete Mathematics and Optimization

Abstract

The available technology to solve Mixed Integer Linear Programs (MILPs) has experienced dramatic improvements in the past two decades. Pushing this algorithmic progress further is essential for solving even more complex optimization problems that arise in practice. This thesis examines various methods to enhance Branch-and-Bound (B&B) based MILP solvers, focusing on areas such as branching and Machine Learning (ML) assisted rules. Through our analysis of current methodologies and the introduction of novel techniques, this thesis contributes to the development of more efficient and adaptive MILP solvers...

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