Scale Invariance Properties in the Simulated Annealing Algorithm

M. A. Fleischer () and S. H. Jacobson ()
Additional contact information
M. A. Fleischer: University of Maryland
S. H. Jacobson: University of Illinois at Urbana-Champaign

Methodology and Computing in Applied Probability, 2002, vol. 4, issue 3, 219-241

Abstract: Abstract The Boltzmann distribution used in the steady-state analysis of the simulated annealing algorithm gives rise to several scale invariant properties. Scale invariance is first presented in the context of parallel independent processors and then extended to an abstract form based on lumping states together to form new aggregate states. These lumped or aggregate states possess all of the mathematical characteristics, forms and relationships of states (solutions) in the original problem in both first and second moments. These scale invariance properties therefore permit new ways of relating objective function values, conditional expectation values, stationary probabilities, rates of change of stationary probabilities and conditional variances. Such properties therefore provide potential applications in analysis, statistical inference and optimization. Directions for future research that take advantage of scale invariance are also discussed.

Keywords: scale invariance; self-similarity; simulated annealing; Markov chains; branch and probability bound; nested partition algorithms (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1023/A:1022596700741 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:4:y:2002:i:3:d:10.1023_a:1022596700741

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1023/A:1022596700741

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().