Open Access
Issue |
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
|
|
---|---|---|
Page(s) | 2315 - 2327 | |
DOI | https://doi.org/10.1051/ro/2022109 | |
Published online | 29 July 2022 |
- A. Abubakar, P. Kumam, M. Malik, P. Chaipunya and A. Ibrahim, A hybrid FR-DY conjugate gradient algorithm for unconstrained optimization with application in portfolio selection. AIMS Math. 66 (2021) 126. [Google Scholar]
- M. Al-Baali, Descent property and global convergence of the Fletcher-Reeves method with inexact line search. IMA J. Numer. Anal. 5 (1985) 121–124. [CrossRef] [MathSciNet] [Google Scholar]
- N. Andrei, Another nonlinear conjugate gradient algorithm for unconstrained optimization. Optim. Methods Softw. 24 (2008) 89–104. [Google Scholar]
- N. Andrei, An unconstrained optimization test functions collection. Adv. Model. Optim. 10 (2008) 147–161. [MathSciNet] [Google Scholar]
- N. Andrei, Accelerated hybrid conjugate gradient algorithm with modified secant condition for unconstrained optimization. Numer. Algorithms 54 (2010) 23–46. [Google Scholar]
- B. Balaram, M. Narayanan and P. Rajendrakumar, Optimal design of multi-parametric nonlinear systems using a parametric continuation based genetic algorithm approach. Nonlinear Dyn. 67 (2012) 2759–2777. [CrossRef] [Google Scholar]
- I. Bongartz, A.R. Conn, N. Gould and P.L. Toint, CUTE: constrained and unconstrained testing environments. ACM Trans. Math. Softw. (TOMS) 21 (1995) 123–160. [CrossRef] [Google Scholar]
- Y.H. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10 (1999) 177–182. [CrossRef] [MathSciNet] [Google Scholar]
- S.S. Djordjević, New hybrid conjugate gradient method as a convex combination of FR and PRP methods. Filomat 30 (2016) 3083–3100. [CrossRef] [MathSciNet] [Google Scholar]
- S.S. Djordjević, New hybrid conjugate gradient method as a convex combination of LS and FR conjugate gradient methods. Acta Math. Sci. 39 (2019) 214–228. [CrossRef] [MathSciNet] [Google Scholar]
- E.D. Dolan and J.J. Moré, Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. [Google Scholar]
- R. Fletcher, Practical Methods of Optimization, Unconstrained Optimization. Vol. 1. John Wiley and Son, New York (1980). [Google Scholar]
- R. Fletcher and C. Reeves, Function minimization by conjugate gradients. Comput. J. 7 (1964) 149–154. [CrossRef] [MathSciNet] [Google Scholar]
- W.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]
- M.R. Hestenes and E.L. Stiefel, Methods of conjugate gradients for solving linear systems. J. Res. Nat. Bur. Standards 49 (1952) 409–436. [CrossRef] [Google Scholar]
- J.K. Liu and S.J. Li, New hybrid conjugate gradient method for unconstrained optimization. Appl. Math. Comput. 245 (2014) 36–43. [MathSciNet] [Google Scholar]
- D. Liu and C. Story, Efficient generalized conjugate gradient algorithms, Part 1: theory. J. Optim. Theory App. 69 (1991) 129–137. [CrossRef] [Google Scholar]
- P.K.-H. Phua, W. Fan and Y. Zeng, Parallel algorithms for large-scale nonlinear optimization. Int. Trans. Oper. Res. 5 (1998) 67–77. [CrossRef] [Google Scholar]
- E. Polak and G. Ribiere, Note sur la convergence de méthodes de directions conjuguées. Revue Française D’informatique et De Recherche Opérationnelle, Série Rouge 3 (1969) 35–43. [Google Scholar]
- B.T. Polyak, The conjugate gradient method in extremal problems. USSR Comput. Math. Math. Phys. 9 (1969) 94–112. [CrossRef] [Google Scholar]
- Z. Salleh and A. Alhawarat, An efficient modification of the Hestenes-Stiefel nonlinear conjugate gradient method with restart property. J. Inequalities App. 2016 (2016) 1–14. [CrossRef] [Google Scholar]
- Z. Wei, G. Li and L. Qi, New nonlinear conjugate gradient formulas for large-scale unconstrained optimization problems. Appl. Math. Comput. 179 (2006) 407–430. [MathSciNet] [Google Scholar]
- P. Wolfe, Convergence conditions for ascent methods. SIAM Rev. 11 (1969) 226–235. [CrossRef] [MathSciNet] [Google Scholar]
- P. Wolfe, Convergence conditions for ascent methods. II: some corrections. SIAM Rev. 13 (1971) 185–188. [CrossRef] [MathSciNet] [Google Scholar]
- H. Yan, L. Chen and B. Jiao, HS-LS-CD hybrid conjugate gradient algorithm for unconstrained optimization. In: Second International Workshop on Computer Science and Engineering. Vol. 1. IEEE (2009) 264–268. [Google Scholar]
- G. Yuan, Z. Wei and Q. Zhao, A modified Polak–Ribiere–Polyak conjugate gradient algorithm for large scale optimization problems. IIE Trans. 46 (2014) 397–413. [CrossRef] [Google Scholar]
- X.Y. Zheng, X.L. Dong, J.R. Shi and W. Yang, Further comment on another hybrid conjugate gradient algorithm for unconstrained optimization by Andrei. Numer. Algorithm 84 (2019) 603–608. [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.