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Título:
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Parallel Regularized Multiple-criteria Linear Programming
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Autor/a:
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Qi, Zhinquan; Alexandrov, Vassil; Shi, Yong; Tian, Yingjie
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Otros autores:
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Barcelona Supercomputing Center |
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Abstract:
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In this paper, we proposed a new parallel algorithm: Parallel Regularized Multiple-Criteria Linear Programming (PRMCLP) to overcome the computing and storage requirements increased rapidly with the number of training samples. Firstly, we convert RMCLP model into a unconstrained optimization problem, and then split it into several parts, and each part is computed by a single processor. After that, we analyze each part's result for next cycle going. By doing this, we are be able to obtain the final optimization solution of the whole classification problem. All experiments in public datasets show that our method greatly increases the training speed of RMCLP in the help of multiple processors. |
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Abstract:
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This work has been partially supported by China Postdoctoral Science Foundation under Grant No.2013M530702, and grants from National Natural Science Foundation of China(NO.11271361), key project of National Natural Science Foundation of China(NO.71331005), Major International (Regional) Joint Research Project(NO.71110107026),
and the Ministry of water resources’ special funds for scientific research on public causes (No. 201301094). |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Enginyeria electrònica
-Parallel algorithms
-Linear programming
-PRMCLP
-Parallel algorithm
-Data mining
-Algorismes paral·lels |
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Derechos:
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Attribution-NonCommercial-NoDerivs 4.0 International License
https://creativecommons.org/licenses/by-nc-nd/4.0/
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Tipo de documento:
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Artículo - Versión publicada
Artículo |
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Editor:
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Elsevier
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Compartir:
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