Research article

A new conjugate gradient method with a restart direction and its application in image restoration

  • Received: 31 August 2023 Revised: 10 October 2023 Accepted: 15 October 2023 Published: 23 October 2023
  • MSC : 65K10, 68U10, 90C06

  • We established a new conjugate gradient method with an efficient restart direction for solving large scale unconstrained optimization problems. The modified method was proposed under the Polak-Ribière-Polyak conjugate gradient method. Under the strong Wolfe line search, the search direction of the new method was sufficiently descent and its global convergence property could be proved. Compared with other methods having good numerical performances, numerical experiments and image restorations showed that the modified method was more effective.

    Citation: Yixin Li, Chunguang Li, Wei Yang, Wensheng Zhang. A new conjugate gradient method with a restart direction and its application in image restoration[J]. AIMS Mathematics, 2023, 8(12): 28791-28807. doi: 10.3934/math.20231475

    Related Papers:

  • We established a new conjugate gradient method with an efficient restart direction for solving large scale unconstrained optimization problems. The modified method was proposed under the Polak-Ribière-Polyak conjugate gradient method. Under the strong Wolfe line search, the search direction of the new method was sufficiently descent and its global convergence property could be proved. Compared with other methods having good numerical performances, numerical experiments and image restorations showed that the modified method was more effective.



    加载中


    [1] M. R. Hestenes, E. L. Steifel, Method of conjugate gradients for solving linear systems, J. Res. Nat. Standard., 49 (1952), 409–436. https://dx.doi.org/10.6028/jres.049.044 doi: 10.6028/jres.049.044
    [2] E. Polak, G. Ribière, Note sur la convergence de méthodes de directions conjuguées, Rev. Fr. Informat Rech. Operationelle 3e Annee., 16 (1969), 35–43. Available from: https://www.esaim-m2an.org/articles/m2an/pdf/1969/01/m2an196903R100351.pdf
    [3] B. T. Polyak, The conjugate gradient method in extremal problems, UssR Comput. Math. Math. Phys., 9 (1969), 94–112. https://dx.doi.org/10.1016/0041-5553(69)90035-4 doi: 10.1016/0041-5553(69)90035-4
    [4] R. Fletcher, C. Reeves, Function minimization by conjugate gradients, Comput. J., 7 (1964), 149–154. https://doi.org/10.1093/comjnl/7.2.149 doi: 10.1093/comjnl/7.2.149
    [5] Y. Liu, C. Storey, Efficient generalized conjugate gradient algorithms, part1: theory., J. Optim. Theory. Appl., 69 (1991), 129–137. https://doi.org/10.1007/BF00940464 doi: 10.1007/BF00940464
    [6] Y. H. Dai, Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property, SIAM J. Optim., 10 (1999), 177–182. https://doi.org/10.1137/s1052623497318992 doi: 10.1137/s1052623497318992
    [7] Z. X. Wei, S. W. Yao, L. Y. Liu, The convergence properties of some new conjugate gradient methods, Appl. Math. Comput., 183 (2006), 1341–1350. https://doi.org/10.1016/j.amc.2006.05.150 doi: 10.1016/j.amc.2006.05.150
    [8] Z. F. Dai, F. H. Wen, Another improved Wei-Yao-Liu nonlinear conjugate gradient method with sufficient descent property, Appl. Math. Comput., 218 (2012), 7421–7430. https://doi.org/10.1016/j.amc.2011.12.091 doi: 10.1016/j.amc.2011.12.091
    [9] P. Wolfe, Convergence conditions for ascent methods, Siam Rev., 11 (1969), 226–235. https://doi.org/10.1137/1011036 doi: 10.1137/1011036
    [10] P. Wolfe, Convergence conditions for ascent methods II: Some Corrections, Siam Rev., 13 (1971), 185–188. https://doi.org/10.1137/1013035 doi: 10.1137/1013035
    [11] Z. Zhu, D. Zhang, S. Wang, Two modified DY conjugate gradient methods for unconstrained optimization problems, Appl. Math. Comput., 373 (2020), 125004. https://doi.org/10.1016/j.amc.2019.125004 doi: 10.1016/j.amc.2019.125004
    [12] L. Pengjie, W. Yanqiang, S. Feng, Z. Yan, S. Hu, Two extended HS-type conjugate gradient methods with restart directions, Acta Math. Sci., 43 (2023), 570–580. Available from: http://121.43.60.238/sxwlxbA/EN/Y2023/V43/I2/570
    [13] Y. H. Dai, C. X. Kou, A modified self-Scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method for unconstrained optimization, J. Optim. Theory. Appl., 165 (2015), 209–224. https://doi.org/10.1007/s10957-014-0528-4 doi: 10.1007/s10957-014-0528-4
    [14] J. C. Gilbert, J. Nocedal, Global convergence properties of conjugate gradient methods for optimization, SIAM J. Optim., 2 (1992), 21–42. https://doi.org/10.1137/0802003 doi: 10.1137/0802003
    [15] G. Zoutendijk, Nonlinear programming computational methods, in: J. Abadie (Ed.), Integer nonlin. Program., (1970), 37–86.
    [16] N. Andrei, Hybrid conjugate gradient algorithm for unconstrained optimization, J. Optim. Theory Appl., 141 (2009), 249–264. https://doi.org/10.1007/s10957-008-9505-0 doi: 10.1007/s10957-008-9505-0
    [17] N. Gould, D. Orban, P. L. Toint, CUTEr and SifDec: A constrained and unconstrained testing environment, ACM Trans. Math. Softw., 29 (2003), 373–394. https://doi.org/10.1145/962437.962439 doi: 10.1145/962437.962439
    [18] J. J. More, B. S. Garbow, K. E. Hillstrom, Testing unconstrained optimization software, ACM T. Math. Softw., 7 (1981), 17–41. https://doi.org/10.1145/355934.355936 doi: 10.1145/355934.355936
    [19] N. Andrei, An unconstrained optimization test functions collection, Adv. Model. Optim., 10 (2008), 147–161.
    [20] E. D. Dolan, J. J. Moré, Benchmarking optimization software with performance profiles, Math. Program., Ser. A., 91 (2002), 201–213. https://doi.org/10.48550/arXiv.cs/0102001 doi: 10.48550/arXiv.cs/0102001
    [21] W. W. Hager, H. C. Zhang, A new conjugate gradient method with guaranteed descent and an efficient line search, Siam J. Optim., 16 (2005), 170–192. https://doi.org/10.1137/030601880 doi: 10.1137/030601880
    [22] Y. H. Dai, C. X. Kou, A nonlinear conjugate gradient algorithm with an optimal property and an improved Wolfe line search, Siam J. Optim., 23 (2013), 296–320. https://doi.org/10.1137/100813026 doi: 10.1137/100813026
    [23] R. H. Chan, C. W. Ho, M. Nikolova, Salt-and-pepper noise removal by median-type noise detectorsand detail-preserving regularization, IEEE Trans. Image Process., 14 (2005), 1479–1485. https://doi.org/10.1109/TIP.2005.852196 doi: 10.1109/TIP.2005.852196
    [24] X. Z. Jiang, W. Liao, J. H. Yin, J. Jian, A new family of hybrid three-term conjugate gradient methods with applications in image restoration, Numer. Algorithms, 91 (2022), 161–191. https://doi.org/10.1007/s11075-022-01258-2 doi: 10.1007/s11075-022-01258-2
    [25] A. C. Bovik, Handbook of image and video processing, Academic press, 2000. https://dl.acm.org/doi/book/10.5555/556230
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1135) PDF downloads(118) Cited by(1)

Article outline

Figures and Tables

Figures(3)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog