Global convergence of nonmonotone descent methods for unconstrained optimization problems

Authors:
Wenyu Sun;Jiye Han;Jie Sun
Affiliations:
School of Mathematics and Computer Science, Nanjing Normal University, Nanjing 210097, People's Republic of China;Institute of Applied Mathematics, Chinese Academy of Science, Beijing 100080, People's Republic of China;Faculty of Business Administration and Singapore-MIT Alliance, National University of Singapore, Singapore
Venue:
Journal of Computational and Applied Mathematics - Special issue: Papers presented at the 1st Sino--Japan optimization meeting, 26-28 October 2000, Hong Kong, China
Year:
2002

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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.