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An accelerating algorithm for globally solving nonconvex quadratic programming
To globally solve a nonconvex quadratic programming problem, this paper presents an accelerating linearizing algorithm based on the framework of the branch-and-bound method. By utilizing a new linear relaxation approach, the initial quadratic programming problem is reduced to a sequence of linear re...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061662/ https://www.ncbi.nlm.nih.gov/pubmed/30137906 http://dx.doi.org/10.1186/s13660-018-1764-1 |
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author | Ge, Li Liu, Sanyang |
author_facet | Ge, Li Liu, Sanyang |
author_sort | Ge, Li |
collection | PubMed |
description | To globally solve a nonconvex quadratic programming problem, this paper presents an accelerating linearizing algorithm based on the framework of the branch-and-bound method. By utilizing a new linear relaxation approach, the initial quadratic programming problem is reduced to a sequence of linear relaxation programming problems, which is used to obtain a lower bound of the optimal value of this problem. Then, by using the deleting operation of the investigated regions, we can improve the convergent speed of the proposed algorithm. The proposed algorithm is proved to be convergent, and some experiments are reported to show higher feasibility and efficiency of the proposed algorithm. |
format | Online Article Text |
id | pubmed-6061662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-60616622018-08-09 An accelerating algorithm for globally solving nonconvex quadratic programming Ge, Li Liu, Sanyang J Inequal Appl Research To globally solve a nonconvex quadratic programming problem, this paper presents an accelerating linearizing algorithm based on the framework of the branch-and-bound method. By utilizing a new linear relaxation approach, the initial quadratic programming problem is reduced to a sequence of linear relaxation programming problems, which is used to obtain a lower bound of the optimal value of this problem. Then, by using the deleting operation of the investigated regions, we can improve the convergent speed of the proposed algorithm. The proposed algorithm is proved to be convergent, and some experiments are reported to show higher feasibility and efficiency of the proposed algorithm. Springer International Publishing 2018-07-16 2018 /pmc/articles/PMC6061662/ /pubmed/30137906 http://dx.doi.org/10.1186/s13660-018-1764-1 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 Ge, Li Liu, Sanyang An accelerating algorithm for globally solving nonconvex quadratic programming |
title | An accelerating algorithm for globally solving nonconvex quadratic programming |
title_full | An accelerating algorithm for globally solving nonconvex quadratic programming |
title_fullStr | An accelerating algorithm for globally solving nonconvex quadratic programming |
title_full_unstemmed | An accelerating algorithm for globally solving nonconvex quadratic programming |
title_short | An accelerating algorithm for globally solving nonconvex quadratic programming |
title_sort | accelerating algorithm for globally solving nonconvex quadratic programming |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061662/ https://www.ncbi.nlm.nih.gov/pubmed/30137906 http://dx.doi.org/10.1186/s13660-018-1764-1 |
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