Title :
A Non-monotone Memory Gradient Method for Unconstrained Optimization
Author :
Gui, Shenghua ; Wang, Hua
Author_Institution :
Dept. of Math., Shanghai Second Polytech. Univ., Shanghai, China
Abstract :
The memory gradient method is used for unconstrained optimization, especially large scale problems. In this paper, we develop a nonmonotone memory gradient method for unconstrained optimization, where a class of memory gradient direction is combined efficiently. The global and Rlinear convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.
Keywords :
approximation theory; convergence; gradient methods; search problems; R-linear convergence; large scale problems; memorygradient direction; nonmonotone line search strategy; nonmonotone memory gradient method; numerical tests; unconstrained optimization; Convergence; Educational institutions; Gradient methods; Iterative methods; Search problems; R-linear convergence; memory gradient method; nonmonotone line search; unconstrained optimization;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4673-1365-0
DOI :
10.1109/CSO.2012.92
