Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)· 2021· Review
Machine learning for combinatorial optimization: A methodological tour d'horizon
- 1,357citations
- 2021year
Short summary
This paper proposes a methodology to integrate machine learning (ML) with combinatorial optimization (CO) by treating generic optimization problems as data points for learning.
AI-generated from the title and abstract; the full text is not read.
Key points
- Combinatorial optimization problems are hard and typically solved with handcrafted heuristics.
- Machine learning offers a principled way to make decisions in combinatorial optimization.
- A proposed methodology treats generic optimization problems as data points for learning.
- Identifying the relevant problem distribution is crucial for successful ML-driven optimization.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
The authors' abstract, as published at the source. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna), 2021 · DOI ↗
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Field: Industrial and Manufacturing Engineering
Industrial and Manufacturing EngineeringEngineering