| Project/Area Number |
60550264
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
計算機工学
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| Research Institution | Kyoto University (1986)
Toyohashi University of Technology (1985) |
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Principal Investigator |
IBARAKI Toshihide Faculty of Engineering, Kyoto University; Professor, 工学部, 教授 (50026192)
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| Co-Investigator(Kenkyū-buntansha) |
OHNISHI Masamitsu Faculty of Engineering, Kyoto University; Assistant, 工学部, 助手 (10160566)
MASUYAMA Shigeru Faculty of Engineering, Kyoto University; Assistant, 工学部, 助手 (60173762)
FUKUSHIMA Masao Faculty of Engineering, Kyoto University: Associate Professor, 工学部, 助教授 (30089114)
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| Project Period (FY) |
1985 – 1986
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| Project Status |
Completed (Fiscal Year 1986)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1986: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1985: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Combinarotial optimization / Dynamic programming / Branch-and-bound method / Nonlinear programming / Computational complexity / 効率的アルゴリズム |
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| Research Abstract |
Combinatorial optimization is one of the most fundamental subjects in computing mathematics, system engineering, operations research and related areas. As clarified by the recent contribution of complexity theory, many combinatorial optimization problems possess inherent computational intractability in their structure. This means that, in order to develop efficient algorithms for practical use, every facet of problem structure must be exploited to reduce required computation time.
In this research, therefore, an attempt is made to establish a paradigm of algorithms in which all beneficial features of the given problem can be easily exploited. Based on the research conducted so far to understand mechanisms of dynamic programming and branch-and-bound, we propose a new paradigm called successive sublimation dynamic programming method (SSDP method). In addition to the theoretical properties of SSDP method, its implementation specified to concrete problems such as the traveling salesman problem and the knapsack problem is also carried out. Some computational results obtained so far suggest the superiority of SSDP method over the conventional approaches.
Finally, optimization algorithms in other areas such as nonlinear programming and minimax game solving are also investigated, and their impact on combinatorial optimization is discussed.
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