Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 6, November-December 2021
Page(s) 3281 - 3291
DOI https://doi.org/10.1051/ro/2021159
Published online 15 November 2021
  • N. Andrei, An unconstrained optimization test functions collection. Adv. Model. Optim. 10 (2008) 147–161. [Google Scholar]
  • Y. Cheng, Q. Mou, X. Pan and S. Yao, A sufficient descent conjugate gradient method and its global convergence. Optim. Methods Softw. 31 (2016) 577–590. [Google Scholar]
  • Y. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10 (1999) 177–182. [Google Scholar]
  • Y. Dai, Nonlinear conjugate gradient methods, in Wiley Encyclopedia of Operations Research and Management Science (2011). DOI:10.1002/9780470400531.eorms0183. [Google Scholar]
  • Y. Dai.Y. Huang, X. Liu, A family of spectral gradient methods for optimization. Comput. Optim. Appl. 74 (2019) 43–65. [Google Scholar]
  • Y. Dai and L. Liao, New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43 (2001) 87–101. [Google Scholar]
  • E. Dolan and J. Moré, Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. [Google Scholar]
  • R. Fletcher, Function minimization by conjugate gradients. Comput. J. 7 (1964) 149–154. [Google Scholar]
  • J. Ford, Y. Narushima and H. Yabe, Multi-step nonlinear conjugate gradient methods for unconstrained minimization. Comput. Optim. Appl. 40 (2008) 191–216. [Google Scholar]
  • J. Gilbert and J. Nocedal, Global convergence properties of conjugate gradient methods for optimization. SIAM J. Optim. 2 (1992) 21–42. [Google Scholar]
  • N. Gould, D. Orban and Ph Toint, CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization. Comput. Optim. Appl. 60 (2015) 545–557. [Google Scholar]
  • W. Hager and H. Zhang, A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16 (2005) 170–192. [Google Scholar]
  • W. Hager and H. Zhang, A survey of nonlinear conjugate gradient methods. Pacific J. Optim. 2 (2006) 35–58. [Google Scholar]
  • M. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems. J. Res. Nat. Bur. Stand. 49 (1952) 409–436. [Google Scholar]
  • Y. Huang, Y. Dai, X. Liu and H. Zhang, Gradient methods exploiting spectral properties. Optim. Methods Softw. 35 (2020) 681–705. [Google Scholar]
  • C. Kou and Y. Dai, A modified self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno method for unconstrained optimization. J. Optim. Theory Appl. 165 (2015) 209–224. [Google Scholar]
  • G. Li, C. Tang and Z. Wei, New conjugacy condition and related new conjugate gradient methods for unconstrained optimization. J. Comput. Appl. Math. 202 (2007) 523–539. [Google Scholar]
  • E. Polak and G. Ribiere, Note sur la convergence de méthodes de directions conjuguées. ESAIM: M2AN 3 (1969) 35–43. [Google Scholar]
  • B. Polyak, The conjugate gradient method in extremal problems. USSR Comput. Math. Math. Phys. 9 (1969) 94–112. [Google Scholar]
  • H. Yabe and M. Takano, Global convergence properties of nonlinear conjugate gradient methods with modified secant condition. Comput. Optim. Appl. 28 (2004) 203–225. [MathSciNet] [Google Scholar]
  • J. Zhang and C. Xu, Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations. J. Comput. Appl. Math. 137 (2001) 269–278. [Google Scholar]
  • K. Zhang, H. Liu and Z. Liu, A new Dai-Liao conjugate gradient method with optimal parameter choice. Numer. Funct. Anal. Optim. 40 (2019) 194–215. [Google Scholar]
  • Y. Zheng and B. Zheng, Two new Dai-Liao–type conjugate gradient methods for unconstrained optimization problems. J. Optim. Theory Appl. 175 (2017) 502–509. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.