2011 Fiscal Year Final Research Report
Development of Fast Solution Methods based on Sequential Partial Optimization for Large Scale Convex Programming Problems
Project/Area Number |
21560068
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Engineering fundamentals
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Research Institution | Kyushu University |
Principal Investigator |
TAKAHASHI Norikazu 九州大学, 大学院・システム情報科学研究院, 准教授 (60284551)
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Project Period (FY) |
2009 – 2011
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Keywords | 凸計画問題 / 逐次部分最適化 / 非負行列因子分解 / ニューラルネットワーク / 最大流問題 / 複雑ネットワーク / クラスター係数 / マルチエージェントネットワーク |
Research Abstract |
We developed sequential partial optimization algorithms for fundamental continuous convex programming problems such as linear programs and convex quadratic programs, and evaluated their characteristics and efficiency through global convergence analysis and numerical experiments. We also carried out theoretical analysis and development of efficient algorithms for various optimization problems arising in the field of information and communication technology. To be more specific, we studied research issues such as efficient computation methods for nonnegative matrix factorization, optimal design methods for recurrent neural networks, maximizing clustering coefficient of complex networks, and optimization of the topology of multi-agent networks for fast consensus, and obtained many important results.
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[Presentation]2010
Author(s)
Norikazu Takahashi, Global Asymptotic Stability Analysis of Nonlinear Circuits for Solving the Maximum Flow Problem
Organizer
2010 International Symposium on Nonlinear Theory and its Applications
Place of Presentation
Krakow, Poland
Year and Date
2010-09-07
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[Presentation]2009
Author(s)
Yuta Kobayashi, Norikazu Takahashi, A Two-Phase Decomposition Algorithm for Solving Convex Quadratic Programming Problems
Organizer
2009 International Symposium on Nonlinear Theory and its Applications
Place of Presentation
Sapporo, Japan
Year and Date
2009-10-21
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