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Reseach Article

Another Conjugate Gradient Algorithm based on Spectral-scaled Memoryless BFGS Update

by I. A. Osinuga, Y. N. Nwodo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 35
Year of Publication: 2020
Authors: I. A. Osinuga, Y. N. Nwodo
10.5120/ijca2020920440

I. A. Osinuga, Y. N. Nwodo . Another Conjugate Gradient Algorithm based on Spectral-scaled Memoryless BFGS Update. International Journal of Computer Applications. 176, 35 ( Jul 2020), 40-45. DOI=10.5120/ijca2020920440

@article{ 10.5120/ijca2020920440,
author = { I. A. Osinuga, Y. N. Nwodo },
title = { Another Conjugate Gradient Algorithm based on Spectral-scaled Memoryless BFGS Update },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 35 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number35/31429-2020920440/ },
doi = { 10.5120/ijca2020920440 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:16.851022+05:30
%A I. A. Osinuga
%A Y. N. Nwodo
%T Another Conjugate Gradient Algorithm based on Spectral-scaled Memoryless BFGS Update
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 35
%P 40-45
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study, we present another modification of a scaled three-term conjugate gradient (CG) algorithm. The proposed method incorporates the BFGS updating scheme of the inverse Hessian approximation within the frame of a memoryless quasi-Newton approach. In this case, the inverse Hessian approximation is restarted as a multiple of identity matrix with a spectral scaling parameter at every iteration. Under standard Wolfe line search, numerical results from an implementation of the proposed method indicate that the method is promising and competitive when subjected to comparison with other state-of-the art similar algorithms available in literature utilizing performance profiles of Dolan and More.

References
  1. Navon, M. and Legler, D. N. 1987 Conjugate gradient Methods for large-scale minimization in meteorology, descent property, Miskolc Mathematical Notes, 16 (1), 45 – 55.
  2. Bamigbola, O. M., Ali, M. M. and Oke, M. O. 2014Mathematical modeling of electric power flow and the minimization of power losses on transmission lines, Applied Mathematics and Computation, 241, 214 – 221.
  3. Moyi, A. U., Leong, W. J., Ibrahim, S. 2014 On the Application of Three - Term Conjugate Gradient Method in Regression Analysis, International Journal of Computer Applications, 102 (8), 1-4.
  4. Hestenes, M. R. and Stiefel, E. L. 1952 Method of conjugate gradients for solving linear systems,Journal of Research National Bureau Standards, 49, 409-436.
  5. Fletcher, R. and Reeves, C. 1964 Function minimization by conjugate gradients, Computer Journal, 7, 149-154.
  6. Polak, E. and Ribieré, G. 1969 Note sur la convergence de directions conjugeés, Rev. Francaise Informat Recherche Operationelle, 3e Année, 16: 35-43.
  7. Polyak, B. T. 1969 The conjugate gradient method in extreme problems, USSR Computer Mathematics and Mathematical Physics, 9, 94-112
  8. Liu, Y. and Storey, C. 1991 Efficient generalized conjugate gradient algorithms, Part 1: Theory Journal of Optimization Theory Application, 69, 129-137.
  9. Dai Y. H. and Yuan Y. 2001 An efficient hybrid conjugate gradient method for unconstrained optimization, Annals of Operations Research, 103, 33- 47.
  10. Fletcher, R., 1987. Practical Methods of Optimization vol. 1: Unconstrained Optimization, John Wiley & Sons, New York.
  11. Andrei N. 2007 Numerical comparison of conjugate algorithms in unconstrained optimization, Studies in Informatics and Control, 16, 333-352.
  12. Taqi, A. H. 2015 Improved three - term conjugate Algorithm for training neural network, Journal of Kufa for Mathematics and Computer, 2 (3), 93 – 100.
  13. Adeleke, O. J. and Osinuga, I. A. 2018 A five-term Hybrid conjugate gradient method with global convergence and descent properties for unconstrained optimization problems, Asian Journal of Scientific Research, 11, 185-194. DOI: 10.3923/ajsr.2018.
  14. Andrei N. 2010 Accelerated hybrid conjugate gradient algorithm with modified secant condition for unconstrained optimization. Numerical Algorithms, 54 (1), 23 – 46.
  15. Babaie – Kafaki S. and Ghanbari R. 2015 An extendedthree - term conjugate gradient method with sufficient descent property, Miskolc Mathematical Notes, 16 (1), 45 – 55.
  16. Gilbert E. G. and Nocedal J. 1992 Global convergence properties of conjugate gradient methods for Optimization, SIAM Journal of Optimization, 2, 21- 42.
  17. Beale, E. M. I. 1972 A derivative of conjugate gradients in numerical methods for nonlinear optimization, F.A. Lootsma, Ed., 39-43, Academic Press, London, UK.
  18. Ibrahim M. A. H., Mustafa M., Leong W. J. 2014 The hybrid BFGS - CG method in solving unconstrained optimization problems,. Abstract and Applied Analysis, Vol. 2014 Article ID 507102, 6 pp
  19. Buckley, A. G., 1978 A combined conjugate-gradient quasi-Newton minimization algorithm, Mathematical Programming, 15, 200 – 210.
  20. Ibrahim M. A. H., Mustafa M., Leong W. J., Azfi Z. S. 2014 The Algorithms of Broyden – CG for unconstrained optimization problems, International Journal of Mathematical Analysis, 8 (52), 2591 – 2600, http://dx.doi.org/10.12988/ijma.2014.49272
  21. i J. K.. and Li S. J. 2014 New hybrid conjugate Gradient Method for Unconstrained Optimization, Applied Mathematics Computation, 245, 36 – 43.
  22. Touati - Ahmed D., Storey C. 1990 Efficient hybrid conjugate gradient techniques, J. Optim. Theory Appl. 64 (2), 379 – 397.
  23. Andrei, N. 2008 An unconstrained optimization test functions collection. Advanced Modelling and Optimization, 10 (1), 147- 161.
  24. Bongart, I, Conn, A. R., Gould, N. I. M. and Toint, P. L. 1995, CUTE: constrained and unconstrained testing environments, ACM TOMS, 21, 123-160.
  25. Andrei, N. 2013 On three-term conjugate gradient algorithm for unconstrained optimization, Applied Mathematics and Computation, 219 (11), 6316 – 6327.
  26. Arzuka, I., Abubakar, M. R. and Leong, W. J. 2016 A scaled three-term conjugate gradient method for unconstrained optimization, Journal of inequalities and applications, 325, DOI.10-1186/s13660-016-1239-1
  27. Dolan, E., More, J. J. 2002 Benchmarking optimization software with performance profile, Mathematical Programming”, 91, 201 – 213.
  28. Hager, W. W. and Zhang, H. 2000 Asurvey of nonlinear conjugate gradient methods, Pacific Journal of Optimization, 2, 35 – 58.
  29. Zhang L., Zhou W. and Li D. 2007 Some descent conjugate gradient method and their global convergence, Optim. Methods Softw., 22 (4), 697 – 711.
  30. Wan Osman W. F. H., Ibrahim, M. A. H., Mamat M. 2017 Hybrid DFP-CG method for solving Unconstrained optimization problems”, Journal of Physics: Conference Series, 890, DOI:10.1088/1742-6596/890/1/012033.
  31. Xu X. and Kong F. 2016 New hybrid conjugate gradient methods with the generalized Wolfe line search”. Springerplus, 5:881.
  32. Zhang L., Zhou W. and Li D. 2006 Global convergence of a modified FR conjugate gradient method with Armidjo-type line search, Numer. Math., 2006, 104, 561 – 572.
  33. Zhang L., Zhou W. and Li D. 2006 A descent modified PRP conjugate gradient method and its global convergence, IMA J. Numer. Anal., 26, 629 – 649.
  34. Liu, J. K., Feng, Y. M. and Zou, L. M. 2019 A spectral conjugate gradient method for solving large-scale unconstrained optimization, Computers and Mathematics with Applications, 77, 731-739.
Index Terms

Computer Science
Information Sciences

Keywords

Unconstrained optimization conjugate gradient method spectral-scaled memoryless BFGS numerical comparisons