L.V. Scavuzzo Montaña
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Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.
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Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.
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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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...
Branch-and-bound for integer optimization typically uses single-variable disjunctions. Enumerative methods for integer optimization with theoretical guarantees use a non-binary search tree with general disjunctions based on lattice structure. These disjunctions are expensive to compute and challenging to implement. Here we compare two lattice reformulations that can be used to heuristically obtain general disjunctions in the original space, we develop a new lattice-based variant, and compare the derived disjunctions computationally with those produced by the algorithm of Lovász and Scarf.
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Branch-and-bound for integer optimization typically uses single-variable disjunctions. Enumerative methods for integer optimization with theoretical guarantees use a non-binary search tree with general disjunctions based on lattice structure. These disjunctions are expensive to compute and challenging to implement. Here we compare two lattice reformulations that can be used to heuristically obtain general disjunctions in the original space, we develop a new lattice-based variant, and compare the derived disjunctions computationally with those produced by the algorithm of Lovász and Scarf.