1.0 / December 15, 2015
(5.0/5) (2)
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Description

This app is developed to determine the unconstrained minimum of afunction of several variables without calculating theirderivatives. The app is derived from Simulated Annealing (SA)algorithm recently introduced in combinatorial optimization. Thealgorithm is an iterative random search procedure with adaptivemoves along the coordinate directions. It permits uphill movesunder the control of a probabilistic criterion, thus, tending toavoid the first local minima encountered.The app has been testedagainst Nelder and Mead simplex method and against the basic SAalgorithm, the test functions were Rosenbrock valley in twodimension, Powell's quartic function in 4D, and multiminimafunction in 6 and 10 dimensions.The app proved to be more reliablethan the others, being always able to find the optimum, or at leasta point very close to it. The number of iterations at eachtemperature during the cooling was able to determine. The coolingfactor in the SA cooling schedule was found to be a variable andnot a constant as proposed by many other authors.

App Information Scudd PSA

  • App Name
    Scudd PSA
  • Package Name
    projects.purgren.tech.scuddpsa
  • Updated
    December 15, 2015
  • File Size
    2.6M
  • Requires Android
    Android 2.2 and up
  • Version
    1.0
  • Developer
    Felix Siaw-Yeboah
  • Installs
    50+
  • Price
    Free
  • Category
    Education
  • Developer
    Silver House First Floor Room 15 O
  • Google Play Link

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This app is developed to determine the unconstrained minimum of afunction of several variables without calculating theirderivatives. The app is derived from Simulated Annealing (SA)algorithm recently introduced in combinatorial optimization. Thealgorithm is an iterative random search procedure with adaptivemoves along the coordinate directions. It permits uphill movesunder the control of a probabilistic criterion, thus, tending toavoid the first local minima encountered.The app has been testedagainst Nelder and Mead simplex method and against the basic SAalgorithm, the test functions were Rosenbrock valley in twodimension, Powell's quartic function in 4D, and multiminimafunction in 6 and 10 dimensions.The app proved to be more reliablethan the others, being always able to find the optimum, or at leasta point very close to it. The number of iterations at eachtemperature during the cooling was able to determine. The coolingfactor in the SA cooling schedule was found to be a variable andnot a constant as proposed by many other authors.
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