Presentation 2009-11-11
Experimental Evaluation of a Two-Phase Sequential Partial Optimization Algorithm for Convex Quadratic Programming Problems
Yuta KOBAYASHI, Norikazu TAKAHASHI,
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Abstract(in English) A convex quadratic programming (QP) problem is an optimization problem in which a convex quadratic function is minimized subject to some linear constraints. QP problems play important roles in many fields. Recently, the authors have proposed a two-phase sequential partial optimization algorithm for solving general convex QP problems, which is based on the decomposition method for the training of support vector machines. In this report, we describe the algorithm in detail and present experimental results to evaluate its performance.
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Keyword(in English) convex quadratic programming problem / sequential partial optimization / support vector machine / Scilab
Paper # NLP2009-97
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Committee NLP
Conference Date 2009/11/4(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Experimental Evaluation of a Two-Phase Sequential Partial Optimization Algorithm for Convex Quadratic Programming Problems
Sub Title (in English)
Keyword(1) convex quadratic programming problem
Keyword(2) sequential partial optimization
Keyword(3) support vector machine
Keyword(4) Scilab
1st Author's Name Yuta KOBAYASHI
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Norikazu TAKAHASHI
2nd Author's Affiliation Faculty of Information Science and Electrical Engineering, Kyushu University
Date 2009-11-11
Paper # NLP2009-97
Volume (vol) vol.109
Number (no) 269
Page pp.pp.-
#Pages 6
Date of Issue