Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0377-0427(02)00420-X
Title: Global convergence of nonmonotone descent methods for unconstrained optimization problems
Authors: Sun, W.
Han, J.
Sun, J. 
Keywords: Global convergence
Nonmonotone line search
Unconstrained optimization
Issue Date: 2002
Citation: Sun, W., Han, J., Sun, J. (2002). Global convergence of nonmonotone descent methods for unconstrained optimization problems. Journal of Computational and Applied Mathematics 146 (1) : 89-98. ScholarBank@NUS Repository. https://doi.org/10.1016/S0377-0427(02)00420-X
Abstract: Global convergence results are established for unconstrained optimization algorithms that utilize a nonmonotone line search procedure. This procedure allows the user to specify a flexible forcing function and includes the nonmonotone Armijo rule, the nonmonotone Goldstein rule, and the nonmonotone Wolfe rule as special cases. © 2002 Elsevier Science B.V. All rights reserved.
Source Title: Journal of Computational and Applied Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/43973
ISSN: 03770427
DOI: 10.1016/S0377-0427(02)00420-X
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