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Parallel Regularized Multiple-criteria Linear Programming
Qi, Zhinquan; Alexandrov, Vassil; Shi, Yong; Tian, Yingjie
Barcelona Supercomputing Center
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.
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).
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria electrònica
Parallel algorithms
Linear programming
PRMCLP
Parallel algorithm
Data mining
Algorismes paral·lels
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
Artículo
Elsevier
         

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