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Z.-J. Shi
A new class of memory gradient methods with inexact line searches
Keywords: unconstrained optimization, memory gradient method, inexact line search, convergence
The paper presents a new class of memory gradient methods with inexact line searches for unconstrained minimization problems. The methods use more previous iterative information than other methods to generate a search direction and use inexact line searches to select a step-size at each iteration. It is proved that the new methods have global convergence under weak mild conditions. The convergence rate of these methods is also investigated under some special cases. Some numerical experiments show that these new algorithms converge more stably than other line search methods and are effective in solving large scale unconstrained minimization problems.
Journal of Numerical Mathematics, Walter de Gruyter
Print ISSN: 1570-2820
Volume: 13, 04/2005
Pages: 53 - 71