| Project/Area Number |
16540131
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
ITO Satoshi The Institute of Statistical Mathematics, Department of Mathematical Analysis and Statistical Inference, Associate Professor, 数理・推論研究系, 助教授 (50232442)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2004: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | robust optimization / min-max problem / cutting plane method / capacity problem / optimization in measure spaces / robust linear programming / infinite programming / optimal control / 国際情報交換 / 中国 / 最適制御問題 / オーストラリア:中国 / 非線形計画 / 半無限計画 / 微分不可能最適化 / 大域的最適化 |
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| Research Abstract |
This research project aims at developing efficient algorithms for a class of robust optimization problem based on its theoretical analysis. Such a class of problem is represented in general as a min-max problem involving uncertainty as parameters. We first studied (1) inexact implementations of some numerical algorithms based on the cutting plane strategy, and finally proposed (2) a bilateral cutting plane framework as an ultimate solution form, whose global convergence is shown for a class of optimization problem formulated in a measure space. Furthermore we tried some refinements of the numerical framework towards its more efficient implementation with better local convergence properties. The practical side of this research project includes (3) some applications to semi-infinite programming especially for digital filter design, (4) a theoretical analysis of the asymptotic behavior of the developed framework when applied to a special case whose decision variable is limited to a class of absolutely continuous measure, and (5) some applications to optimal control problems with state variable inequality constraints. We also investigated, as related topics, (6) a type of path-following algorithm based on continuous descent methods and (7) a numerical analysis of the integration of stiff systems of ordinary differential equations.
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