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Exploring Complex Landscapes with Classical Monte Carlo

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Quantum Annealing and Other Optimization Methods

Part of the book series: Lecture Notes in Physics ((LNP,volume 679))

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Abstract

Nowadays, the annealing concept is useful in (at least) two different fields, namely Physics and Optimization. The annealing strategy was well known in a physical context when Kirkpatrick, Gellat and Vecchi [1] generalized it to complex Optimization problems. A familiar example (for a physicist) is that of crystal growth: the more slowly you cool a liquid, the better crystal you will obtain when temperature drops beyond the melting point.1 One could say that Nature is trying to solve an Optimization problem: the variables to play with are the atomic positions, while the cost function to be minimized is the potential energy. The configuration that minimizes the cost function is the perfect crystal, and a slow annealing schedule allows Nature to find good crystals.

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  1. Departamento de Física Teórica I, Facultad de Ciencias Físicas, Universidad Complutense, Madrid, 28040, Spain

    Victor Martín-Mayor

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Arnab Das Bikas K. Chakrabarti

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Martín-Mayor, V. Exploring Complex Landscapes with Classical Monte Carlo. In: Das, A., K. Chakrabarti, B. (eds) Quantum Annealing and Other Optimization Methods. Lecture Notes in Physics, vol 679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526216_13

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