Abstract
A new type of stepsize, which was recently introduced by Liu et al. (Optimization 67(3):427–440, 2018), is called approximately optimal stepsize and is very efficient for gradient method. Interestingly, all gradient methods can be regarded as gradient methods with approximately optimal stepsizes. In this paper, we present an efficient gradient method with approximately optimal stepsize based on tensor model for unconstrained optimization. In the proposed method, if the objective function is not close to a minimizer and a quadratic function on a line segment between the current and latest iterates, then a tensor model is exploited to generate approximately optimal stepsize for gradient method. Otherwise, quadratic approximation models are constructed to generate approximately optimal stepsizes for gradient method. The global convergence of the proposed method is established under weak conditions. Numerical results indicate that the proposed method is very promising.










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Cauchy, A.: Méthode générale pour la résolution des systéms déquations simultanées. Comp. Rend. Sci. Paris 25, 46–89 (1847)
Barzilai, J., Borwein, J.M.: Two-point step size gradient methods. IMA J. Numer. Anal. 8, 141–148 (1988)
Asmundis, R.D., Serafino, D.D., Riccio, F., et al.: On spectral properties of steepest descent methods. IMA J. Numer. Anal. 33(4), 1416–1435 (2013)
Raydan, M.: On the Barzilai and Borwein choice of steplength for the gradient method. IMA J. Numer. Anal. 13, 321–326 (1993)
Dai, Y.H., Liao, L.Z.: \( R \)-linear convergence of the Barzilai and Borwein gradient method. IMA J. Numer. Anal. 22(1), 1–10 (2002)
Raydan, M.: The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem. SIAM J. Optim. 7, 26–33 (1997)
Grippo, L., Lampariello, F., Lucidi, S.: A nonmonotone line search technique for Newton’s method. SIAM J. Numer. Anal. 23, 707–716 (1986)
Biglari, F., Solimanpur, M.: Scaling on the spectral gradient method. J. Optim. Theory Appl. 158(2), 626–635 (2013)
Dai, Y.H., Yuan, J.Y., Yuan, Y.X.: Modified two-point stepsize gradient methods for unconstrained optimization problems. Comput. Optim. Appl. 22, 103–109 (2002)
Dai, Y.H., Hager, W.W., Schittkowski, K., et al.: The cyclic Barzilai–Borwein method for unconstrained optimization. IMA J. Numer. Anal. 26(3), 604–627 (2006)
Xiao, Y.H., Wang, Q.Y., Wang, D., et al.: Notes on the Dai–Yuan–Yuan modified spectral gradient method. J. Comput. Appl. Math. 234(10), 2986–2992 (2010)
Nosratipour, H., Fard, O.S., Borzabadi, A.H.: An adaptive nonmonotone global Barzilai–Borwein gradient method for unconstrained optimization. Optimization 66(4), 641–655 (2017)
Miladinović, M., Stanimirović, P., Miljković, S.: Scalar correction method for solving large scale unconstrained minimization problems. J. Optim. Theory Appl. 151(2), 304–320 (2011)
Liu, Z.X., Liu, H.W., Dong, X.L.: An efficient gradient method with approximate optimal stepsize for the strictly convex quadratic minimization problem. Optimization 67(3), 427–440 (2018)
Liu, Z.X., Liu, H.W.: An efficient gradient method with approximate optimal stepsize for large-scale unconstrained optimization. Numer. Algorithms 78(1), 21–39 (2018)
Liu, Z.X., Liu, H.W.: Several efficient gradient methods with approximate optimal stepsizes for large scale unconstrained optimization. J. Comput. Appl. Math. 328, 400–413 (2018)
Liu, H.W., Liu, Z.X., Dong, X.L.: A new adaptive Barzilai and Borwein method for unconstrained optimization. Optim. Lett. 12(4), 845–873 (2018)
Dai, Y.H., Kou, C.X.: A Barzilai–Borwein conjugate gradient method. Sci. China Math. 59(8), 1511–1524 (2016)
Yuan, G.L., Meng, Z.H., Li, Y.: A modified Hestenes and Stiefel conjugate gradient algorithm for large-scale nonsmooth minimizations and nonlinear equations. J. Optim. Theory Appl. 168, 129–152 (2016)
Liu, Z.X., Liu, H.W.: An efficient Barzilai–Borwein conjugate gradient method for unconstrained optimization. J. Optim. Theory Appl. https://doi.org/10.1007/s10957-018-1393-3 (2018)
Yuan, G.L., Wei, Z.X., Zhao, Q.M.: A modified Polak–Ribière–Polyak conjugate gradient algorithm for large-scale optimization problems. IIE Trans. 46, 397–413 (2014)
Yuan, G.L., Wei, Z.X., Lu, X.W.: Global convergence of BFGS and PRP methods under a modified weak Wolfe–Powell line search. Appl. Math. Model. 47, 811–825 (2017)
Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007)
Wright, S.J., Nowak, R.D., Figueiredo, M.A.T.: Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57(7), 3373–3376 (2009)
Huang, Y.K., Liu, H.W.: Smoothing projected Barzilai–Borwein method for constrained non-Lipschitz optimization. Comput. Optim. Appl. 63(3), 671–698 (2016)
Liu, H.W., Li, X.L.: Modified subspace Barzilai–Borwein gradient method for non-negative matrix factorization. Comput. Optim. Appl. 55(1), 173–196 (2013)
Huang, Y.K., Liu, H.W., Zhou, S.: An efficient monotone projected Barzilai–Borwein method for nonnegative matrix factorization. Appl. Math. Lett. 45, 12–17 (2015)
Yuan, Y.X.: A modified BFGS algorithm for unconstrained optimization. IMA J. Numer. Anal. 11(3), 325–332 (1991)
Schnabel, R.B., Chow, T.: Tensor methods for unconstrained optimization using second derivatives. SIAM J. Optim. 1(3), 293–315 (1991)
Chow, T., Eskow, E., Schnabel, R.: Algorithm 738: a software package for unconstrained optimization using tensor methods. ACM Trans. Math. Softw. 20(4), 518–530 (1994)
Bouaricha, A.: Tensor methods for large, sparse unconstrained optimization. SIAM J. Optim. 7(3), 732–756 (1997)
Yuan, Y.X., Sun, W.Y.: Theory and Methods of Optimization. Science Press of China, Beijing (1999)
Li, D.H., Fukushima, M.: A modified BFGS method and its global convergence in nonconvex minimization. J. Comput. Appl. Math. 129(1), 15–35 (2001)
Toint, P.L.: An assessment of nonmonotone linesearch techniques for unconstrained optimization. SIAM J. Sci. Comput. 17(3), 725–739 (1996)
Zhang, H.C., Hager, W.W.: A nonmonotone line search technique and its application to unconstrained optimization. SIAM J. Optim. 14, 1043–1056 (2004)
Birgin, E.G., Martínez, J.M., Raydan, M.: Nonmonotone spectral projected gradient methods for convex sets. SIAM J. Optim. 10(4), 1196–1211 (2000)
Hager, W.W., Zhang, H.C.: A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J. Optim. 16(1), 170–192 (2005)
Andrei, N.: Open problems in nonlinear conjugate gradient algorithms for unconstrained optimization. Bull. Malays. Math. Sci. Soc. 34(2), 319–330 (2011)
Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)
Andrei, N.: An unconstrained optimization test functions collection. Adv. Model. Optim. 10, 147–161 (2008)
Gould, N.I.M., Orban, D., Toint, P.L.: CUTEr and SifDec: a constrained and unconstrained testing environment, revisited. ACM Trans. Math. Softw. 29(4), 373–394 (2003)
Hager, W.W., Zhang, H.C.: Algorithm 851:CG\_DESCENT, a conjugate gradient method with guaranteed descent. ACM Trans. Math. Softw. 32(1), 113–137 (2006)
Yuan, G.L., Zhou, S., Wang, B.P., et al.: The global convergence of a modified BFGS method for nonconvex functions. J. Comput. Appl. Math. 327, 274–294 (2018)
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)
Yuan, G.L., Wei, Z.X.: Convergence analysis of a modified BFGS method on convex minimizations. Comput. Optim. Appl. 47, 237–255 (2010)
Acknowledgements
We would like to thank Professors Hager and Zhang, H. C. for their C code of CG_DESCENT, and thank Professor Dai, Y. H. for his help in the numerical experiments. This research is supported by National Science Foundation of China (No.11461021), Shangxi Science Foundation (No. 2017JM1014), Guangxi Science Foundation (Nos. 2018GXNSFBA281180, 2017GXNSFBA198031), Project of Guangxi Education Department Grant (2017KY0648), Scientific Research Project of Hezhou University (Nos. 2014YBZK06, 2016HZXYSX03).
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Guoyin Li.
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Liu, Z., Liu, H. An Efficient Gradient Method with Approximately Optimal Stepsize Based on Tensor Model for Unconstrained Optimization. J Optim Theory Appl 181, 608–633 (2019). https://doi.org/10.1007/s10957-019-01475-1
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DOI: https://doi.org/10.1007/s10957-019-01475-1