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Regional division and reduction algorithm for minimizing the sum of linear fractional functions

This paper presents a practicable regional division and cut algorithm for minimizing the sum of linear fractional functions over a polyhedron. In the algorithm, by using an equivalent problem (P) of the original problem, the proposed division operation generalizes the usual standard bisection, and t...

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Detalles Bibliográficos
Autores principales: Shen, Pei-Ping, Lu, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862992/
https://www.ncbi.nlm.nih.gov/pubmed/29606840
http://dx.doi.org/10.1186/s13660-018-1651-9
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author Shen, Pei-Ping
Lu, Ting
author_facet Shen, Pei-Ping
Lu, Ting
author_sort Shen, Pei-Ping
collection PubMed
description This paper presents a practicable regional division and cut algorithm for minimizing the sum of linear fractional functions over a polyhedron. In the algorithm, by using an equivalent problem (P) of the original problem, the proposed division operation generalizes the usual standard bisection, and the deleting and reduction operations can cut away a large part of the current investigated region in which the global optimal solution of (P) does not exist. The main computation involves solving a sequence of univariate equations with strict monotonicity. The proposed algorithm is convergent to the global minimum through the successive refinement of the solutions of a series of univariate equations. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm.
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spelling pubmed-58629922018-03-28 Regional division and reduction algorithm for minimizing the sum of linear fractional functions Shen, Pei-Ping Lu, Ting J Inequal Appl Research This paper presents a practicable regional division and cut algorithm for minimizing the sum of linear fractional functions over a polyhedron. In the algorithm, by using an equivalent problem (P) of the original problem, the proposed division operation generalizes the usual standard bisection, and the deleting and reduction operations can cut away a large part of the current investigated region in which the global optimal solution of (P) does not exist. The main computation involves solving a sequence of univariate equations with strict monotonicity. The proposed algorithm is convergent to the global minimum through the successive refinement of the solutions of a series of univariate equations. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm. Springer International Publishing 2018-03-21 2018 /pmc/articles/PMC5862992/ /pubmed/29606840 http://dx.doi.org/10.1186/s13660-018-1651-9 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Shen, Pei-Ping
Lu, Ting
Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title_full Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title_fullStr Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title_full_unstemmed Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title_short Regional division and reduction algorithm for minimizing the sum of linear fractional functions
title_sort regional division and reduction algorithm for minimizing the sum of linear fractional functions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862992/
https://www.ncbi.nlm.nih.gov/pubmed/29606840
http://dx.doi.org/10.1186/s13660-018-1651-9
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