A new hybrid conjugate gradient algorithm based on the Newton direction to solve unconstrained optimization problems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s12190-022-01821-z.pdf
Reference34 articles.
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2. Wolfe, P.: Convergence conditions for ascent methods. SIAM Rev. 11(2), 226–235 (1969). https://doi.org/10.1137/1011036
3. Wolfe, P.: Convergence conditions for ascent methods. II: some corrections. SIAM Rev. 13(2), 185–188 (1971). https://doi.org/10.1137/1013035
4. Narayanan, S., Kaelo, P.: A linear hybridization of Dai–Yuan and Hestenes–Stiefel conjugate gradient method for unconstrained optimization. Numer. Math. Theor. Meth. Appl. 14, 527–539 (2021). https://doi.org/10.4208/nmtma.OA-2020-0056
5. Hestenes, M.R., Stiefel, E.L.: Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49(6), 409–436 (1952). https://doi.org/10.6028/jres.049.044
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1. A sufficient descent hybrid conjugate gradient method without line search consideration and application;Engineering Computations;2024-06-18
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