R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing

This paper discusses using R-chaosoptimiser, an R language package for nonlinear optimisation based on gradient techniques and chaos optimisation algorithms. Its implementation was based on three building blocks which could be executed alone or un combination: the first carrier wave algorithm, the c...

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Main Author: Juan David Velásquez H.
Format: Article
Language:English
Published: Universidad Nacional de Colombia 2011-09-01
Series:Ingeniería e Investigación
Subjects:
Online Access:https://revistas.unal.edu.co/index.php/ingeinv/article/view/26383
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spelling doaj-3c49f3ff8ff5487a9cc43ea084c6a0182020-11-25T02:15:33ZengUniversidad Nacional de ColombiaIngeniería e Investigación0120-56092248-87232011-09-01313505523478R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computingJuan David Velásquez H.0Universidad Nacional de ColombiaThis paper discusses using R-chaosoptimiser, an R language package for nonlinear optimisation based on gradient techniques and chaos optimisation algorithms. Its implementation was based on three building blocks which could be executed alone or un combination: the first carrier wave algorithm, the chaos-based cyclical coordinate search method and the second wave carrier algorithm. Using chaos optimisation algorithms allows the tool to break away from local optimal points and converge towards an overall optimum inside a predefined search domain. Within the previous components, a user would be specifying the BFGS algorithm for refining the current best solution. Using the BFGS algorithm is not mandatory, so that its implementation was able to optimise problems having objective function discontinuities. However, the BFGS algorithm is a powerful local search method, meaning that it is used to exploit current knowledge about an objective function for improving a current solution; an explanatory example is presented.https://revistas.unal.edu.co/index.php/ingeinv/article/view/26383optimisationR languagegradient-based methodchaosalgorithm
collection DOAJ
language English
format Article
sources DOAJ
author Juan David Velásquez H.
spellingShingle Juan David Velásquez H.
R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
Ingeniería e Investigación
optimisation
R language
gradient-based method
chaos
algorithm
author_facet Juan David Velásquez H.
author_sort Juan David Velásquez H.
title R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
title_short R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
title_full R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
title_fullStr R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
title_full_unstemmed R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing
title_sort r-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in r language for statistical computing
publisher Universidad Nacional de Colombia
series Ingeniería e Investigación
issn 0120-5609
2248-8723
publishDate 2011-09-01
description This paper discusses using R-chaosoptimiser, an R language package for nonlinear optimisation based on gradient techniques and chaos optimisation algorithms. Its implementation was based on three building blocks which could be executed alone or un combination: the first carrier wave algorithm, the chaos-based cyclical coordinate search method and the second wave carrier algorithm. Using chaos optimisation algorithms allows the tool to break away from local optimal points and converge towards an overall optimum inside a predefined search domain. Within the previous components, a user would be specifying the BFGS algorithm for refining the current best solution. Using the BFGS algorithm is not mandatory, so that its implementation was able to optimise problems having objective function discontinuities. However, the BFGS algorithm is a powerful local search method, meaning that it is used to exploit current knowledge about an objective function for improving a current solution; an explanatory example is presented.
topic optimisation
R language
gradient-based method
chaos
algorithm
url https://revistas.unal.edu.co/index.php/ingeinv/article/view/26383
work_keys_str_mv AT juandavidvelasquezh rchaosoptimiseranoptimiserforunconstrainedglobalnonlinearoptimisationwritteninrlanguageforstatisticalcomputing
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