2017 Fiscal Year Final Research Report
Network optimization based on discrete convexity, and its interdisciplinary research
| Project/Area Number |
26730006
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Theory of informatics
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| Research Institution | Gunma University (2017)
Future University-Hakodate (2014-2016) |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Keywords | ネットワーク最適化 / 劣モジュラ最適化 / 人工知能 / 機械学習 / 組合せ最適化 |
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| Outline of Final Research Achievements |
Optimization methods, which find good solutions from many candidates, are the core technologies of this research. In this research, we focused on optimization methods with respect to discrete structures such as networks. Especially, we aimed at theoretical developments of network optimization methods based on submodular optimization. Submodularity can be regarded as a discrete version of convexity. In addition to basic theory, we applied network optimization methods to problems in machine learning, artificial intelligence, and so on. In this way, we worked on building basic technologies for optimization theory for solving problems in modern society, and designed efficient network optimization algorithms.
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| Free Research Field |
離散最適化
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