Discrete Convex Optimization for Artificial Intelligence Technology
| Project/Area Number |
17K00036
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Mathematical informatics
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| Research Institution | Gunma University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 劣モジュラ最適化 / 人工知能 / 機械学習 / ネットワーク最適化 / モビリティ / 組合せ最適化 / アルゴリズム |
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| Outline of Final Research Achievements |
In this research, we deal with discrete optimization (combinatorial optimization) problems related to discrete structures such as networks. In particular, we focus on submodular optimization problems, which can be called convex optimization in the discrete world. In particular, we focus on submodular optimization problems, which can be called convex optimization in the discrete world.
We worked on a theoretical study of submodular optimization and related problems. In addition, we dealt with the application of discrete convex optimization to artificial intelligence techniques such as machine learning. Furthermore, as an application of network optimization technology, we worked on research related to transportation networks and so on.
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| Academic Significance and Societal Importance of the Research Achievements |
数多くある候補の中から最もよいものを見つける数学的手法は「数理最適化」とよばれる。本研究は数理最適化でも、特にネットワーク構造のような離散的な対象を扱う数理最適化の理論研究とその人工知能技術への応用をテーマとしてきた。ネットワーク構造のような様々な分野において現れる基本的な研究対象であり、その効率化は理論研究も、実社会への応用研究も社会的意義のある取り組みであるといえる。
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Report
(7 results)
Research Products
(9 results)
