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

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.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2020145
Classification : 90C06, 90C26, 65Y20
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] 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. | MR | Zbl | DOI

[2] N. Andrei, Numerical comparison of conjugate gradient algorithms for unconstrained optimization. Stud. Inf. Control 16 (2007) 333–352.

[3] N. Andrei, Conjugate gradient algorithms for unconstrained optimization. A survey on their definition. Technical report, ICI Technical Report (2008).

[4] N. Andrei, Accelerated conjugate gradient algorithm with modified secant condition for unconstrained optimization. Stud. Inf. Control 18 (2009) 211–232.

[5] N. Andrei, Accelerated hybrid conjugate gradient algorithm with modified secant condition for unconstrained optimization. Numer. Algorithms 54 (2010) 23–46. | MR | Zbl | DOI

[6] 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.

[7] N. Andrei, A Dai-Liao conjugate gradient algorithm with clustering of eigenvalues. Numer. Algorithms 77 (2018) 1273–1282. | MR | Zbl | DOI

[8] S. Babaie-Kafaki, Two modified scaled nonlinear conjugate gradient methods. J. Comput. Appl. Math. 261 (2014) 172–182. | MR | Zbl | DOI

[9] 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. | MR | Zbl | DOI

[10] S. Babaie-Kafaki and R. Ghanbari, A descent family of Dai-Liao conjugate gradient methods. Optim. Methods Softw. 29 (2014) 583–591. | MR | Zbl | DOI

[11] 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. | MR | Zbl | DOI

[12] 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. | MR | Zbl | DOI

[13] 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. | MR | Zbl | DOI

[14] A. C. Bovik, Handbook of Image and Video Processing. Academic Press (2010). | Zbl

[15] 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. | MR | Zbl | DOI

[16] Y.-H. Dai and L.-Z. Liao, New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43 (2001) 87–101. | MR | Zbl | DOI

[17] Y.-H. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10 (1999) 177–182. | MR | Zbl | DOI

[18] E. D. Dolan and J. J. Moré, Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. | MR | Zbl | DOI

[19] 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. | MR | Zbl

[20] 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. | MR | Zbl | DOI

[21] M. Fatemi, A new efficient conjugate gradient method for unconstrained optimization. J. Comput. Appl. Math. 300 (2016) 207–216. | MR | Zbl | DOI

[22] R. Fletcher, Practical Methods of Optimization. John Wiley & Sons, New York, NY (2013). | MR | Zbl

[23] R. Fletcher and C. M. Reeves, Function minimization by conjugate gradients. Comput. J. 7 (1964) 149–154. | MR | Zbl | DOI

[24] 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. | Zbl | DOI

[25] 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. | MR | Zbl | DOI

[26] 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. | MR | Zbl | DOI

[27] W. W. Hager and H. Zhang, A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2 (2006) 35–58. | MR | Zbl

[28] 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). | MR | Zbl

[29] M. R. Hestenes and E. Stiefel, Methods of Conjugate Gradients for Solving Linear Systems. J. Res. Nat. l Bur. Standards 49 (1952) 2379. | MR | Zbl

[30] 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. | MR | Zbl | DOI

[31] 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. | MR | Zbl | DOI

[32] Y. Liu and C. Storey, Efficient generalized conjugate gradient algorithms, part 1: theory. J. Optim. Theory Appl. 69 (1991) 129–137. | MR | Zbl | DOI

[33] Y. Lu, Out-of-focus Blur: Image De-blurring. Preprint: arXiv:1710.00620 (2017).

[34] 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. | MR | Zbl | DOI

[35] B. T. Polyak, The conjugate gradient method in extremal problems. Comput. Math. Math. Phys. 9 (1969) 94–112. | Zbl | DOI

[36] F. Vankawala, A. Ganatra and A. Patel, A survey on different image deblurring techniques. Int. J. Comput. Math. 116 (2015) 15–18.

[37] Z. Wei, G. Li and L. Qi, New quasi-newton methods for unconstrained optimization problems. Appl. Math. Comput. 175 (2006) 1156–1188. | MR | Zbl

[38] 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. | MR | Zbl | DOI

[39] H. Yabe and M. Takano, Global convergence properties of nonlinear conjugate gradient methods with modified secant condition. Comput. Optim. Appl. 28 (2004) 203–225. | MR | Zbl | DOI

[40] 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. | MR | Zbl | DOI

[41] 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. | MR | Zbl | DOI

Cité par Sources :