
Diffuse optical tomography by simulated annealing via a spin Hamiltonian
The inverse problem of diffuse optical tomography is solved by the Marko...
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Simulated Annealing Algorithm for Graph Coloring
The goal of this Random Walks project is to code and experiment the Mark...
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Simulated Annealing: Rigorous finitetime guarantees for optimization on continuous domains
Simulated annealing is a popular method for approaching the solution of ...
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An approximate ItôSDE based simulated annealing algorithm for multivariate design optimization problems
This research concerns design optimization problems involving numerous d...
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Using Simulated Annealing to Calculate the Trembles of Trembling Hand Perfection
Within the literature on noncooperative game theory, there have been a ...
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An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing
Graph matching is one of the most important problems in graph theory and...
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Optimization of population annealing Monte Carlo for largescale spinglass simulations
Population annealing Monte Carlo is an efficient sequential algorithm fo...
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Ergodic Annealing
Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Methods for the solution of NPhard optimization problems in which the cost function is known. Here, by replacing the Metropolis engine of Simulated Annealing with a reinforcement learning variation – that we call Macau Algorithm – we show that the Simulated Annealing heuristic can be very effective also when the cost function is unknown and has to be learned by an artificial agent.
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