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Dagstuhl Seminar 24441

Machine Learning Augmented Algorithms for Combinatorial Optimization Problems

( Oct 27 – Oct 31, 2024 )

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Combinatorial optimisation problems arise naturally in a multitude of crucial applications, ranging from business analytics, engineering, supply-chain optimisation, transportation, bioinformatics etc. In recent years, motivated by the success of machine learning in diverse fields, researchers have explored if learning techniques can be used to efficiently solve combinatorial optimisation problems. This is challenging because these problems have highly correlated decision variables and the correlations are long-range with very little spatial or temporal coherence. As a result, the end-to-end learning systems that take the problem instance as an input and produce the optimal solution as an output often do not generalise well to instances of larger sizes and from a different input distribution. Experts in this area have advocated for using machine learning in combination with current combinatorial optimisation algorithms to benefit from the theoretical guarantees and state-of-the-art algorithms already available.

The discussion in this Dagstuhl Seminar will focus on how best to combine the machine learning techniques with algorithmic insights and optimisation solvers to solve larger and harder instances of combinatorial optimisation problems arising from real-world applications. These discussions are expected to accelerate the pace of research in this area and build collaborations and synergies between the researchers working in the areas of algorithm design and engineering, combinatorial optimisation, and machine learning.

The seminar will provide a forum to discuss topics at the intersection of combinatorial optimisation, algorithm engineering, and machine learning:

  • Learning-augmented algorithms and data structures
  • Going beyond worst-case analysis using algorithms with predictions
  • Machine learning augmented optimisation solvers
  • Smart predict + optimise systems for applications where optimisation decisions are taken on data that is predicted using machine learning techniques
  • Integrating algorithmic insights into machine learning techniques for solving optimisation problems.
Copyright Deepak Ajwani, Bistra Dilkina, Tias Guns, and Ulrich Carsten Meyer

  • Data Structures and Algorithms
  • Discrete Mathematics
  • Machine Learning

  • Combinatorial Optimisation
  • Algorithm Engineering
  • Machine Learning
  • Constraint Solvers