Month: January 2018

Lab. 3 Takayasu Matsuo

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Takayasu Matsuo（松尾　宇泰）
松尾　宇泰

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

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656
Eng. 6 Bldg. Room 347
Tel: +81-3-5841-6911, ext. 26911
Fax:

E-mail：matsuo＠mist.i.u-tokyo.ac.jp

[Home Page]

Curriculum Vitae

Mar. 1995 Graduated from the Master Course of the Department of Applied Physics, Graduate School of Engineering, University of Tokyo
Mar. 1997 Withdrew from the Doctor Course of the Department of Applied Physics, Graduate School of Engineering, University of Tokyo
Apr. 1997 Assistant Professor, Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University
Feb. 2003 Received a Ph.D.(Engineering) from the Graduate School of Engineering, University of Tokyo
Apr. 2004 Lecturer, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Aug. 2007 Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Jun. 2013 Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

Research Themes

Numerical Analysis，in particular, “good” numerical methods for solving differential equations.

There are many differential equations that have important physical properties such as conservation or dissipation.
A “good” numerical method for such a equation
refers to a numerical method that retain the conservation/dissipation properties in a discrete sense.
Such a method is called a structure-preserving numerical method.
Compared with a versatile numerical method,
the structure-preserving method not only provides a qualitatively correct result but also achieves numerical stability.
Currently, I’m engaged mainly in the study of high-order (highly accurate) methods for conservative/dissipative systems.

Selected Publications

Takayasu Matsuo and Daisuke Furihata,
Dissipative or Conservative Finite-Difference Schemes for Complex-Valued Nonlinear Partial Differential Equations,
J. Comput. Phys., 171 (2001), 425-447.

Takayasu Matsuo, Masaaki Sugihara, Daisuke Furihata, and Masatake Mori,
Spatially Accurate Dissipative or Conservative Finite Difference Schemes Derived by the Discrete Variational Method,
Japan J. Indust. Appl. Math., 19 (2002), 311–330.

Takayasu Matsuo,
High-order Schemes for Conservative or Dissipative Systems,
J. Comput. Appl. Math., 152 (2003), 305–317.

Lab. 3 Kengo Nakajima

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Kengo Nakajima （中島　研吾）
中島　研吾

Supercomputing Division, Information Technology Center, University of Tokyo
(concurrent post) Department of Mathematical Informatics, Graduate School of Information Science and Technology
Professor

2-11-16, Yayoi, Bunkyo-ku, Tokyo, 113-8658

Information technology center, Annex 3F, Project room 2
Tel: +81-3-5841-2719, ext. 22719
Fax: +81-3-5841-2708

E-mail：nakajima＠cc.u-tokyo.ac.jp

[Home Page]

Curriculum Vitae

Mar. 1985 Graduated from the Department of Aeronautics, School of Engineering, University of Tokyo
Apr. 1985 Mitsubishi Research Institute, Inc.
May. 1993 M.S. in Engineering, Dept. Aerospace Eng. & Eng. Mechanics, The University of Texas at Austin
Jul. 1999 Research Organization of Information Science and Technology (RIST)
Mar. 2003 Ph. D (Engineering) (University of Tokyo)
Apr. 2004 Specially-Appointed Associate Professor, Department of Earth and Planetary Science, Graduate school of Science, University of Tokyo
Apr. 2008 Specially-Appointed Professor, Supercomputing Division, Information Technology Center, University of Tokyo
Dec. 2008 Professor, Supercomputing Division, Information Technology Center, University of Tokyo

Research Themes

Large scale simulation and its foundation.
I aim to develop new algorithms through solution of practical problems in science and engineering.

1. Parallel numerical computation, parallel algorithms
2. Numerical linear algebra, parallel preconditioning
3. Computational dynamics, forms processing, visualization

Selected Publications

Nakajima, K., Parallel Iterative Solvers of GeoFEM with Selective Blocking Preconditioning for Nonlinear Contact Problems on the Earth Simulator, ACM/IEEE Proceedings of SC2003, 2003

Nakajima, K., Parallel iterative solvers for finite-element methods using an OpenMP/MPI hybrid programming model on the Earth Simulator, Parallel Computing 31, 1048-1065, 2005

Nakajima, K., Strategies for Preconditioning Methods of Parallel Iterative Solvers in Finite-Element Applications on Geophysics, Advances in Geocomputing, Lecture Notes in Earth Science 119, 65-118, 2009

奥田洋司，中島研吾編著，並列有限要素解析〔I〕，培風館，2004 (in Japanese)

Lab. 3 Ken’ichiro Tanaka

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Ken’ichiro Tanaka（田中　健一郎）
田中　健一郎

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

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656
Eng. 6 Bldg. Room 342
Tel: +81-3-5841-6439, ext. 26439
Fax:

E-mail：kenichiro＠mist.i.u-tokyo.ac.jp

[Home Page]

Curriculum Vitae

Mar. 2002 Graduated from the Department of Mathematical Engineering and Information Physics, School of Engineering, The University of Tokyo
Mar. 2004 Graduated from the Master Course of the Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Mar. 2007 Graduated from the Doctor Course of the Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Apr. 2007 – Mar. 2011 Tokio Marine & Nichido Fire Insurance Co., Ltd.
Apr. 2011 – Mar. 2015 Assistant Professor, School of Systems Information Science, Department of Complex and Intelligent Systems, Future University Hakodate
Apr. 2015 – Mar. 2017 Associate Professor, Department of Mathematical Engineering, Faculty of Engineering, Musashino University
Apr. 2017 – Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

Research Themes

Numerical analysis，in particular, theory and application related to function approximation and numerical integration.

Function approximation and numerical integration are bases of various computational methods for problems in mathematical analysis such as differential equations. I have been engaged mainly in design and analysis of accurate approximation formulas for analytic functions by means of complex analytic methods. In particular, I’m interested in analysis and application of formulas (such as the DE formulas and DE-Sinc formulas) derived by the double-exponential (DE) transformations proposed by Takahasi and Mori. Recently, I’m developing general frameworks for designing accurate approximation formulas.

Selected Publications

Ken’ichiro Tanaka, Tomoaki Okayama, and Masaaki Sugihara, Potential theoretic approach to design of accurate formulas for function approximation in symmetric weighted Hardy spaces, IMA Journal of Numerical Analysis, Volume 37, Issue 2 (2017), pp. 861-904 (doi:10.1093/imanum/drw022).

Ken’ichiro Tanaka, A fast and accurate numerical method for the symmetric Levy processes based on the Fourier transform and sinc-Gauss sampling formula, IMA Journal of Numerical Analysis, Volume 36, Issue 3 (2016), pp. 1362-1388 (doi:10.1093/imanum/drv038).

Ken’ichiro Tanaka and Alexis Akira Toda, Discretizing distributions with exact moments: error estimate and convergence analysis, SIAM Journal on Numerical Analysis, Volume 53, Issue 5 (2015), pp. 2158-2177 (doi:10.1137/140971269).

Sunao Murashige and Ken’ichiro Tanaka, A new method of convergence acceleration of series expansion for analytic functions in the complex domain, Japan Journal of Industrial and Applied Mathematics, Volume 32, Issue 1 (2015), pp. 95-117 (doi: 10.1007/s13160-014-0159-z).

Tomoaki Okayama, Ken’ichiro Tanaka, Takayasu Matsuo, and Masaaki Sugihara, DE-Sinc methods have almost the same convergence property as SE-Sinc methods even for a family of functions fitting the SE-Sinc methods Part I: Definite integration and function approximation, Numerische Mathematik, Volume 125, Issue 3 (2013), pp. 511-543 (doi: 10.1007/s00211-013-0540-x).

Ken’ichiro Tanaka, Masaaki Sugihara, Kazuo Murota, and Masatake Mori, Function classes for double exponential integration formulas, Numerische Mathematik, Volume 111, Issue 4 (2009), pp. 631-655 (doi: 10.1007/s00211-008-0195-1).

Numerical Analysis Laboratory (Mathematical Informatics Lab. 3)

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Numerical Analysis Laboratory (Mathematical Informatics Lab. 3) Home Page of Lab. 3 →
松尾　宇泰
Takayasu Matsuo
Professor		 中島　研吾
Kengo Nakajima
Professor
田中　健一郎
Ken’ichiro Tanaka
Associate Professor
Numerical Analysis
Many problems in science and engineering cannot be solved without computers. Numerical analysis is a field of study that deals with such problems based on various mathematical facts and a deep insight about the original background of the problems. Study in numerical analysis is concerned with a wide spectrum of topics from fundamental mathematics of computation to various application of numerical methods to problems in science and engineering.

Large Scale Simulation
Numerical simulation is said to be “third science” following theory and experiments. We study mathematical foundations of large scale simulations such as the method for solving linear equations by parallel computers. In the study, we take account of various aspects of a simulation such as physics, modeling, computer hardware, etc.

Numerical Simulation for Problems in Science and Engineering
Based on the theoretical and computational foundations stated above, we study state-of-the-art methods for computer-aided analysis of scientific phenomena such as nonlinear waves, and large data with matrix or tensor form.

Akiko Takeda

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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)