Basic Principles of Annealing for Large Scale Non-Linear Optimization

Joachim M. Buhmann and Jan Puzicha
Additional contact information
Joachim M. Buhmann: Rheinische Friedrich-Wilhelm Universität Bonn, Institut für Informatik
Jan Puzicha: University of California, Department of Computer Science

A chapter in Online Optimization of Large Scale Systems, 2001, pp 749-777 from Springer

Abstract: Abstract Computational Annealing, a class of optimization heuristics that are inspired by statistical physics of phase transitions has been demonstrated to be highly effective for large, non-linear combinatorial optimization problems. In many applications in computer vision and pattern recognition one encounters non-linear objective functions with a very large number of discrete and possibly additional continuous variables. Typical cases of such problems are clustering, grouping and image segmentation or assignment problems in motion or stereo analysis or in object recognition. For this type of problems, standard integer programming techniques are not applicable and one has to resort to optimization heuristics that are fast, yet avoid a possibly exponential number of unfavorable local minima. A particularly powerful, generic class of algorithms is provided by simulated or deterministic annealing techniques. Simulated annealing and the Gibbs sampler are discussed first to present the basic concepts; then, the theory of deterministic annealing is presented in great detail and the relation to continuation methods are discussed.

Keywords: Cost Function; Simulated Annealing; Gibbs Sampler; Continuation Method; Factorial Distribution (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-3-662-04331-8_36

Ordering information: This item can be ordered from
http://www.springer.com/9783662043318

DOI: 10.1007/978-3-662-04331-8_36

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().