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RIKEN Center for Advanced Intelligence Project Continuous Optimization Team

Team Leader: Akiko Takeda (D.Sci.)

Research Summary

Akiko  Takeda(D.Sci.)

Our team focuses on mathematical optimization. An optimization problem is the problem of finding the best solution from all feasible solutions. By formulating various real-world problems as optimization problems and solving them by efficient algorithms, we can find their reasonable solutions. Optimization algorithms are applicable not only to machine learning problems but also to various applications, e.g., of production research and industrial engineering. However some problems such as nonconvex optimization and uncertain optimization problems are difficult to solve. Currently, we are working on efficient algorithms for such difficult optimization problems.

Research Subjects:

  • Mathematical Optimization
  • Nonconvex Optimization
  • Uncertainty

Main Research Fields

  • Informatics

Related Research Fields

  • Interdisciplinary Science & Engineering
  • Mathematical informatics

Keywords

  • Mathematical Optimization
  • Operations Research

Selected Publications

  • 1. Tianxiang Liu, Ivan Markovsky, Ting Kei Pong, Akiko Takeda.:
    "A hybrid penalty method for a class of optimization problems with multiple rank constraints"
    SIAM Journal on Matrix Analysis and Applications, 2020.
  • 2. Kazuhiro Sato, Akiko Takeda.:
    "Controllability maximization of large-scale systems using projected gradient method"
    IEEE Control Systems Letters (L-CSS), 4(4), pp.821-826 (2020).
  • 3. Ivan Markovsky, Tianxiang Liu, Akiko Takeda.:
    "Data-driven structured noise filtering via common dynamics estimation"
    IEEE Transactions on Signal Processing, 68, pp.3064-3073 (2020)
  • 4. Daniel Andrade, Akiko Takeda, Kenji Fukumizu.:
    "Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model"
    Statistics and Computing, 30, pp.351-376 (2020).
  • 5. Tianxiang Liu, Ting Kei Pong and Akiko Takeda.:
    "A refined convergence analysis of pDCAe with applications to simultaneous sparse recovery and outlier detection"
    Computational Optimization and Applications, 73 (1), pp 69-100 (2019)
  • 6. Michael Metel, Akiko Takeda.:
    "Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization"
    Proceedings of the 36th International Conference on Machine Learning, PMLR 97, pp.4537-4545 (2019).
  • 7. Tianxiang Liu, Ting Kei Pong and Akiko Takeda.:
    "A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems"
    Mathematical Programming, 176, pp.339-367 (2019).
  • 8. Shinji Yamada, Akiko Takeda.:
    "Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization"
    Journal of Global Optimization, 71 (2), pp.313-339 (2018).
  • 9. Jun-ya Gotoh, Akiko Takeda and Katsuya Tono.:
    "DC Formulations and Algorithms for Sparse Optimization Problems"
    Mathematical Programming, 169 (1), pp.141-176 (2018).
  • 10. Atsushi Miyauchi, Akiko Takeda.:
    "Robust Densest Subgraph Discovery"
    2018 IEEE International Conference on Data Mining (ICDM), pp.1188-1193 (2018)

Related Links

Lab Members

Principal investigator

Akiko Takeda
Team Leader

Core members

Takayuki Okuno
Research Scientist
Pierre-Louis Poirion
Research Scientist
Michael Ros Metel
Postdoctoral Researcher
Tianxiang Liu
Postdoctoral Researcher

Contact Information

Department of Creative Informatics, Graduate School of Information Science and Technology, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan
Email: takeda [at] mist.i.u-tokyo.ac.jp

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