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Physical Optimization applies a set of Optimization (minimization) methods motivated by physical processes to general optimization problems
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These include simulated annealing, neural networks, deterministic annealing, simulated tempering and genetic algorithms
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We look at general TSP, clustering in physical spaces, track finding, navigation, school class scheduling, Random field Ising Models and data decomposition and other computing optimization problems
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We discuss when methods such as neural networks are effective
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