Shummin NAKAYAMA's Homepage

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Name: Shummin Nakayama
Ph.D: Tokyo University of Science

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Affiliation

Faculty of Science Division Ⅰ, Department of Applied Mathematics,
Tokyo University of Science
Position: Lecture

Research Areas

  • Continuous optimization theory
  • Operations Research

Curriculum Vitae

Employment History

  • April 2019 -- March 2021: Assistant Professor
    Department of Industrial and Systems Engineering, Faculty of science and engineering,
    Chuo University

  • April 2021 -- September 2022: Assistant Professor
    Department of Data Science for Business Innovation, Faculty of science and engineering,
    Chuo University

  • October 2022 -- March 2025: Assistant Professor
    Info-Powered Energy System Research Center (iPERC),
    The University of Electro-Communications

  • April 2025 -- present: Lecture
    Faculty of Science Division Ⅰ, Department of Applied Mathematics,
    Tokyo University of Science

Academic Background

  • April 2010 -- March 2014: Department of Mathematical Information Science, Tokyo University of Science
  • April 2014 -- March 2016: Master Course in Department of Mathematical Science for Information Science, Tokyo University of Science
  • April 2016 -- March 2019: Doctor Course in Department of Applied Mathematics, Tokyo University of Science

Research Achievements

Refereed Papers in Journals

  1. Shummin Nakayama, ''Active set block Barzilai-Borwein method for model predictive control", Journal of the Operations Research Society of Japan, 68 (2025), pp.82--97. [DOI, PDF]
  2. Shotaro Yagishita and Shummin Nakayama, ''An acceleration of proximal diagonal Newton method", JSIAM Letters, 16 (2024), pp. 5--8. [DOI]
  3. Shummin Nakayama, Yasushi Narushima and Hiroshi Yabe, ''Inexact proximal DC Newton-type method for nonconvex composite functions", Computational Optimization and Applications, 87 (2024), pp. 611--640. [DOI,Code]
  4. Yasushi Narushima, Shummin Nakayama, Masashi Takemura, and Hiroshi Yabe, ''Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization", Journal of Optimization Theory and Applications, 197 (2023), pp. 639--664. [DOI]
  5. Yasushi Narushima and Shummin Nakayama, ''A proximal quasi-Newton method based on memoryless modified symmetric rank-one formula", Journal of Industrial and Management Optimization, 19 (2023), pp. 4095--4111. [DOI]
  6. Shummin Nakayama, Yasushi Narushima, Hiroaki Nishio, Hiroshi Yabe, ''An active-set memoryless quasi-Newton method based on a spectral-scaling Broyden family for bound constrained optimization", Results in Control and Optimization, 3 (2021), 100012. [DOI]
  7. Shummin Nakayama and Jun-ya Gotoh, ''On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression", Optimization Letters, 15 (2021), pp.2831--2860. [DOI]
  8. Shummin Nakayama, Yasushi Narushima and Hiroshi Yabe, ''Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions", Computational Optimization and Applications, 79 (2021), pp.127--154. [DOI]
  9. 成島康史，中山舜民，矢部博，''無制約最適化問題に対するメモリーレス準ニュートン法について (Memoryless quasi-Newton methods for unconstrained optimization)"，応用数理，29(4) (2019), pp. 8--17. [DOI, PDF]
  10. Shummin Nakayama ''A hybrid method of three-term conjugate gradient method and memoryless quasi-Newton method for unconstrained optimization", SUT Journal of Mathematics, 54 (2018), pp.79--98. [PDF]
  11. Shummin Nakayama, Yasushi Narushima and Hiroshi Yabe, ''Memoryless quasi-Newton methods based on spectral-scaling Broyden family for unconstrained optimization", Journal of Industrial and Management Optimization, 15 (2019), pp. 1773--1793. [DOI]
  12. Shummin Nakayama, Yasushi Narushima and Hiroshi Yabe, ''A memoryless symmetric rank-one method with sufficient descent property for unconstrained optimization", Journal of the Operations Research Society of Japan, 61 (2018), pp.53--70. [DOI, PDF]

Proceedings

  1. Shummin Nakayama, Yasushi Narushima, ''Global convergence of a proximal memoryless symmetric rank one method for minimizing composite functions", In Proceedings of International Conference on Nonlinear Analysis and Convex Analysis - International Conference on Optimization Techniques and Applications (NACA-ICOTA2019), (Hakodate, Japan, 2019), Contents II, pp. 99--108, 2021. [PDF]
  2. Hiroaki Nishio, Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''A globally convergent active-set memoryless quasi-Newton method based on spectral-scaling Broyden family for bound constrained optimization", In Proceedings of International Conference on Nonlinear Analysis and Convex Analysis - International Conference on Optimization Techniques and Applications (NACA-ICOTA2019), (Hakodate, Japan, 2019), Contents II, pp. 147--160, 2021. [PDF]

Conference Activities & Talks

  1. *Yasushi Narushima, Shummin Nakayama, Hiroshi Yabe, ''Nonmonotone proximal structured quasi-Newton methods based on the Broyden family ", The Third Pacific Optimisation Conference (POC2023), Sunway City, Malaysia, December 10, 2023.
  2. *Shummin Nakayama, Shotaro Yagishita, ''Proximal diagonal Newton method for nonconvex composite optimization", The Third Pacific Optimisation Conference (POC2023), Sunway City, Malaysia, December 10, 2023.
  3. *Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''Proximal structured quasi-Newton method for nonlinear least squares with nonsmooth regularizer", 10th International Congress on Industrial and Applied Mathematics (ICIAM), August 21, 2023.
  4. *Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''An Inexact Proximal Difference-of-Convex Algorithm Based on Memoryless Quasi-Newton Methods", 2021 SIAM Conference on Optimization, July 23, 2021 (Virtual Conference).
  5. *Jun-ya Gotoh, Shummin Nakayama, ''Continuous Exact Penalty Approach To Grouped Variable Selection In Regression Methods", INFORMS Annual Meeting, November 14, 2020 (Virtual Conference).
  6. *Jun-ya Gotoh, Shummin Nakayama, ''Sparse Robust Regression With Continuous Exact K-sparse Penalties", INFORMS Annual Meeting, Catonsville, USA, October 23, 2019.
  7. *Hiroaki Nishio, Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''Global convergence of an active-set memoryless quasi-Newton method based on spectral-scaling Broyden family for bound constrained optimization", International Conference on Nonlinear Analysis and Convex Analysis - International Conference on Optimization Techniques and Applications, Hakodate, Japan, August 30, 2019.
  8. *Shummin Nakayama, Yasushi Narushima, ''Global convergence of a proximal memoryless symmetric rank one method for minimizing composite functions", International Conference on Nonlinear Analysis and Convex Analysis - International Conference on Optimization Techniques and Applications, Hakodate, Japan, August 29, 2019.
  9. *Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''Inexact proximal memoryless quasi-Newton methods based on Broyden family for minimizing composite functions", The Sixth International Conference on Continuous Optimization of the Mathematical Optimization Society, Berlin, Germany, August 7, 2019.
  10. *Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''Inexact proximal memoryless spectral-scaling MBFGS mehtod", 23rd International Symposium on Mathematical Programming, Bordeaux, France, July 5, 2018.
  11. *Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe, ''Global Convergence of Memoryless Quasi-Newton Methods Based on Broyden Family for Unconstrained Optimization", 2017 SIAM Conference on Optimization, Vancouver, Canada, May 24, 2017.
  12. *Shummin NAKAYAMA, Yasushi NARUSHIMA, Hiroshi YABE, ''A memoryless sized symmetric rank-one method with sufficient descent property for unconstrained optimization", The Fifth International Conference on Continuous Optimization of the Mathematical Optimization Society, Tokyo, Japan, August 11, 2016.

Contact

E-mail: nakayama<at>rs.tus.ac.jp (Please replace <at> by @)