Efficient Interpretation of Large-Scale Real Data by Static Inverse Optimization

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  • Zhang Hong
    Department of Brain Science & Engineering, Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology
  • Ishikawa Masumi
    Department of Brain Science & Engineering, Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology

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  • 静的逆最適化による大規模実データの効率的解釈
  • セイテキ ギャクサイテキカ ニ ヨル ダイキボ ジツ データ ノ コウリツテキ カイシャク

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Abstract

We have already proposed a methodology for static inverse optimization to interpret real data from a viewpoint of optimization. In this paper we propose a method for efficiently generating constraints by divide-and-conquer to interpret large-scale data by static inverse optimization. It radically decreases computational cost of generating constraints by deleting non-Pareto optimal data from given data. To evaluate the effectiveness of the proposed method, simulation experiments using 3-D artifical data are carried out. As an application to real data, criterion functions underlying decision making of about 5, 000 tenants living along Yamanote line and Soubu-Chuo line in Tokyo are estimated, providing interpretation of rented housing data from a viewpoint of optimization.

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