NARUSHIMA Yasushi

写真a

Affiliation

Faculty of Science and Technology, Department of Industrial and Systems Engineering (Yagami)

Position

Professor

Related Websites

External Links
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Career 【 Display / hide 】

  • 2007.04
    -
    2010.09

    Tokyo University of Science, Department of Mathematical Information Science, Faculty of Science, Assistant Professor

  • 2010.10
    -
    2012.03

    Fukushima National College of Technology, Department of Communication and Information Science, Assistant Professor

  • 2012.04
    -
    2013.03

    Yokohama National University, Department of Management Science, Fuculty of Business Administration, Associate Professor

  • 2013.04
    -
    2019.03

    Yokohama National University, Faculty of International Social Sciences, Associate Professor

  • 2019.04
    -
    2023.03

    Keio University, Department of Industrial and Systems Engineering, Faculty of Science and Technology, Associate Professor

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Academic Background 【 Display / hide 】

  • 1998.04
    -
    2002.03

    Tokyo University of Science, Faculty of Science, Department of Applied Mathematics

    University, Graduated

  • 2002.04
    -
    2004.03

    Tokyo University of Science, Graduate school of Science

    Graduate School, Completed, Master's course

  • 2004.04
    -
    2007.03

    Tokyo University of Science, Graduate school of Science

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • Master (Science), Tokyo University of Science, Coursework, 2004.03

  • Doctor (Science), Tokyo University of Science, Coursework, 2007.03

    Gradient methods and their convergence properties for large-scale unconstrained optimization problems

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Licenses and Qualifications 【 Display / hide 】

  • 中学校及び高等学校　教員専修免許(数学)（更新講習未受講）, 2004.03

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Research Areas 【 Display / hide 】

  • Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Social systems engineering (Operations Research)

  • Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Safety engineering (Operations Research)

  • Informatics / Mathematical informatics (Mathematical Optimization)

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Research Keywords 【 Display / hide 】

  • Mathematical Optimization

  • Equilibrium Problem

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Books 【 Display / hide 】

  • 非線形最適化法-数理的基礎とPythonによる実装-

    成島康史, 中山舜民, 矢部博, オーム社, 2025.08,  Page： 376

  • 基礎数学IV. 最適化理論

    山本芳嗣 他, 東京化学同人, 2019.10,  Page： 348

    Contact page： 2.3節(pp. 52-101)

  • 応用数理ハンドブック

    薩摩順吉ら編, 朝倉書店, 2013.11

    Contact page： 394-397

  • 数理工学辞典

    太田快人ら編, 朝倉書店, 2011.11

    Contact page： 544-546

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Papers 【 Display / hide 】

  • Inexact proximal DC Newton-type method for nonconvex composite functions

    Nakayama S., Narushima Y., Yabe H.

    Computational Optimization and Applications (Computational Optimization and Applications)  87 ( 2 ) 611 - 640 2024

    Research paper (scientific journal), Accepted,  ISSN  09266003

    　View Summary

    We consider a class of difference-of-convex (DC) optimization problems where the objective function is the sum of a smooth function and a possibly nonsmooth DC function. The application of proximal DC algorithms to address this problem class is well-known. In this paper, we combine a proximal DC algorithm with an inexact proximal Newton-type method to propose an inexact proximal DC Newton-type method. We demonstrate global convergence properties of the proposed method. In addition, we give a memoryless quasi-Newton matrix for scaled proximal mappings and consider a two-dimensional system of semi-smooth equations that arise in calculating scaled proximal mappings. To efficiently obtain the scaled proximal mappings, we adopt a semi-smooth Newton method to inexactly solve the system. Finally, we present some numerical experiments to investigate the efficiency of the proposed method, which show that the proposed method outperforms existing methods.

  • Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization

    Narushima Y., Nakayama S., Takemura M., Yabe H.

    Journal of Optimization Theory and Applications (Journal of Optimization Theory and Applications)  197 ( 2 ) 639 - 664 2023

    Research paper (scientific journal), Joint Work, Lead author, Corresponding author,  ISSN  00223239

    　View Summary

    We consider iterative methods for unconstrained optimization on Riemannian manifolds. Though memoryless quasi-Newton methods are effective for large-scale unconstrained optimization in the Euclidean space, they have not been studied over Riemannian manifolds. Therefore, in this paper, we propose a memoryless quasi-Newton method in Riemannian manifolds. The proposed method is based on the spectral-scaling Broyden family with additional modifications to ensure the sufficient descent condition. We present an algorithm for the proposed method that uses the Wolfe line search conditions and show that this algorithm guarantees global convergence. We emphasize that global convergence is guaranteed without any assumptions regarding the convexity of the objective function or the isometric property of the vector transport. In addition, we derive appropriate selections for the parameter vector contained in the proposed method. Numerical experiments are conducted to compare the proposed method with conventional conjugate gradient methods using typical test problems. The results show that the proposed method is superior to the tested conjugate gradient methods.

  • A PROXIMAL QUASI-NEWTON METHOD BASED ON MEMORYLESS MODIFIED SYMMETRIC RANK-ONE FORMULA

    Narushima Yasushi, Nakayama Shummin

    JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION (Journal of Industrial and Management Optimization)  19 ( 6 ) 4095 - 4111 2022.07

    Research paper (scientific journal), Lead author, Corresponding author, Accepted,  ISSN  1547-5816

    　View Summary

    We consider proximal gradient methods for minimizing a composite function of a differentiable function and a convex function. To accelerate the general proximal gradient methods, we focus on proximal quasi-Newton type methods based on proximal mappings scaled by quasi-Newton matrices. Although it is usually dificult to compute the scaled proximal mappings, applying the memoryless symmetric rank-one (SR1) formula makes this easier. Since the scaled (quasi-Newton) matrices must be positive definite, we develop an algorithm using the memoryless SR1 formula based on a modified spectral scaling secant condition. We give the subsequential convergence property of the proposed method for general objective functions. In addition, we show the R-linear convergence property of the method under a strong convexity assumption. Finally, some numerical results are reported.

  • An active-set memoryless quasi-Newton method based on a spectral-scaling Broyden family for bound constrained optimization

    Shummin Nakayama, Yasushi Narushima, Hiroaki Nishio, Hiroshi Yabe

    Results in Control and Optimization (ELSEVIER)  3 2021.04

    Research paper (scientific journal), Joint Work, Accepted

    　View Summary

    In this paper, we consider an active-set algorithm for solving large-scale bound constrained optimization problems. First, by incorporating a restart technique, we modify the active-set strategy by Yuan and Lu (2011) and combine it with the memoryless quasi-Newton method based on a modified spectral-scaling Broyden family. Then, we propose an algorithm of our method with the framework of the Armijo line search, and show its global convergence. Finally, we illustrate some numerical experiments to investigate how the parameter choice in our method affects numerical performance.

  • Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions

    Nakayama S., Narushima Y., Yabe H.

    Computational Optimization and Applications (Computational Optimization and Applications)  79 ( 1 ) 127 - 154 2021

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  09266003

    　View Summary

    This study considers a proximal Newton-type method to solve the minimization of a composite function that is the sum of a smooth nonconvex function and a nonsmooth convex function. In general, the method uses the Hessian matrix of the smooth portion of the objective function or its approximation. The uniformly positive definiteness of the matrix plays an important role in establishing the global convergence of the method. In this study, an inexact proximal memoryless quasi-Newton method is proposed based on the memoryless Broyden family with the modified spectral scaling secant condition. The proposed method inexactly solves the subproblems to calculate scaled proximal mappings. The approximation matrix is shown to retain the uniformly positive definiteness and the search direction is a descent direction. Using these properties, the proposed method is shown to have global convergence for nonconvex objective functions. Furthermore, the R-linear convergence for strongly convex objective functions is proved. Finally, some numerical results are provided.

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Reviews, Commentaries, etc. 【 Display / hide 】

  • 管理工学科でのOR研究-ロバストサプライチェインネットワーク均衡モデルと二次錐相補性問題-

    成島康史

    オペレーションズ・リサーチ：経営の科学 66   144 - 150 2021

    Article, review, commentary, editorial, etc. (scientific journal), Single Work

  • Robust Supply Chain Network Equilibrium Model

    Transportation Science  2019.07

  • 無制約最適化問題に対する勾配法について

    成島康史

    オペレーションズ・リサーチ：経営の科学 64   344 - 351 2019

    Article, review, commentary, editorial, etc. (scientific journal), Single Work

  • 進化ゲーム理論を用いたビジネスゲームの考察

    成島康史

    シミュレーション&ゲーミング 27   20 - 26 2017

    Rapid communication, short report, research note, etc. (scientific journal), Single Work

  • 無制約最適化問題に対するアルゴリズムの最前線-非線形共役勾配法を中心に-

    成島康史

    オペレーションズ・リサーチ：経営の科学 59   131 - 137 2014

    Article, review, commentary, editorial, etc. (scientific journal), Single Work

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Presentations 【 Display / hide 】

  • An inexact proximal difference-of-convex algorithm based on memoryless quasi-Newton methods

    Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe

    [Domestic presentation]  SIAM Conference on Optimization (OP21), 

    2021.07
    , 

    Oral presentation (general)

  • Some robust supply chain network equilibrium models

    Yasushi Narushima

    [International presentation]  International Conference on Nonlinear Analysis and Convex Analysis--International Conference on Optimization: Techniques and Applications (NACA-ICOTA2019), 

    2019.08
    , 

    Oral presentation (invited, special)

  • Global convergence of a proximal memoryless symmetric rank one method for minimizing composite functions

    Shummin Nakayama, Yasushi Narushima

    [International presentation]  International Conference on Nonlinear Analysis and Convex Analysis--International Conference on Optimization: Techniques and Applications (NACA-ICOTA2019), 

    2019.08
    , 

    Oral presentation (general)

  • Global convergence of an active-set memoryless quasi-Newton method based on spectral-scaling Broyden family for bound constraind optimization

    Hiroaki Mishio, Shummin Nakayama, Yasushi Narushima, and Hiroshi Yabe

    [International presentation]  International Conference on Nonlinear Analysis and Convex Analysis--International Conference on Optimization: Techniques and Applications (NACA-ICOTA2019), 

    2019.08
    , 

    Oral presentation (general)

  • Inexact proximal memoryless spectral-scaling MBFGS method

    Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe

    [International presentation]  23th International Symposium of Mathematical Programming (ISMP2018), 

    2018.09
    , 

    Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • 大規模非線形最適化問題に対する数値計算法の理論的研究およびその実装

    2023.04
    -
    2027.03

    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), No Setting

    　View Summary

    最適化問題はいろいろな分野で扱われる重要な問題であり、無制約最適化問題と制約付き最適化問題とに分けられる。最適化問題を効率よく解くための数値解法の研究は近年ますます活発に行われている。本研究では、非線形最適化問題の数値計算アルゴリズムの研究に焦点をあてる。提案した数値計算アルゴリズムの収束性を証明して理論的な裏づけをするとともに、数値実験を通してその有効性・実用性を検証する。さらに、実社会で発生する具体的な最適化問題を解く際の実用化を目指して、提案する数値計算アルゴリズムのソフトウェアも開発していく。したがって、本研究は社会的に大きな意義を持つ。

  • Systematization of sustainable service systems considering strategic goal mitigation and simplification

    2022.04
    -
    2026.03

    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), No Setting

    　View Summary

    アマゾンが「置き配」を取り入れたように，当然と考えられてきたサービス目標をあえて緩和させることで事業を成功させるという逆転の発想による成功事例は十分に論議されていない．本研究は，完璧を目指していた従来のサービス観とは異なる，多様な目標の間のバランスを取ることを重視する新たなサービス観の理論的体系化を目的とする．サービス失敗の許容範囲と補償制度を主要要素とするサービス運用の理論モデルを構築し，サービス企業，従業員，顧客の全てが便益を与えられる新しい解決策の実務への提案を行う．さらに，労働者不足などのサービス業の問題を解決し，働き甲斐も経済成長も両立できる持続可能なサービス観を社会に発信する．

  • Study on algorithms of numerical methods for large scale nonlinear optimization problems and their implementation

    2020.04
    -
    2023.03

    Tokyo University of Science, Grants-in-Aid for Scientific Research, YABE HIROSHI, Grant-in-Aid for Scientific Research (C), No Setting

    　View Summary

    We studied a proximal Newton-type method to solve the minimization of a composite function that is the sum of a smooth nonconvex function and a nonsmooth convex function. We proposed an inexact proximal memoryless quasi-Newton method based on the Broyden family and showed its global convergence. We considered the case where the nonsmooth function was given as a DC function. In addition, we dealt with the smooth function whose Hessian has a special structure. We also combined the active set strategy with the memoryless quasi-Newton method for solving bound constrained minimization problems. We considered memoryless quasi-Newton methods for optimization problems on Riemannian manifolds. We also proposed a primal-dual interior point trust-region method for nonlinear semidefinite programming problems, and a trust-region SQP method in which negative-curvature directions were used to obtain the global convergence to a second-order critical point for constrained optimization problems.

  • Strategy and Organization of Business of Hyper-flexible and Configurable Facilities

    2018.04
    -
    Present

    Yokohama National University, Grants-in-Aid for Scientific Research, Sato Ryo, Grant-in-Aid for Scientific Research (B), Research grant, Coinvestigator(s)

    　View Summary

    Speculating possible strategy and organization to utilize bigdata and SNS technology is quite urgent issue for business organization and academics. We provided two directions in the research of such hyper-flexible business organization. In one direction we focus on clarifying mechanism of aligned flow of material and information. And in the other direction, we aimed at ever changing demand process and tried to take it into the business process.
    We got the following result. We developed gaming methodology for the analysis of corporate strategy that allows us dynamic if-then conditions, especially in servitization of manufacturers. As to marketing strategy, we designed and implemented a mechanism that allows us to use SNS in usual commerce. A wide variety of questionnaire-based research on supply chain management are conducted, and we showed some important relationship among flexibility, quality management, and others.

    　View Remarks

    2018年度は学内分担者であったため分担金なし．

  • Analysis of equilibrium problems arising within supply chain networks

    2018.04
    -
    2022.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Narushima Yasushi, Grant-in-Aid for Scientific Research (C), Principal investigator

    　View Summary

    Supply Chain Management (hereafter referred to as SCM) is a central research topic in the current field of management science. Mathematical models aiming for overall optimization in Supply Chain Networks (hereafter referred to as SCN) have been studied for a long time, but research on competitive SCNs has not advanced as much as its importance would suggest. In this study, we focus on formulating competitive situations in SCNs as equilibrium problems, particularly addressing "equilibrium problems in SCNs with uncertainties," "equilibrium problems in SCNs considering service," and "equilibrium problems in SCNs with complex structures and constraints".

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Awards 【 Display / hide 】

  • 文献賞奨励賞

    成島康史, 2008.03, 日本オペレーションズ・リサーチ学会

    Type of Award： Award from Japanese society, conference, symposium, etc.

    　View Description

    受賞論文：A Nonmonotone Memory Gradient Method for Unconstrained Optimization（Journal of the Operations Research Society of Japan Vol.50, No.1）

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Other 【 Display / hide 】

  • 高度化社会に向けた数理最適化の新潮流

    　View Details

    平成30年度 京都数理解析研究所 共同研究(公開型)　研究代表者

  • 数理最適化の発展：モデル化とアルゴリズム

    　View Details

    平成29年度 京都数理解析研究所 共同研究(公開型) 研究代表者

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Courses Taught 【 Display / hide 】

  • SYSTEMS OPTIMIZATION

    2025

  • SEMINAR IN INDUSTRIAL AND SYSTEMS ENGINEERING

    2025

  • OPERATIONS RESEARCH 4

    2025

  • OPERATIONS RESEARCH 2

    2025

  • OPEN SYSTEMS MANAGEMENT: LECTURE AND LABORATORIES

    2025

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Memberships in Academic Societies 【 Display / hide 】

  • 日本オペレーションズ・リサーチ学会, 

    2004
    -
    Present

  • 日本応用数理学会, 

    2004
    -
    Present
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Committee Experiences 【 Display / hide 】

  • 2016.04
    -
    Present

    論文誌JORSJ編集委員会 編集委員, 日本オペレーションズ・リサーチ学会

  • 2013.04
    -
    Present

    庶務幹事, 日本オペレーションズ・リサーチ学会

  • 2013.04
    -
    2016.03

    機関紙「応用数理」編集委員, 日本応用数理学会

  • 2009.04
    -
    2011.03

    論文誌JORSJ編集委員会 編集幹事, 日本オペレーションズ・リサーチ学会

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