Homotopy optimization methods for global optimization.

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We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating ... continued below

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33 p.

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Dunlavy, Daniel M. & O'Leary, Dianne P. (University of Maryland, College Park, MD) December 1, 2005.

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We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating performance of HOM and HOPE.

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33 p.

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  • Report No.: SAND2005-7495
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/876373 | External Link
  • Office of Scientific & Technical Information Report Number: 876373
  • Archival Resource Key: ark:/67531/metadc879586

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Office of Scientific & Technical Information Technical Reports

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  • December 1, 2005

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Nov. 29, 2016, 12:55 p.m.

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Dunlavy, Daniel M. & O'Leary, Dianne P. (University of Maryland, College Park, MD). Homotopy optimization methods for global optimization., report, December 1, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc879586/: accessed November 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.