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All issues Volume 55 / No 1 (January-February 2021) RAIRO-Oper. Res., 55 1 (2021) 167-187 References
Free Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 1, January-February 2021
Page(s) 167 - 187
DOI https://doi.org/10.1051/ro/2020145
Published online 12 March 2021
  • K. Amini and A.G. Rizi, A new structured quasi-newton algorithm using partial information on hessian. J. Comput. Appl. Math. 234 (2010) 805–811. [Google Scholar]
  • N. Andrei, Numerical comparison of conjugate gradient algorithms for unconstrained optimization. Stud. Inf. Control 16 (2007) 333–352. [Google Scholar]
  • N. Andrei, Conjugate gradient algorithms for unconstrained optimization. A survey on their definition. Technical report, ICI Technical Report (2008). [Google Scholar]
  • N. Andrei, Accelerated conjugate gradient algorithm with modified secant condition for unconstrained optimization. Stud. Inf. Control 18 (2009) 211–232. [Google Scholar]
  • N. Andrei, Accelerated hybrid conjugate gradient algorithm with modified secant condition for unconstrained optimization. Numer. Algorithms 54 (2010) 23–46. [Google Scholar]
  • N. Andrei, A numerical study on efficiency and robustness of some conjugate gradient algorithms for large-scale unconstrained optimization. Stud. Inf. Control 22 (2013) 259–284. [Google Scholar]
  • N. Andrei, A Dai-Liao conjugate gradient algorithm with clustering of eigenvalues. Numer. Algorithms 77 (2018) 1273–1282. [Google Scholar]
  • S. Babaie-Kafaki, Two modified scaled nonlinear conjugate gradient methods. J. Comput. Appl. Math. 261 (2014) 172–182. [Google Scholar]
  • S. Babaie-Kafaki and R. Ghanbari, The Dai-Liao nonlinear conjugate gradient method with optimal parameter choices. Eur. J. Oper. Res. 234 (2014) 625–630. [Google Scholar]
  • S. Babaie-Kafaki and R. Ghanbari, A descent family of Dai-Liao conjugate gradient methods. Optim. Methods Softw. 29 (2014) 583–591. [Google Scholar]
  • S. Babaie-Kafaki and N. Mahdavi-Amiri, Two modified hybrid conjugate gradient methods based on a hybrid secant equation. Math. Model. Anal. 18 (2013) 32–52. [Google Scholar]
  • S. Babaie-Kafaki, R. Ghanbari and N. Mahdavi-Amiri, Two new conjugate gradient methods based on modified secant equations. J. Comput. Appl. Math. 234 (2010) 1374–1386. [MathSciNet] [Google Scholar]
  • S. Babaie-Kafaki, M. Fatemi and N. Mahdavi-Amiri, Two effective hybrid conjugate gradient algorithms based on modified bfgs updates. Numer. Algorithms 58 (2011) 315–331. [Google Scholar]
  • A.C. Bovik, Handbook of Image and Video Processing. Academic Press (2010). [Google Scholar]
  • A.M. Bruckstein, D.L. Donoho and M. Elad, From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51 (2009) 34–81. [NASA ADS] [CrossRef] [Google Scholar]
  • Y.-H. Dai and L.-Z. Liao, New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43 (2001) 87–101. [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. [Google Scholar]
  • E.D. Dolan and J.J. Moré, Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. [Google Scholar]
  • X.L. Dong, H.W. Liu and Y.B. He, New version of the three-term conjugate gradient method based on spectral scaling conjugacy condition that generates descent search direction. Appl. Math. Comput. 269 (2015) 606–617. [MathSciNet] [Google Scholar]
  • X. Dong, D. Han, Z. Dai, L. Li and J. Zhu, An accelerated three-term conjugate gradient method with sufficient descent condition and conjugacy condition. J. Optim. Theory Appl. 179 (2018) 944–961. [Google Scholar]
  • M. Fatemi, A new efficient conjugate gradient method for unconstrained optimization. J. Comput. Appl. Math. 300 (2016) 207–216. [Google Scholar]
  • R. Fletcher, Practical Methods of Optimization. John Wiley & Sons, New York, NY (2013). [Google Scholar]
  • R. Fletcher and C.M. Reeves, Function minimization by conjugate gradients. Comput. J. 7 (1964) 149–154. [Google Scholar]
  • N.I. Gould, D. Orban and P.L. Toint, CUTEr and SifDec: A constrained and unconstrained testing environment, revisited. ACM Trans. Math. Soft. 29 (2003) 373–394. [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]
  • W.W. Hager and H. Zhang, Algorithm 851: CG_DESCENT, a conjugate gradient method with guaranteed descent. ACM Trans. Math. Softw. 32 (2006) 113–137. [Google Scholar]
  • W.W. Hager and H. Zhang, A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2 (2006) 35–58. [MathSciNet] [Google Scholar]
  • P.C. Hansen, J.G. Nagy and D.P. O’leary, Deblurring Images: Matrices, Spectra, and Filtering. Vol. 3 of: Fundamentals of Algorithms. SIAM (2006). [Google Scholar]
  • M.R. Hestenes and E. Stiefel, Methods of Conjugate Gradients for Solving Linear Systems. J. Res. Nat. l Bur. Standards 49 (1952) 2379. [Google Scholar]
  • X. Jiang and J. Jian, Improved Fletcher-Reeves and Dai-Yuan conjugate gradient methods with the strong wolfe line search. J. Comput. Appl. Math. 348 (2019) 525–534. [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]
  • Y. Liu and C. Storey, Efficient generalized conjugate gradient algorithms, part 1: theory. J. Optim. Theory Appl. 69 (1991) 129–137. [Google Scholar]
  • Y. Lu, Out-of-focus Blur: Image De-blurring. Preprint: arXiv:1710.00620 (2017). [Google Scholar]
  • Y. Narushima and H. Yabe, Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization. J. Comput. Appl. Math. 236 (2012) 4303–4317. [MathSciNet] [Google Scholar]
  • B.T. Polyak, The conjugate gradient method in extremal problems. Comput. Math. Math. Phys. 9 (1969) 94–112. [Google Scholar]
  • F. Vankawala, A. Ganatra and A. Patel, A survey on different image deblurring techniques. Int. J. Comput. Math. 116 (2015) 15–18. [Google Scholar]
  • Z. Wei, G. Li and L. Qi, New quasi-newton methods for unconstrained optimization problems. Appl. Math. Comput. 175 (2006) 1156–1188. [Google Scholar]
  • Y.-W. Wen, M.K. Ng and W.-K. Ching, Iterative algorithms based on decoupling of deblurring and denoising for image restoration. SIAM J. Sci. Comput. 30 (2008) 2655–2674. [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]
  • J. Zhang, N. Deng and L. Chen, New quasi-newton equation and related methods for unconstrained optimization. J. Optim. Theory Appl. 102 (1999) 147–167. [Google Scholar]

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