An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition

Expand
  • 1. School of Mathematics and Information, North Minzu University, Yinchuan 750030, China;
    2. School of Mathematics Science, Nanjing Normal University, Nanjing 210023, China;
    3. College of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China;
    4. Department of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran;
    5. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, Gauangxi, China

Received date: 2018-03-23

  Revised date: 2018-12-05

  Online published: 2021-06-08

Supported by

This work was supported by First-Class Disciplines Foundation of Ningxia Hui Autonomous Region (No.NXYLXK2017B09),the National Natural Science Foundation of China (Nos.11601012,11861002,71771030),the Key Project of North Minzu University (No.ZDZX201804),Natural Science Foundation of Ningxia Hui Autonomous Region (Nos.NZ17103,2018AAC03253),Natural Science Foundation of Guangxi Zhuang Autonomous Region (No.2018GXNSFAA138169),Guangxi Key Laboratory of Cryptography and Information Security (No.GCIS201708).

Abstract

In this paper, an adaptive three-term conjugate gradient method is proposed for solving unconstrained problems,which generates sufficient descent directions at each iteration. Different from the existent methods, a dynamical adjustment between Hestenes–Stiefel and Dai–Liao conjugacy conditions in our proposed method is developed. Under mild condition, we show that the proposed method converges globally. Numerical experimentation with the new method indicates that it efficiently solves the test problems and therefore is promising.

Cite this article

Xiao-Liang Dong, Zhi-Feng Dai, Reza Ghanbari, Xiang-Li Li . An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition[J]. Journal of the Operations Research Society of China, 2021 , 9(2) : 411 -425 . DOI: 10.1007/s40305-019-00257-w

References

[1] Fletcher, R., Reeves, C.: Function minimization by conjugate gradients. Comput. J. 7(2), 149–154(1964)
[2] Fletcher, R.: Practical Methods of Optimization. Wiley, Hoboken (2013)
[3] Dai, Y.H., Yuan, Y.X.: A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10(1), 177–182(1999)
[4] Gilbert, J.C., Nocedal, J.: Global convergence properties of conjugate gradient methods for optimization. SIAM J. Optim. 2(1), 21–42(1992)
[5] Liu, Y.L., Storey, C.S.: Efficient generalized conjugate gradient algorithms. Part 1: theory. J. Optim. Theory Appl. 69(1), 129–137(1991)
[6] Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49(6), 409–436(1952)
[7] Hager, W.W., Zhang, H.: A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2, 35–58(2006)
[8] Dai,Y.H.: Conjugate gradient methods with Armijo-type line searches. Acta Math. Appl. Sin. (English Series) 18(1), 123–130(2002)
[9] Dai, Y.H., Liu, X.W.: Advances in linear and nonlinear programming. Oper. Res. Trans. (Chin. Ser.) 18(1), 69–92(2014)
[10] Perry, A.: A modified conjugate gradient algorithm. Oper. Res. 26(6), 1073–1078(1976)
[11] Dai, Y.H., Liao, L.Z.: New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43(1), 87–101(2001)
[12] Zhang, J.Z., Deng, N.Y., Chen, L.H.: New quasi-Newton equation and related methods for unconstrained optimization. J. Optim. Theory Appl. 102(1), 147–167(1999)
[13] Li, G., Tang, C., Wei, Z.: New conjugacy condition and related new conjugate gradient methods for unconstrained optimization. J. Comput. Appl. Math. 202(2), 523–539(2007)
[14] Ford, J.A., Narushima, Y., Yabe, H.: Multi-step nonlinear conjugate gradient methods for unconstrained minimization. Comput. Optim. Appl. 40(2), 191–216(2008)
[15] Zhou, W., Zhang, L.: A nonlinear conjugate gradient method based on the MBFGS secant condition. Optim. Methods Softw. 21(5), 707–714(2006)
[16] Babaie-Kafaki, S., Reza, G.: A descent family of Dai–Liao conjugate gradient methods. Optim. Methods Softw. 29(3), 583–591(2014)
[17] Babaie-Kafaki, S., Reza, G.: The Dai–Liao nonlinear conjugate gradient method with optimal parameter choices. Eur. J. Oper. Res. 234(3), 625–630(2014)
[18] Babaie-Kafaki, S., Ghanbari, R.: Two modified three-term conjugate gradient methods with sufficient descent property. Optim. Lett. 8(8), 2285–2297(2014)
[19] Hager, W.W., Zhang, H.: A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16(1), 170–192(2005)
[20] Dai, Y.H., Kou, C.X.: A nonlinear conjugate gradient algorithm with an optimal property and an improved Wolfe line search. SIAM J. Optim. 23(1), 296–320(2013)
[21] Yuan, Y.X., Stoer, J.: A subspace study on conjugate gradient algorithms. ZAMM J. Appl. Math. Mech. 75(1), 69–77(1995)
[22] Dai, Y.H., Kou, C.X.: A Barzilai–Borwein conjugate gradient method. Sci. China Math. 59(8), 1511–1524(2016)
[23] Kou, C.X.: An improved nonlinear conjugate gradient method with an optimal property. Sci. China Math. 57(3), 635–648(2014)
[24] Kou, C.X., Dai, Y.H.: A modified self-scaling memoryless Broyden–Fletcher–Goldfarb–Shanno method for unconstrained optimization. J. Optim. Theory Appl. 165(1), 209–224(2015)
[25] Dong,X.,Liu,H.,He,Y.: A self-adjusting conjugate gradient method with sufficient descent condition and conjugacy condition. J. Optim. Theory Appl. 165(1), 225–241(2015)
[26] Dong, X., Han, D., Dai, Z., Li, X., Zhu, J.: An accelerated three-term conjugate gradient method with sufficient descent condition and conjugacy condition. J. Optim. Theory Appl. 179(3), 944–961(2018)
[27] Dong, X., Liu, H., He, Y.: A modified Hestenes–Stiefel conjugate gradient method with sufficient descent condition and conjugacy condition. J. Comput. Appl. Math. 281, 239–249(2015)
[28] Zhang, L., Zhou, W.J., Li, D.H.: Some descent three-term conjugate gradient methods and their global convergence. Optim. Methods Softw. 22(4), 697–711(2007)
[29] Cheng, W.Y., Li, D.H.: An active set modified Polak-Ribière–Polyak method for large-scale nonlinear bound constrained optimization. J. Optim. Theory Appl. 155(3), 1084–1094(2012)
[30] Narushima, Y., Yabe, H., Ford, J.A.: A three-term conjugate gradient method with sufficient descent property for unconstrained optimization. SIAM J. Optim. 21(1), 212–230(2011)
[31] Andrei, N.: A simple three-term conjugate gradient algorithm for unconstrained optimization. J. Comput. Appl. Math. 241, 19–29(2013)
[32] Andrei, N.: On three-term conjugate gradient algorithms for unconstrained optimization. Appl. Math. Comput. 219(11), 6316–6327(2013)
[33] Andrei, N.: Another conjugate gradient algorithm with guaranteed descent and the conjugacy conditions for large-scaled unconstrained optimization. J. Optim. Theory Appl. 159(3), 159–182(2013)
[34] Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213(2002)
[35] Wolfe, P.: Convergence conditions for ascent methods. SIAM Rev. 11(2), 226–235(1969)
Options
Outlines

/