Yoshihiro Kanno

教員紹介

寒野 善博(かんの よしひろ)
寒野 善博

東京大学大学院 情報理工学系研究科
数理情報学専攻
教授

〒113-8656 東京都文京区本郷 7-3-1 工学部 6 号館 435 号室
Tel: 03-5841-6913 内線 26913
Fax: 03-5841-6886

E-mail:kanno@mist.i.u-tokyo.ac.jp

[ホームページ]

Education and Employment

March 1998 B. Eng., Kyoto University
March 2000 M. Eng., Kyoto University
September 2002 Dr. Eng., Kyoto University
March 2004 Assistant Professor, Department of Urban and Environmental Engineering, Kyoto University
May 2006 Assistant Professor, Department of Mathematical Informatics, The University of Tokyo
September 2008 Associate Professor, Department of Mathematical Informatics, The University of Tokyo
April 2015 Associate Professor, Materials and Structures Laboratory, Tokyo Institute of Technology
April 2016 Associate Professor, Laboratory for Future Interdisciplinary Research of Science and Technology, Tokyo Institute of Technology
October 2017 Professor, Mathematics and Informatics Center, The University of Tokyo

Research Interests

Modeling and algorithms of mathematical optimization and their applications to applied mechanics and structural design

  • Continuous optimization and applied mechanics: convex optimization, complementarity, duality and their applications to structural optimization, contact mechanics, plasticity, etc.
  • Robust optimization and its applications: Optimization with uncertain data, robust optimization of structures, robustness evaluation of uncertain systems, etc.

Selected Publications

Y. Kanno, “A fast first-order optimization approach to elastoplastic analysis of skeletal structures,” Optimization and Engineering, 17, 861–896 (2016).
Y. Kanno, “Nonsmooth Mechanics and Convex Optimization,” CRC Press, Boca Raton (2011).
Y. Kanno, J. A. C. Martins, A. Pinto da Costa, “Three-dimensional quasi-static frictional contact by using second-order cone linear complementarity problem,” International Journal for Numerical Methods in Engineering, 65, 62–83 (2006).

Mathematical Programming Laboratory

Mathematical Programming Laboratory(Mathematical Informatics 5th Laboratory)
– Resolve “troubles” of the world –
HomePage of Lab.→
Akiko Takeda
Akiko Takeda

Professor
Yoshihiro Kanno
Yoshihiro Kanno

Professor
Kazuhiro Sato
Kazuhiro Sato

Lecturer
Operations Research(OR)
It is a scientific technique that builds mathematical models and finds their solutions by using computers for solving real problems. In particular, we focus on modeling as a mathematical optimization problem and developing algorithms to solve the problem. The scope of application of OR is diverse and we are conducting research to solve real-world problems in the fields of structure design, energy system, financial engineering, machine learning.
Efficient algorithms for continuous optimization and thier applications to real-world problems
Problems in real world often result in large scale, nonlinear, nonconvex continuous optimization problem. Also, in a situation where robustness against uncertainty (variation) of data is required, a model called a robust optimization problem may be useful. We aim to efficiently solve such optimization problems and contribute to real world problem solving.

Akiko Takeda

Our faculty

Akiko Takeda
Akiko Takeda

Professor, Department of Mathematical Informatics,
Graduate School of Information Science and Technology,
the University of Tokyo

Room 253, 7-3-1 6 Hongo, Bunkyo-ku, Tokyo 113-8656
Tel: 03-5841-6920 ext. 26920   Fax:
E-mail: takeda@mist.i.u-tokyo.ac.jp

[ホームページ]

Biography

March, 1996 Bachelor of Engineering, Faculty of Science and Technology, Keio University
March, 1998 M.A. in Administration Engineering, Graduate School of Science and Technology, Keio University
March, 2001 Ph.D. in Mathematical and Computing Sciences, Tokyo Institute of Technology
April, 2001 Researcher, Corporate R&D Center, Toshiba Corporation
April, 2003 Assistant Professor, Department of Mathematical and Computing Sciences, Tokyo Institute of Technology
April, 2008 Lecturer, Faculty of Science and Technology, Keio University
April, 2011 Associate Professor, Faculty of Science and Technology, Keio University
April, 2013 Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, the University of Tokyo
April, 2016 Professor, Department of Mathematical Analysis and Statistical Inference, The Institute of Statistical Mathematics
April 2018 Professor, Graduate School of Information Science and Technology, The University of Tokyo

Research topics

We are conducting research focusing on formulating a mathematical optimization problem and developing an algorithm to solve the problem. In particular, we are developing efficient algorithms for continuous optimization problems, which are considered to be difficult to handle, such as optimization problems including uncertainty and non-convex optimization problems. Recently, I am interested in the topics below.

1: Efficient algorithms of continuous optimization problems focusing on non-convex optimization problems
2: Decision-making method for uncertain optimization problems: development of algorithms for robust optimization problems
3: Application of optimization methods to problems appearing in various fields (machine learning, financial engineering, energy system, etc.)

Major publications

Jun-ya Gotoh, Akiko Takeda and Katsuya Tono, “DC formulations and algorithms for Sparse Optimization Problems”, mathematic Al Programming, First Online: 26 July 2017. (Solution study for sparse optimization problem)

Naoki Ito, Akiko Takeda and Kim-chuan Toh, “A Unified formulation and Fast Accelerated proximal Gradient for Classi Fication “, Journal of Machine Learning Research, 18, pp.1-49, 2017. (Application to machine learning)

Shinsaku Sakaue, Akiko Takeda, Sunyoung Kim and Naoki Ito, “Exact relaxations with truncated Moment for Binary Polynomial Optimization Problems “, SIAM Journal on Optimization, 27 (1), pp. 565-582, 2017. (SDP mitigation method for polynomial optimization problems)

Kosuke Nishida, Akiko Takeda, Satoru Iwata, Mariko Kiho and Isao Nakayama, “household, consumption prediction Ture selection of Lifestyle data “, IEEE International Conference on Smart Grid Communications, Dresden, Germany, 2017. (Application to energy systems)