Parallel Regularized Multiple-criteria Linear Programming

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dc.contributor	Barcelona Supercomputing Center
dc.contributor.author	Qi, Zhinquan
dc.contributor.author	Alexandrov, Vassil
dc.contributor.author	Shi, Yong
dc.contributor.author	Tian, Yingjie
dc.date	2014
dc.identifier.citation	Qi, Zhinquan [et al.]. Parallel Regularized Multiple-criteria Linear Programming. "Procedia Computer Science", 2014, vol. 31, p. 58-65.
dc.identifier.citation	1877-0509
dc.identifier.citation	10.1016/j.procs.2014.05.245
dc.identifier.uri	http://hdl.handle.net/2117/87017
dc.language.iso	eng
dc.publisher	Elsevier
dc.relation	http://www.sciencedirect.com/science/article/pii/S1877050914004220
dc.rights	Attribution-NonCommercial-NoDerivs 4.0 International License
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica
dc.subject	Parallel algorithms
dc.subject	Linear programming
dc.subject	PRMCLP
dc.subject	Parallel algorithm
dc.subject	Data mining
dc.subject	Algorismes paral·lels
dc.title	Parallel Regularized Multiple-criteria Linear Programming
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	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.
dc.description.abstract	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).
dc.description.abstract	Peer Reviewed

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