Abstract In this paper, non-monotone line search procedure is studied, which is combined with the non-quasi-Newton family. Under the uniformly convexity assumption on objective function, the global and superlinear convergence of the non-quasi-Newton family with the proposed non-monotone line search is proved under suitable conditions
Global Convergence of a Class of Collinear Scaling Algorithms with Inexact Line Searches on Convex F...
A direct search quasi-Newton algorithm is presented for local minimization of Lipschitz continuous b...
In this paper, we propose a new non-monotone conjugate gradient method for solving unconstrained non...
AbstractIn this paper, we propose a new nonmonotone line search technique for unconstrained optimiza...
In this paper, on the basis of the DFP method a class of non-quasi-Newton methods is presented. Unde...
In this paper, we define an unconstrained optimization algorithm employing only first-order derivati...
We begin by developing a line search method for unconstrained optimization which can be regarded as ...
We begin by developing a line search method for unconstrained optimization which can be regarded as ...
AbstractQuasi-Newton method is a well-known effective method for solving optimization problems. Sinc...
In this paper we define globally convergent algorithms for the solution of large dimensional unconst...
An essential condition for quasi-Newton optimization methods to converge superlinearly is that a ful...
The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the He...
AbstractGlobal convergence results are established for unconstrained optimization algorithms that ut...
AbstractIn this paper, we propose a new nonmonotone line search technique for unconstrained optimiza...
AbstractThis paper concerns a nonmonotone line search technique and its application to the trust reg...
Global Convergence of a Class of Collinear Scaling Algorithms with Inexact Line Searches on Convex F...
A direct search quasi-Newton algorithm is presented for local minimization of Lipschitz continuous b...
In this paper, we propose a new non-monotone conjugate gradient method for solving unconstrained non...
AbstractIn this paper, we propose a new nonmonotone line search technique for unconstrained optimiza...
In this paper, on the basis of the DFP method a class of non-quasi-Newton methods is presented. Unde...
In this paper, we define an unconstrained optimization algorithm employing only first-order derivati...
We begin by developing a line search method for unconstrained optimization which can be regarded as ...
We begin by developing a line search method for unconstrained optimization which can be regarded as ...
AbstractQuasi-Newton method is a well-known effective method for solving optimization problems. Sinc...
In this paper we define globally convergent algorithms for the solution of large dimensional unconst...
An essential condition for quasi-Newton optimization methods to converge superlinearly is that a ful...
The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the He...
AbstractGlobal convergence results are established for unconstrained optimization algorithms that ut...
AbstractIn this paper, we propose a new nonmonotone line search technique for unconstrained optimiza...
AbstractThis paper concerns a nonmonotone line search technique and its application to the trust reg...
Global Convergence of a Class of Collinear Scaling Algorithms with Inexact Line Searches on Convex F...
A direct search quasi-Newton algorithm is presented for local minimization of Lipschitz continuous b...
In this paper, we propose a new non-monotone conjugate gradient method for solving unconstrained non...