Global energy minimization: A transformation approach

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1 Citation (Scopus)

Abstract

This paper addresses the problem of minimizing an energy function by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporatetransformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for a class of neural network learning problems, which possess much larger parameter spaces.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 3rd International Workshop, EMMCVPR 2001, Proceedings
EditorsAnil K. Jain, Mario Figueiredo, Josiane Zerubia
PublisherSpringer Verlag
Pages391-406
Number of pages16
ISBN (Print)3540425233, 9783540425236
DOIs
Publication statusPublished - 2001 Jan 1
Event3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001 - Sophia Antipolis, France
Duration: 2001 Sep 32001 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2134
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001
CountryFrance
CitySophia Antipolis
Period01/9/301/9/5

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Toh, K. A. (2001). Global energy minimization: A transformation approach. In A. K. Jain, M. Figueiredo, & J. Zerubia (Eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition - 3rd International Workshop, EMMCVPR 2001, Proceedings (pp. 391-406). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2134). Springer Verlag. https://doi.org/10.1007/3-540-44745-8_26