Summary
Combinatorial optimization deals with algorithms for finding extrema of functions subject to a (possibly large) number of constraints. Bayesian inference also requires averages over such extrema. In this chapter we show how simple dynamic systems like billiards can be used to find solutions for such problems. The topics covered are linear and quadratic programming, classification, and Bayesian mixture problems.
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Ruján, P. (2002). Combinatorial Optimization and High Dimensional Billiards. In: Computational Statistical Physics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04804-7_3
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DOI: https://doi.org/10.1007/978-3-662-04804-7_3
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