Relative frequencies of generalized simulated annealing

Jan Hannig, Edwin K P Chong, Sanjeev R. Kulkarni

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


We consider a class of nonhomogeneous Markov chains arising in simulated annealing and related stochastic search algorithms. Using only elementary first principles, we analyze the convergence and rate of convergence of the relative frequencies of visits to states in the Markov chain. We describe in detail three examples, including the standard simulated annealing algorithm, to show how our framework applies to specific stochastic search algorithms - these examples have not previously been recognized to be sufficiently similar to share common analytical grounds. Our analysis, though elementary, provides the strongest sample path convergence results to date for simulated annealing-type Markov chains. Our results serve to illustrate that by taking a purely sample path view, surprisingly strong statements can be made using only relatively elementary tools.

Original languageAmerican English
Pages (from-to)199-216
Number of pages18
JournalMathematics of Operations Research
Issue number1
StatePublished - Feb 2006

ASJC Scopus subject areas

  • General Mathematics
  • Computer Science Applications
  • Management Science and Operations Research


  • Coupling
  • Randomized optimization
  • Relative frequency
  • Simulated annealing


Dive into the research topics of 'Relative frequencies of generalized simulated annealing'. Together they form a unique fingerprint.

Cite this