Combinatorial Optimization: finding an optimal solution from a finite set of solutions
Countless practical optimization problems are, in fact, combinatorial optimization problems: they have an optimal solution that needs to be found amongst a finite set of possible solutions. The aim of combinatorial optimization (CO) is to rapidly and efficiently find such an optimal solution.
CO is related to discrete mathematics, theoretical computer science, applied mathematics, operations research, algorithm theory and computational complexity theory and has important applications in several fields. These include scheduling, production planning, logistics, network design, communication and routing in networks, health care, artificial intelligence, machine learning, auction theory, and software engineering.
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Our most recent peer reviewed publications
Revealed preference theory: an algorithmic outlookEuropean Journal of Operational Research (2019)
Integer programming models for mid-term production planning for high-tech low-volume supply chainsEuropean Journal of Operational Research (2018)
On the number of bases of almost all matroidsCombinatorica (2018)
Computing the chromatic number using graph decompositions via matrix rank26th European Symposium on Algorithms, ESA 2018 (2018)
Testing probabilistic models of choice using column generationComputers & Operations Research (2018)