Abstract
The subject of this study is the analysis of the spectral secant equation for the well-known Davidon–Fletcher–Powell (DFP) quasi-Newton updating formula. More precisely, we plan to make the memoryless DFP formula well conditioned in several aspects. We first focus our efforts on obtaining eigenvalues of the DFP formula and directly analyzing its spectral condition number. Then, we proceed in a different direction by using the Byrd–Nocedal measure function as well. Lastly, we propose a hybrid adaptive formula for the spectral parameter of the method.
Data Availability
Data sharing is not applicable to this manuscript as no new data were created or analyzed in this study.
References
Aminifard, Z., Babaie-Kafaki, S.: Analysis of the maximum magnification by the scaled memoryless DFP updating formula with application to compressive sensing. Mediterr. J. Math. 18(6), 255 (2021). https://doi.org/10.1007/s00009-021-01905-3
Aminifard, Z., Babaie-Kafaki, S., Ghafoori, S.: An augmented memoryless BFGS method based on a modified secant equation with application to compressed sensing. Appl. Numer. Math. 67, 187–201 (2021). https://doi.org/10.1016/j.apnum.2021.05.002
Arazm, M.R., Babaie-Kafaki, S., Ghanbari, R.: An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions. Glas. Mat. 52, 361–375 (2017). https://doi.org/10.3336/gm.52.2.12
Babaie-Kafaki, S.: On optimality of the parameters of self-scaling memoryless quasi-Newton updating formulae. J. Optim. Theory Appl. 167, 91–101 (2015). https://doi.org/10.1007/s10957-015-0724-x
Babaie-Kafaki, S., Ghanbari, R.: A linear hybridization of the Hestenes–Stiefel method and the memoryless BFGS technique. Mediterr. J. Math. 15, 1–10 (2018). https://doi.org/10.1007/s00009-018-1132-x
Barzilai, J., Borwein, J.M.: Two-point stepsize gradient methods. IMA J. Numer. Anal. 8(1), 141–148 (1988). https://doi.org/10.1093/imanum/8.1.141
Byrd, R.H., Nocedal, J.: A tool for the analysis of quasi-Newton methods with application to unconstrained minimization. SIAM J. Numer. Anal. 26(3), 727–739 (1989). https://doi.org/10.1137/0726042
Cheng, W.Y., Li, D.H.: Spectral scaling BFGS method. J. Optim. Theory Appl. 146(2), 305–319 (2010). https://doi.org/10.1007/s10957-010-9652-y
Eldén, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia (2007)
Ford, J.A., Moghrabi, I.A.: Multi-step quasi-Newton methods for optimization. J. Comput. Appl. Math. 50(1–3), 305–323 (1994). https://doi.org/10.1016/0377-0427(94)90309-3
Ford, J.A., Tharmlikit, S.: Three-step fixed-point quasi-Newton methods for unconstrained optimisation. Comput. Math. Appl. 50(7), 1051–1060 (2005). https://doi.org/10.1016/j.camwa.2005.08.007
Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large-scale optimization. Math. Program. 45(1–3), 503–528 (1989). https://doi.org/10.1007/BF01589116
Liu, H., Yao, Y., Qian, X., Wang, H.: Some nonlinear conjugate gradient methods based on spectral scaling secant equations. J. Comput. Appl. Math. 35(2), 639–651 (2016). https://doi.org/10.1007/s40314-014-0212-1
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999). https://doi.org/10.1007/978-0-387-40065-5
Oren, S.S., Luenberger, D.G.: Self-scaling variable metric (SSVM) algorithms. I. Criteria and sufficient conditions for scaling a class of algorithms. Manag. Sci. 20(5), 845–862 (1973). https://doi.org/10.1287/mnsc.20.5.845
Oren, S.S., Spedicato, E.: Optimal conditioning of self-scaling variable metric algorithms. Math. Program. 10(1), 70–90 (1976). https://doi.org/10.1007/BF01580654
Powell, M.J.D.: Some global convergence properties of a variable metric algorithm for minimization without exact line searches. In: Cottle, R.W., Lemke, C.E. (eds.) Nonlinear Programming. SIAM–AMS Proceedings, vol. 4, pp. 53–72. American Mathematical Society, Providence (1976)
Pu, D.: Convergence of the DFP algorithm without exact line search. J. Optim. Theory Appl. 112, 187–211 (2002). https://doi.org/10.1023/A:1013004914923
Pu, D., Tian, W.: A class of DFP algorithms with revised search directions. Numer. Funct. Anal. Optim. 23(3–4), 383–400 (2002). https://doi.org/10.1081/NFA-120006700
Pu, D., Tian, W.: The revised DFP algorithm without exact line search. J. Comput. Appl. Math. 154(2), 319–339 (2003). https://doi.org/10.1016/S0377-0427(02)00856-7
Shi, Z.J.: Convergence of quasi-Newton method with new inexact line search. J. Math. Anal. Appl. 315(1), 120–131 (2006). https://doi.org/10.1016/j.jmaa.2005.05.077
Sun, W., Yuan, Y.X.: Optimization Theory and Methods: Nonlinear Programming. Springer, New York (2006). https://doi.org/10.1007/b106451
Watkins, D.S.: Fundamentals of Matrix Computations. Wiley, New York (2002)
Xu, C., Zhang, J.: A survey of quasi-Newton equations and quasi-Newton methods for optimization. Ann. Oper. Res. 103(1–4), 213–234 (2001). https://doi.org/10.1023/A:1012959223138
Yabe, H., Ogasawara, H., Yoshino, M.: Local and superlinear convergence of quasi-Newton methods based on modified secant conditions. J. Comput. Appl. Math. 205(1), 617–632 (2007). https://doi.org/10.1016/j.cam.2006.05.018
Yao, X., Wang, Z.: Broad echo state network for multivariate time series prediction. J. Franklin Inst. 356(9), 4888–4906 (2019). https://doi.org/10.1016/j.jfranklin.2019.01.027
Yin, F., Wang, Y.N., Wei, S.N.: Inverse kinematic solution for robot manipulator based on electromagnetism-like and modified DFP algorithms. Acta Mech. Solida Sin. 37(1), 74–82 (2011). https://doi.org/10.1016/S1874-1029(11)60199-7
Zhang, H., Wang, K., Zhou, X., Wang, W.: Using DFP algorithm for nodal demand estimation of water distribution networks. KSCE J. Civ. Eng. 22, 2747–2754 (2018). https://doi.org/10.1007/s12205-018-0176-6
Zhang, J.Z., Xue, Y., Zhang, K.: A structured secant method based on a new quasi-Newton equation for nonlinear least squares problems. BIT Numer. Math. 43(1), 217–229 (2003). https://doi.org/10.1023/A:1023665409152
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Dargahi, F., Babaie-Kafaki, S. & Aminifard, Z. Eigenvalue Analyses on the Memoryless Davidon–Fletcher–Powell Method Based on a Spectral Secant Equation. J Optim Theory Appl 200, 394–403 (2024). https://doi.org/10.1007/s10957-023-02354-6
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DOI: https://doi.org/10.1007/s10957-023-02354-6