We propose an effective conjugate gradient method belonging to the class of Dai–Liao methods for solving unconstrained optimization problems. We employ a variant of the modified secant condition and introduce a new conjugate gradient parameter by solving an optimization problem. The optimization problem combines the well-known features of the linear conjugate gradient method using some penalty functions. This new parameter takes advantage of function information as well as the gradient information to provide the iterations. Our proposed method is globally convergent under mild assumptions. We examine the ability of the method for solving some real-world problems from image processing field. Numerical results show that the proposed method is efficient in the sense of the PSNR test. We also compare our proposed method with some well-known existing algorithms using a collection of CUTEr problems to show its efficiency.
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DOI : 10.1051/ro/2020145
Keywords: Conjugate gradient method, line search, modified secant condition and image processing
@article{RO_2021__55_1_167_0,
author = {Abdollahi, Fahimeh and Fatemi, Masoud},
title = {A new conjugate gradient method based on a modified secant condition with its applications in image processing},
journal = {RAIRO. Operations Research},
pages = {167--187},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {1},
doi = {10.1051/ro/2020145},
mrnumber = {4228693},
zbl = {1471.90091},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2020145/}
}
TY - JOUR AU - Abdollahi, Fahimeh AU - Fatemi, Masoud TI - A new conjugate gradient method based on a modified secant condition with its applications in image processing JO - RAIRO. Operations Research PY - 2021 SP - 167 EP - 187 VL - 55 IS - 1 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2020145/ DO - 10.1051/ro/2020145 LA - en ID - RO_2021__55_1_167_0 ER -
%0 Journal Article %A Abdollahi, Fahimeh %A Fatemi, Masoud %T A new conjugate gradient method based on a modified secant condition with its applications in image processing %J RAIRO. Operations Research %D 2021 %P 167-187 %V 55 %N 1 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2020145/ %R 10.1051/ro/2020145 %G en %F RO_2021__55_1_167_0
Abdollahi, Fahimeh; Fatemi, Masoud. A new conjugate gradient method based on a modified secant condition with its applications in image processing. RAIRO. Operations Research, Tome 55 (2021) no. 1, pp. 167-187. doi: 10.1051/ro/2020145
[1] and , A new structured quasi-newton algorithm using partial information on hessian. J. Comput. Appl. Math. 234 (2010) 805–811. | MR | Zbl | DOI
[2] , Numerical comparison of conjugate gradient algorithms for unconstrained optimization. Stud. Inf. Control 16 (2007) 333–352.
[3] , Conjugate gradient algorithms for unconstrained optimization. A survey on their definition. Technical report, ICI Technical Report (2008).
[4] , Accelerated conjugate gradient algorithm with modified secant condition for unconstrained optimization. Stud. Inf. Control 18 (2009) 211–232.
[5] , Accelerated hybrid conjugate gradient algorithm with modified secant condition for unconstrained optimization. Numer. Algorithms 54 (2010) 23–46. | MR | Zbl | DOI
[6] , A numerical study on efficiency and robustness of some conjugate gradient algorithms for large-scale unconstrained optimization. Stud. Inf. Control 22 (2013) 259–284.
[7] , A Dai-Liao conjugate gradient algorithm with clustering of eigenvalues. Numer. Algorithms 77 (2018) 1273–1282. | MR | Zbl | DOI
[8] , Two modified scaled nonlinear conjugate gradient methods. J. Comput. Appl. Math. 261 (2014) 172–182. | MR | Zbl | DOI
[9] and , The Dai-Liao nonlinear conjugate gradient method with optimal parameter choices. Eur. J. Oper. Res. 234 (2014) 625–630. | MR | Zbl | DOI
[10] and , A descent family of Dai-Liao conjugate gradient methods. Optim. Methods Softw. 29 (2014) 583–591. | MR | Zbl | DOI
[11] and , Two modified hybrid conjugate gradient methods based on a hybrid secant equation. Math. Model. Anal. 18 (2013) 32–52. | MR | Zbl | DOI
[12] , and , Two new conjugate gradient methods based on modified secant equations. J. Comput. Appl. Math. 234 (2010) 1374–1386. | MR | Zbl | DOI
[13] , and , Two effective hybrid conjugate gradient algorithms based on modified bfgs updates. Numer. Algorithms 58 (2011) 315–331. | MR | Zbl | DOI
[14] , Handbook of Image and Video Processing. Academic Press (2010). | Zbl
[15] , and , From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51 (2009) 34–81. | MR | Zbl | DOI
[16] and , New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43 (2001) 87–101. | MR | Zbl | DOI
[17] and , A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10 (1999) 177–182. | MR | Zbl | DOI
[18] and , Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. | MR | Zbl | DOI
[19] , and , 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. | MR | Zbl
[20] , , , and , An accelerated three-term conjugate gradient method with sufficient descent condition and conjugacy condition. J. Optim. Theory Appl. 179 (2018) 944–961. | MR | Zbl | DOI
[21] , A new efficient conjugate gradient method for unconstrained optimization. J. Comput. Appl. Math. 300 (2016) 207–216. | MR | Zbl | DOI
[22] , Practical Methods of Optimization. John Wiley & Sons, New York, NY (2013). | MR | Zbl
[23] and , Function minimization by conjugate gradients. Comput. J. 7 (1964) 149–154. | MR | Zbl | DOI
[24] , and , CUTEr and SifDec: A constrained and unconstrained testing environment, revisited. ACM Trans. Math. Soft. 29 (2003) 373–394. | Zbl | DOI
[25] and , A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16 (2005) 170–192. | MR | Zbl | DOI
[26] and , Algorithm 851: CG_DESCENT, a conjugate gradient method with guaranteed descent. ACM Trans. Math. Softw. 32 (2006) 113–137. | MR | Zbl | DOI
[27] and , A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2 (2006) 35–58. | MR | Zbl
[28] , and , Deblurring Images: Matrices, Spectra, and Filtering. Vol. 3 of: Fundamentals of Algorithms. SIAM (2006). | MR | Zbl
[29] and , Methods of Conjugate Gradients for Solving Linear Systems. J. Res. Nat. l Bur. Standards 49 (1952) 2379. | MR | Zbl
[30] and , Improved Fletcher-Reeves and Dai-Yuan conjugate gradient methods with the strong wolfe line search. J. Comput. Appl. Math. 348 (2019) 525–534. | MR | Zbl | DOI
[31] , and , New conjugacy condition and related new conjugate gradient methods for unconstrained optimization. J. Comput. Appl. Math. 202 (2007) 523–539. | MR | Zbl | DOI
[32] and , Efficient generalized conjugate gradient algorithms, part 1: theory. J. Optim. Theory Appl. 69 (1991) 129–137. | MR | Zbl | DOI
[33] , Out-of-focus Blur: Image De-blurring. Preprint: arXiv:1710.00620 (2017).
[34] and , Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization. J. Comput. Appl. Math. 236 (2012) 4303–4317. | MR | Zbl | DOI
[35] , The conjugate gradient method in extremal problems. Comput. Math. Math. Phys. 9 (1969) 94–112. | Zbl | DOI
[36] , and , A survey on different image deblurring techniques. Int. J. Comput. Math. 116 (2015) 15–18.
[37] , and , New quasi-newton methods for unconstrained optimization problems. Appl. Math. Comput. 175 (2006) 1156–1188. | MR | Zbl
[38] , and , Iterative algorithms based on decoupling of deblurring and denoising for image restoration. SIAM J. Sci. Comput. 30 (2008) 2655–2674. | MR | Zbl | DOI
[39] and , Global convergence properties of nonlinear conjugate gradient methods with modified secant condition. Comput. Optim. Appl. 28 (2004) 203–225. | MR | Zbl | DOI
[40] and , Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations. J. Comput. Appl. Math. 137 (2001) 269–278. | MR | Zbl | DOI
[41] , and , New quasi-newton equation and related methods for unconstrained optimization. J. Optim. Theory Appl. 102 (1999) 147–167. | MR | Zbl | DOI
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