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2019 Fiscal Year Final Research Report

Development of practical combinatorial optimization algorithms by speeding up the continuous relaxation method

Research Project

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Project/Area Number 17K00040
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Mathematical informatics
Research InstitutionChuo University (2019)
Institute of Physical and Chemical Research (2017-2018)

Principal Investigator

Fukunaga Takuro  中央大学, 理工学部, 准教授 (60452314)

Project Period (FY) 2017-04-01 – 2020-03-31
Keywords近似アルゴリズム / 最適化 / クラスタリング / 劣モジュラ最適化
Outline of Final Research Achievements

This project aims at developing practical efficient approximation algorithms for hard combinatorial optimization problems by the continuous relaxation method. The continuous relaxation method has advantages in flexibility and usability. Simultaneously, it has a disadvantage in the computational speed. To obtain practical approximation algorithms, the project worked on this issue by investigating formulations of continuous relaxations and by studying fast algorithms for solving continuous relaxations. As accomplishments of the project, we obtain new algorithms for various combinatorial optimization problems such as hypergraph correlation clustering problem, stochastic submodular maximization problem under knapsack constraints, and generalized budget allocation problem.

Free Research Field

組合せ最適化

Academic Significance and Societal Importance of the Research Achievements

本研究により,多くの組合せ最適化問題について実用的なアルゴリズムを得ることができた.また,未知の組合せ最適化問題を効率的に解くための理論基盤になるような知見を得ることもできた.組合せ最適化問題は,ロジスティクスなどの産業分野から機械学習のような人工知能技術まで多様な場面で現れるので,組合せ最適化問題を解く効率的なアルゴリズムの実用化は利便性の高い情報システム実現につながる重要な成果である.

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Published: 2021-02-19  

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