Convergence Results for a Class of Time-Varying Simulated Annealing Algorithms

Mathieu Gerber, Luke Bornn

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
250 Downloads (Pure)


We provide a set of conditions which ensure the almost sure convergence of a class of simulated annealing algorithms on a bounded set X ⊂ Rd based on a time-varying Markov kernel. The class of algorithms considered in this work encompasses the one studied in Belisle (1992) and Yang (2000) as well as its derandomized version recently proposed by Gerber and Bornn (2016). To the best of our knowledge, the results we derive are the first examples of almost sure convergence results for simulated annealing based on a time-varying kernel. In addition, the assumptions on the Markov kernel and on the cooling schedule have the advantage of being trivial to verify in practice.
Original languageEnglish
Number of pages22
JournalStochastic Processes and their Applications
Early online date19 Jul 2017
Publication statusE-pub ahead of print - 19 Jul 2017


  • Digital sequences
  • Global optimization
  • Simmulated annealing


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