Abstract
In this paper, a class of nonmonotone line search, study the con- vergence properties of such an algorithm for general nonconvex function, and proved its global convergence. More conjugate gradient algorithm is used the Wolfe rule nonmonotone line search. The global convergence results are proved.
Highlights
Our problem is to minimize a function of n variables min {f (x) : x ∈ Rn} (1.1)where f : Rn → R is smooth and its gradient g(x) is available
A class of non-monotone line search (NLS), study the convergence properties of such an algorithm for general nonconvex function, and proved its global convergence.This article studies the global convergence of a class of nonmonotone line search of Wolf rule conjugate gradient algorithm to prove that the idea comes from the on the F-rule search techniques
We study the condition of global convergence of four conjugate gradient methods with nonmonotone line searchs
Summary
Where f : Rn → R is smooth and its gradient g(x) is available. Conjugate gradient method for solving (1.1) are iterative methods of the form xk+1 = xk + αkdk (1.2). A class of non-monotone line search (NLS), study the convergence properties of such an algorithm for general nonconvex function, and proved its global convergence.This article studies the global convergence of a class of nonmonotone line search of Wolf rule conjugate gradient algorithm to prove that the idea comes from the on the F-rule search techniques. We study the condition of global convergence of four conjugate gradient methods with nonmonotone line searchs. In the convergence analysis and implementation of conjugate gradient method, the extended wolf rule nonmonotone line search, namely f We begin with two definitions the forcing function and the reverse modulus of continuity of gradient
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