RIKEN Center for Advanced Intelligence Project Continuous Optimization Team

Team Leader: Akiko Takeda (D.Sc.)

Japanese Page

Research Summary

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)

Lab Members

Principal investigator

Akiko Takeda: Team Leader

Core members

Pierre-Louis Poirion: Research Scientist
Jan Harold Alcantara: Postdoctoral Researcher
Andi Han: Postdoctoral Researcher
Trung Hieu Vu: Postdoctoral Researcher
Takayuki Okuno: Visiting Scientist
Mirai Tanaka: Visiting Scientist
Benjamin Poignard: Visiting Scientist
Tomoya Kamijima: Research Part-time Worker II

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: akiko.takeda [at] riken.jp