Solving Large Scale Optimization Problems via Grid and Cluster Computing
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- Fujisawa Katsuki
- Tokyo Denki University
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- Kojima Masakazu
- Tokyo Institute of Technology
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- Takeda Akiko
- Tokyo Institute of Technology
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- Yamashita Makoto
- Kanagawa University
書誌事項
- タイトル別名
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- SOLVING LARGE SCALE OPTIMIZATION PROBLEMS VIA GRID AND CLUSTER COMPUTING(<Special Issue>Network Design, Control and Optimization)
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抄録
Solving large scale optimization problems requires a huge amount of computational power. The size of optimization problems that can be solved on a few CPUs has been limited due to a lack of computational power. Grid and cluster computing has received much attention as a powerful and inexpensive way of solving large scale optimization problems that an existing single-unit CPU cannot process. The aim of this paper is to show that grid and cluster computing provides tremendous power to optimization methods. The methods that this paper picks up are a successive convex relaxation method for quadratic optimization problems, a polyhedral homotopy method for polynomial systems of equations and a primal-dual interiorpoint method for semidefinite programs. Their parallel implementations on grids and clusters together with numerical results are reported. The paper also mentions a grid portal system for optimization problems briefly.
収録刊行物
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- 日本オペレーションズ・リサーチ学会論文誌
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日本オペレーションズ・リサーチ学会論文誌 47 (4), 265-274, 2004
公益社団法人 日本オペレーションズ・リサーチ学会
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詳細情報 詳細情報について
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- CRID
- 1390001204110678656
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- NII論文ID
- 110001185975
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- NII書誌ID
- AA00703935
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- ISSN
- 21888299
- 04534514
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- NDL書誌ID
- 7192163
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可