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Ergodic Annealing

Author

Listed:
  • Carlo Baldassi
  • Fabio Maccheroni
  • Massimo Marinacci
  • Marco Pirazzini

Abstract

Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Methods for the solution of NP-hard 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.

Suggested Citation

  • Carlo Baldassi & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020. "Ergodic Annealing," Papers 2008.00234, arXiv.org.
    • Handle: RePEc:arx:papers:2008.00234
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    File URL: http://arxiv.org/pdf/2008.00234
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    References listed on IDEAS

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    1. Lawrence J. Osborne & Billy E. Gillett, 1991. "A Comparison of Two Simulated Annealing Algorithms Applied to the Directed Steiner Problem on Networks," INFORMS Journal on Computing, INFORMS, vol. 3(3), pages 213-225, August.
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