By means of an example, the authors show that the sequence of estimates generated by the projection algorithm does not always converge. The authors' construction shows that convergence is not automatically among the properties that can be derived without additional assumptions on the input sequenc
Due to their extraordinary utility and broad applicability in many areas of classical mathematics an...
<p>Convergence of Backpropagation and Levenberg–Marquardt algorithms to the desired error (<i>EMIN</...
Non-smooth convex optimization problems occur in all fields of engineering. A common approach to sol...
By means of an example, the authors show that the sequence of estimates generated by the projection ...
AbstractWe provide a general theorem for the convergence of some projection methods with perturbatio...
AbstractThe validity of the subgradient projections algorithms (V. P. Sreedharan, J. Approx. Theory ...
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We rel...
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We rel...
In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA...
Countless theorems of analysis assert the convergence of sequences of numbers, functions, or element...
Abstract. We consider projection algorithms for solving (nonconvex) feasibility problems in Euclidea...
The main contributions of this paper are the proposition and the convergence analysis of a class of ...
We study the usage of regularity properties of collections of sets in convergence analysis of altern...
We introduce and study the convergence properties of a projection-type algorithm for solving the var...
Assuming that the absence of perturbations guarantees weak or strong convergence to a common fixed p...
Due to their extraordinary utility and broad applicability in many areas of classical mathematics an...
<p>Convergence of Backpropagation and Levenberg–Marquardt algorithms to the desired error (<i>EMIN</...
Non-smooth convex optimization problems occur in all fields of engineering. A common approach to sol...
By means of an example, the authors show that the sequence of estimates generated by the projection ...
AbstractWe provide a general theorem for the convergence of some projection methods with perturbatio...
AbstractThe validity of the subgradient projections algorithms (V. P. Sreedharan, J. Approx. Theory ...
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We rel...
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We rel...
In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA...
Countless theorems of analysis assert the convergence of sequences of numbers, functions, or element...
Abstract. We consider projection algorithms for solving (nonconvex) feasibility problems in Euclidea...
The main contributions of this paper are the proposition and the convergence analysis of a class of ...
We study the usage of regularity properties of collections of sets in convergence analysis of altern...
We introduce and study the convergence properties of a projection-type algorithm for solving the var...
Assuming that the absence of perturbations guarantees weak or strong convergence to a common fixed p...
Due to their extraordinary utility and broad applicability in many areas of classical mathematics an...
<p>Convergence of Backpropagation and Levenberg–Marquardt algorithms to the desired error (<i>EMIN</...
Non-smooth convex optimization problems occur in all fields of engineering. A common approach to sol...
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