A modified Perry conjugate gradient method and its global convergence
Original Paper
First Online:
Received:
Accepted:
- 289 Downloads
- 1 Citations
Abstract
In this work, we propose a new conjugate gradient method which consists of a modification of Perry’s method and ensures sufficient descent independent of the accuracy of the line search. An important property of our proposed method is that it achieves a high-order accuracy in approximating the second order curvature information of the objective function by utilizing a new modified secant condition. Moreover, we establish that the proposed method is globally convergent for general functions provided that the line search satisfies the Wolfe conditions. Our numerical experiments indicate that our proposed method is preferable and in general superior to classical conjugate gradient methods in terms of efficiency and robustness.
Keywords
Unconstrained optimization Conjugate gradient method Sufficient descent property Hybrid secant equation Global convergenceReferences
- 1.Andrei, N.: Scaled conjugate gradient algorithms for unconstrained optimization. Comput. Optim. Appl. 38, 401–416 (2007)CrossRefMATHMathSciNetGoogle Scholar
- 2.Andrei, N.: Scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization. Optim. Methods Softw. 22, 561–571 (2007)CrossRefMATHMathSciNetGoogle Scholar
- 3.Andrei, N.: A new three-term conjugate gradient algorithm for unconstrained optimization. Numer. Algorithms 22, 1–17 (2014)Google Scholar
- 4.Babaie-Kafaki, S.: A modified BFGS algorithm based on a hybrid secant equation. Sci. China Math. 54(9), 2019–2036 (2011)CrossRefMATHMathSciNetGoogle Scholar
- 5.Babaie-Kafaki, S.: Two modified scaled nonlinear conjugate gradient methods. J. Comput. Appl. Math. 261, 172–182 (2014)CrossRefMATHMathSciNetGoogle Scholar
- 6.Babaie-Kafaki, S., Ghanbari, R.: The Dai–Liao nonlinear conjugate gradient method with optimal parameter choices. Eur. J. Oper. Res. 234(3), 625–630 (2014)CrossRefMATHMathSciNetGoogle Scholar
- 7.Babaie-Kafaki, S., Ghanbari, R., Mahdavi-Amiri, N.: Two new conjugate gradient methods based on modified secant equations. J. Comput. Appl. Math. 234, 1374–1386 (2010)CrossRefMATHMathSciNetGoogle Scholar
- 8.Babaie-Kafaki, S., Ghanbari, R., Mahdavi-Amiri, N.: Two effective hybrid conjugate gradient algorithms based on modified BFGS updates. Numer. Algorithms 58(3), 315–331 (2011)CrossRefMATHMathSciNetGoogle Scholar
- 9.Babaie-Kafaki, S., Mahdavi-Amiri, N.: Two hybrid nonlinear conjugate gradient methods based on a modified secant equation. Optimization, 63(7), 1–16 (2012)Google Scholar
- 10.Babaie-Kafaki, S., Mahdavi-Amiri, N.: Two modified hybrid conjugate gradient methods based on a hybrid secant equation. Math. Model. Anal. 18(1), 32–52 (2013)CrossRefMATHMathSciNetGoogle Scholar
- 11.Birgin, E.G., Martínez, J.M.: A spectral conjugate gradient method for unconstrained optimization. Appl. Math. Optim. 43, 117–128 (1999)CrossRefGoogle Scholar
- 12.Bongartz, I., Conn, A., Gould, N., Toint, P.: CUTE: constrained and unconstrained testing environments. ACM Trans. Math. Softw. 21(1), 123–160 (1995)CrossRefMATHGoogle Scholar
- 13.Chen, W., Liu, Q.: Sufficient descent nonlinear conjugate gradient methods with conjugacy condition. Numer. Algorithms 53, 113–131 (2010)CrossRefGoogle Scholar
- 14.Dai, Y.H., Yuan, Y.X.: A nonlinear conjugate gradient with a strong global convergence properties. SIAM J. Optim. 10, 177–182 (1999)CrossRefMATHMathSciNetGoogle Scholar
- 15.Dai, Y.H., Yuan, Y.X.: Nonlinear conjugate gradient methods. Shanghai Scientific and Technical Publishers, Shanghai (2000)Google Scholar
- 16.Dai, Z., Wen, F.: A modified CG-DESCENT method for unconstrained optimization. J. Comput. Appl. Math. 235(11), 3332–3341 (2011)CrossRefMATHMathSciNetGoogle Scholar
- 17.Dai, Z.F., Tian, B.S.: Global convergence of some modified PRP nonlinear conjugate gradient methods. Optim. Lett. 5(4), 1–16 (2010)Google Scholar
- 18.Dolan, E., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)CrossRefMATHMathSciNetGoogle Scholar
- 19.Du, S.Q., Chen, Y.Y.: Global convergence of a modified spectral FR conjugate gradient method. Appl. Math. Comput. 202(2), 766–770 (2008)CrossRefMATHMathSciNetGoogle Scholar
- 20.Fletcher, R., Reeves, C.M.: Function minimization by conjugate gradients. Comput. J. 7, 149–154 (1964)CrossRefMATHMathSciNetGoogle Scholar
- 21.Ford, J.A., Narushima, Y., Yabe, H.: Multi-step nonlinear conjugate gradient methods for unconstrained minimization. Comput. Optim. Appl. 40, 191–216 (2008)CrossRefMATHMathSciNetGoogle Scholar
- 22.Gilbert, J.C., Nocedal, J.: Global convergence properties of conjugate gradient methods for optimization. SIAM J. Optim. 2(1), 21–42 (1992)CrossRefMATHMathSciNetGoogle Scholar
- 23.Hager, W.W., Zhang, H.: The limited memory conjugate gradient method. SIAM J. Optim. 23(4), 2150–2168 (2005)CrossRefMathSciNetGoogle Scholar
- 24.Hager, W.W., Zhang, H.: A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16, 170–192 (2005)CrossRefMATHMathSciNetGoogle Scholar
- 25.Hager, W.W., Zhang, H.: A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2, 35–58 (2006)MATHMathSciNetGoogle Scholar
- 26.Hestenes, M.R., Stiefel, E.: Methods for conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49, 409–436 (1952)CrossRefMATHMathSciNetGoogle Scholar
- 27.Li, D.H., Fukushima, M.: A modified BFGS method and its global convergence in nonconvex minimization. J.Comput. Appl. Math. 129, 15–35 (2001)CrossRefMATHMathSciNetGoogle Scholar
- 28.Li, G., Tang, C., Wei, Z.: New conjugacy condition and related new conjugate gradient methods for unconstrained optimization. J. Comput. Appl. Math. 202, 523–539 (2007)CrossRefMATHMathSciNetGoogle Scholar
- 29.Livieris, I.E., Pintelas, P.: Globally convergent modified Perry conjugate gradient method. Appl. Math. Comput. 218(18), 9197–9207 (2012)CrossRefMATHMathSciNetGoogle Scholar
- 30.Livieris, I.E., Pintelas, P.: A new class of spectral conjugate gradient methods based on a modified secant equation for unconstrained optimization. J. Comput. Appl. Math. 239, 396–405 (2013)CrossRefMATHMathSciNetGoogle Scholar
- 31.Lu, A., Liu, H., Zheng, X., Cong, W.: A variant spectral-type FR conjugate gradient method and its global convergence. Appl. Math. Comput. 217(12), 5547–5552 (2011)CrossRefMATHMathSciNetGoogle Scholar
- 32.Narushima, Y., Yabe, H.: Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization. J. Comput. Appl. Math. 236(17), 4303–4317 (2012)CrossRefMATHMathSciNetGoogle Scholar
- 33.Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)CrossRefMATHGoogle Scholar
- 34.Perry, A.: A modified conjugate gradient algorithm. Oper. Res. 26, 1073–1078 (1978)CrossRefMATHMathSciNetGoogle Scholar
- 35.Polak, E., Ribière, G.: Note sur la convergence de methods de directions conjuguees. Revue Francais d’Informatique et de Recherche Operationnelle 16, 35–43 (1969)Google Scholar
- 36.Powell, M.J.D.: Restart procedures for the conjugate gradient method. Math. Program. 12, 241–254 (1977)CrossRefMATHGoogle Scholar
- 37.Shanno, D.F., Phua, K.H.: Minimization of unconstrained multivariate functions. ACM Trans. Math. Softw. 2, 87–94 (1976)CrossRefMATHGoogle Scholar
- 38.Sugiki, K., Narushima, Y., Yabe, H.: Globally convergent three-term conjugate gradient methods that use secant conditions and generate descent search directions for unconstrained optimization. J. Optim. Theory Appl. 153(3), 733–757 (2012)CrossRefMATHMathSciNetGoogle Scholar
- 39.Yabe, H., Takano, M.: Global convergence properties of nonlinear conjugate gradient methods with modified secant condition. Comput. Optim. Appl. 28, 203–225 (2004)CrossRefMATHMathSciNetGoogle Scholar
- 40.Yu, G., Guan, L., Chen, W.: Spectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization. Optim. Methods Softw. 23(2), 275–293 (2008)CrossRefMATHMathSciNetGoogle Scholar
- 41.Yu, G.H.: Nonlinear self-scaling conjugate gradient methods for large-scale optimization problems. PhD thesis, Sun Yat-Sen University (2007)Google Scholar
- 42.Zhang, J.Z., Deng, N.Y., Chen, L.H.: New quasi-Newton equation and related methods for unconstrained optimization. J. Optim. Theory Appl. 102, 147–167 (1999)CrossRefMATHMathSciNetGoogle Scholar
- 43.Zhang, J.Z., Xu, C.X.: Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations. J. Comput. Appl. Math. 137, 269–278 (2001)CrossRefMATHMathSciNetGoogle Scholar
- 44.Zhang, L.: Two modified Dai-Yuan nonlinear conjugate gradient methods. Numer. Algorithms 50, 1–16 (2009)CrossRefMathSciNetGoogle Scholar
- 45.Zhang, L., Zhou, W.: Two descent hybrid conjugate gradient methods for optimization. J. Comput. Appl. Math. 216, 251–264 (2008)CrossRefMATHMathSciNetGoogle Scholar
- 46.Zhang, L., Zhou, W., Li, D.: A descent modified Polak-Ribière-Polyak conjugate gradient method and its global convergence. IMA J. Numer. Anal. 26(4), 629–640 (2006)CrossRefMATHMathSciNetGoogle Scholar
- 47.Zhang, L., Zhou, W., Li, D.: Global convergence of a modified Fletcher-Reeves conjugate gradient method with Armijo-type line search. Numer. Math. 104, 561–572 (2006)CrossRefMATHMathSciNetGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2014