成島 康史

研究成果

年別の研究成果 2006 2011 2012 2013 2023

• 27 Article
• 1 Conference contribution

年別の研究成果

年別の研究成果

検索結果

• 2023

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

  Narushima, Y. & Nakayama, S., 2023 6月, In: Journal of Industrial and Management Optimization. 19, 6, p. 4095-4111 17 p.

  研究成果: Article › 査読

  Open Access

  • Proximal Methods 100%
  • Quasi-Newton 97%
  • Quasi-Newton Method 94%
  • Newton-Raphson method 83%
  • Proximal Mapping 75%

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

  Narushima, Y., Nakayama, S., Takemura, M. & Yabe, H., 2023 5月, In: Journal of Optimization Theory and Applications. 197, 2, p. 639-664 26 p.

  研究成果: Article › 査読

  • Conjugate gradient method 100%
  • Quasi-Newton Method 98%
  • Scaling 93%
  • Newton-Raphson method 87%
  • Optimization 59%
• 2021

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

  Nakayama, S., Narushima, Y., Nishio, H. & Yabe, H., 2021 6月, In: Results in Control and Optimization. 3, 100012.

  研究成果: Article › 査読

  Open Access

  • Active Set Method 100%
  • Quasi-Newton Method 83%
  • Constrained Optimization 75%
  • Newton-Raphson method 73%
  • Constrained optimization 72%
  4 被引用数 (Scopus)

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

  Nakayama, S., Narushima, Y. & Yabe, H., 2021 5月, In: Computational Optimization and Applications. 79, 1, p. 127-154 28 p.

  研究成果: Article › 査読

  • Proximal Methods 100%
  • Composite function 97%
  • Quasi-Newton Method 94%
  • Newton-Raphson method 83%
  • Objective function 47%
  5 被引用数 (Scopus)
• 2019

  Memoryless quasi-newton methods based on spectral-scaling broyden family for unconstrained optimization

  Nakayama, S., Narushima, Y. & Yabe, H., 2019, In: Journal of Industrial and Management Optimization. 15, 4, p. 1773-1793 21 p.

  研究成果: Article › 査読

  Open Access

  • Quasi-Newton Method 100%
  • Scaling 95%
  • Unconstrained Optimization 88%
  • Newton-Raphson method 88%
  • Spectrality 59%
  3 被引用数 (Scopus)

• Robust supply chain network equilibrium model

  Hirano, T. & Narushima, Y., 2019, In: Transportation Science. 53, 4, p. 1196-1212 17 p.

  研究成果: Article › 査読

  • equilibrium model 100%
  • Network Equilibrium 91%
  • Supply Chain Network 71%
  • Supply chains 65%
  • Variational Inequalities 34%
  4 被引用数 (Scopus)
• 2018

  A memoryless symmetric rank-one method with sufficient descent property for unconstrained optimization

  Nakayama, S., Narushima, Y. & Yabe, H., 2018 1月, In: Journal of the Operations Research Society of Japan. 61, 1, p. 53-70 18 p.

  研究成果: Article › 査読

  Open Access

  • Optimization Problem 100%
  • Large-scale Optimization 93%
  • Scaling 64%
  • Objective Function 50%
  • Matrix 48%
  10 被引用数 (Scopus)
• 2017

  Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

  Kobayashi, H., Narushima, Y. & Yabe, H., 2017 11月 2, In: Optimization Methods and Software. 32, 6, p. 1313-1329 17 p.

  研究成果: Article › 査読

  • Conjugate gradient method 100%
  • Unconstrained Optimization 87%
  • Chord or secant line 85%
  • Conjugate Gradient Method 85%
  • Descent 79%
  10 被引用数 (Scopus)
• 2014

  A family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimization

  Al-Baali, M., Narushima, Y. & Yabe, H., 2014 1月, In: Computational Optimization and Applications. 60, 1, p. 89-110 22 p.

  研究成果: Article › 査読

  • Conjugate gradient method 100%
  • Unconstrained Optimization 87%
  • Conjugate Gradient Method 85%
  • Descent 79%
  • Sufficient 58%
  42 被引用数 (Scopus)

• A survey of sufficient descent conjugate gradient methods for unconstrained optimization

  Narushima, Y. & Yabe, H., 2014 1月 1, In: SUT Journal of Mathematics. 50, 2, p. 167-203 37 p.

  研究成果: Article › 査読

  • Unconstrained Optimization 100%
  • Conjugate Gradient Method 97%
  • Descent 90%
  • Sufficient 66%
  • Large-scale Optimization 39%
  19 被引用数 (Scopus)
• 2013

  A smoothing conjugate gradient method for solving systems of nonsmooth equations

  Narushima, Y., 2013, In: Applied Mathematics and Computation. 219, 16, p. 8646-8655 10 p.

  研究成果: Article › 査読

  • Nonsmooth Equations 100%
  • Conjugate gradient method 92%
  • Conjugate Gradient Method 78%
  • Smoothing 66%
  • Numerical methods 64%
  7 被引用数 (Scopus)

• A smoothing method with appropriate parameter control based on Fischer-Burmeister function for second-order cone complementarity problems

  Narushima, Y., Ogasawara, H. & Hayashi, S., 2013, In: Abstract and Applied Analysis. 2013, 830698.

  研究成果: Article › 査読

  Open Access

  • Second-order Cone 100%
  • Smoothing Methods 96%
  • Complementarity Problem 89%
  • Control Parameter 83%
  • Newton-Raphson method 83%
  3 被引用数 (Scopus)

• Nonlinear conjugate gradient methods with sufficient descent properties for unconstrained optimization

  Nakamura, W., Narushima, Y. & Yabe, H., 2013, In: Journal of Industrial and Management Optimization. 9, 3, p. 595-619 25 p.

  研究成果: Article › 査読

  Open Access

  • Conjugate gradient method 100%
  • Global Convergence 90%
  • Unconstrained Optimization 87%
  • Gradient 86%
  • Conjugate Gradient Method 85%
  14 被引用数 (Scopus)
• 2012

  Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization

  Narushima, Y. & Yabe, H., 2012 11月, In: Journal of Computational and Applied Mathematics. 236, 17, p. 4303-4317 15 p.

  研究成果: Article › 査読

  Open Access

  • Conjugate gradient method 100%
  • Unconstrained Optimization 87%
  • Chord or secant line 85%
  • Conjugate Gradient Method 85%
  • Descent 79%
  38 被引用数 (Scopus)

• Conjugate gradient methods using value of objective function for unconstrained optimization

  Iiduka, H. & Narushima, Y., 2012 6月, In: Optimization Letters. 6, 5, p. 941-955 15 p.

  研究成果: Article › 査読

  • Unconstrained Optimization 100%
  • Gradient 98%
  • Conjugate Gradient Method 97%
  • Objective Function 83%
  • Objective function 75%
  15 被引用数 (Scopus)

• Global and superlinear convergence of inexact sequential quadratically constrained quadratic programming method for convex programming

  Kato, A., Narushima, Y. & Yabe, H., 2012 7月 1, In: Pacific Journal of Optimization. 8, 3, p. 609-629 21 p.

  研究成果: Article › 査読

  • Superlinear Convergence 100%
  • Convex Programming 95%
  • Quadratic programming 94%
  • Quadratic Programming 92%
  • Convex optimization 89%
  2 被引用数 (Scopus)

• Globally Convergent Three-Term Conjugate Gradient Methods that Use Secant Conditions and Generate Descent Search Directions for Unconstrained Optimization

  Sugiki, K., Narushima, Y. & Yabe, H., 2012 6月, In: Journal of Optimization Theory and Applications. 153, 3, p. 733-757 25 p.

  研究成果: Article › 査読

  • Conjugate gradient method 100%
  • Unconstrained Optimization 87%
  • Gradient 86%
  • Chord or secant line 85%
  • Conjugate Gradient Method 85%
  65 被引用数 (Scopus)

• The Jacobian consistency of a smoothed Fischer-Burmeister function associated with second-order cones

  Ogasawara, H. & Narushima, Y., 2012 10月 1, In: Journal of Mathematical Analysis and Applications. 394, 1, p. 231-247 17 p.

  研究成果: Article › 査読

  Open Access

  • Nonsmooth Equations 100%
  • Second-order Cone 95%
  • Complementarity Problem 85%
  • Cones 68%
  • Smoothing Newton Method 53%
  5 被引用数 (Scopus)
• 2011

  A Smoothing Newton Method with Fischer-Burmeister Function for Second-Order Cone Complementarity Problems

  Narushima, Y., Sagara, N. & Ogasawara, H., 2011 2月, In: Journal of Optimization Theory and Applications. 149, 1, p. 79-101 23 p.

  研究成果: Article › 査読

  • Smoothing Newton Method 100%
  • Second-order Cone 90%
  • Complementarity Problem 80%
  • Complementarity 76%
  • Newton-Raphson method 75%
  25 被引用数 (Scopus)

• A three-term conjugate gradient method with sufficient descent property for unconstrained optimization

  Narushima, Y., Yabe, H. & Ford, J. A., 2011, In: SIAM Journal on Optimization. 21, 1, p. 212-230 19 p.

  研究成果: Article › 査読

  • Conjugate gradient method 100%
  • Unconstrained Optimization 87%
  • Conjugate Gradient Method 85%
  • Descent 79%
  • Sufficient 58%
  96 被引用数 (Scopus)

• Symmetric rank-one method based on some modified secant conditions for unconstrained optimization

  Narushima, Y., 2011 12月 1, In: SUT Journal of Mathematics. 47, 1, p. 25-43 19 p.

  研究成果: Article › 査読

  • Unconstrained Optimization 100%
  • Chord or secant line 97%
  • Quasi-Newton Method 37%
  • Trust Region Method 19%
  • Accelerate 16%
• 2010

  Extended Barzilai-Borwein method for unconstrained minimization problems

  Narushima, Y., Wakamatsu, T. & Yabe, H., 2010 11月 9, In: Pacific Journal of Optimization. 6, 3, p. 591-613 23 p.

  研究成果: Article › 査読

  • Unconstrained Minimization 100%
  • Minimization Problem 72%
  • Steepest descent method 48%
  • Gradient methods 46%
  • Steepest Descent Method 16%
  6 被引用数 (Scopus)

• Nonlinear conjugate gradient methods with structured secant condition for nonlinear least squares problems

  Kobayashi, M., Narushima, Y. & Yabe, H., 2010 5月 15, In: Journal of Computational and Applied Mathematics. 234, 2, p. 375-397 23 p.

  研究成果: Article › 査読

  Open Access

  • Nonlinear Least Squares Problem 100%
  • Conjugate gradient method 92%
  • Chord or secant line 78%
  • Conjugate Gradient Method 78%
  • Newton-Raphson method 53%
  22 被引用数 (Scopus)
• 2008

  Multi-step nonlinear conjugate gradient methods for unconstrained minimization

  Ford, J. A., Narushima, Y. & Yabe, H., 2008 6月 1, In: Computational Optimization and Applications. 40, 2, p. 191-216 26 p.

  研究成果: Article › 査読

  • Unconstrained Minimization 100%
  • Conjugate gradient method 98%
  • Conjugate Gradient Method 83%
  • Chord or secant line 63%
  • Large-scale Optimization 25%
  43 被引用数 (Scopus)
• 2007

  A nonmonotone memory gradient method for unconstrained optimization

  Narushima, Y., 2007 3月, In: Journal of the Operations Research Society of Japan. 50, 1, p. 31-45 15 p.

  研究成果: Article › 査読

  Open Access

  • Gradient 100%
  • Large-scale Optimization 25%
  • Search Strategy 18%
  • Objective Function 14%
  2 被引用数 (Scopus)
• 2006

  A memory gradient method without line search for unconstrained optimization

  Narushima, Y., 2006 12月 1, In: SUT Journal of Mathematics. 42, 2, p. 191-206 16 p.

  研究成果: Article › 査読

  • Gradient Method 100%
  • Line Search 99%
  • Unconstrained Optimization 99%
  • Sun 51%
  • Search Strategy 30%
  3 被引用数 (Scopus)

• Global convergence of a memory gradient method for unconstrained optimization

  Narushima, Y. & Yabe, H., 2006 11月, In: Computational Optimization and Applications. 35, 3, p. 325-346 22 p.

  研究成果: Article › 査読

  • Gradient methods 100%
  • Gradient Method 92%
  • Unconstrained Optimization 92%
  • Global Convergence 78%
  • Data storage equipment 50%
  23 被引用数 (Scopus)