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An iterative algorithm baaed on probabilistic estimation is described for obtaining the minimum-norm solution of a very large, consistent, linear system of equations AX = g where A is an (m times n) matrix with non-negative elements, x and g are respectively (n times 1) and (m times 1) vectors with positive components
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
This work will present metaheuristic computations, namely, probabilistic artificial neural network, ...
We discuss the positive definite solutions for the system of nonlinear matrix equations and , where...
An iterative algorithm baaed on probabilistic estimation is described for obtaining the minimum-norm...
AbstractWe present a unifying framework for a wide class of iterative methods in numerical linear al...
AbstractWe consider iterative methods for the minimal nonnegative solution of the matrix equation G ...
We present a numerical algorithm for finding real non-negative solutions to a certain class of polyn...
We introduce a general iterative scheme for image reconstruction based on Landweber's method. I...
Several recent works have developed a new, probabilistic interpretation for numerical algorithms sol...
In this paper the gradient based iterative algorithms are presented to solve the following four type...
Artificial neural network and genetic algorithm have been extensively used in solving many real-worl...
AbstractNew iterative algorithms, QMART1 and QMART2, are proposed to solve large systems of linear e...
This paper presents a probabilistic perspective on iterative methods for approximating the solution ...
In this paper the gradient based iterative algorithm is presented to solve the linear matrix equatio...
Abstract. This manuscript proposes a probabilistic framework for algorithms that iteratively solve u...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
This work will present metaheuristic computations, namely, probabilistic artificial neural network, ...
We discuss the positive definite solutions for the system of nonlinear matrix equations and , where...
An iterative algorithm baaed on probabilistic estimation is described for obtaining the minimum-norm...
AbstractWe present a unifying framework for a wide class of iterative methods in numerical linear al...
AbstractWe consider iterative methods for the minimal nonnegative solution of the matrix equation G ...
We present a numerical algorithm for finding real non-negative solutions to a certain class of polyn...
We introduce a general iterative scheme for image reconstruction based on Landweber's method. I...
Several recent works have developed a new, probabilistic interpretation for numerical algorithms sol...
In this paper the gradient based iterative algorithms are presented to solve the following four type...
Artificial neural network and genetic algorithm have been extensively used in solving many real-worl...
AbstractNew iterative algorithms, QMART1 and QMART2, are proposed to solve large systems of linear e...
This paper presents a probabilistic perspective on iterative methods for approximating the solution ...
In this paper the gradient based iterative algorithm is presented to solve the linear matrix equatio...
Abstract. This manuscript proposes a probabilistic framework for algorithms that iteratively solve u...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
This work will present metaheuristic computations, namely, probabilistic artificial neural network, ...
We discuss the positive definite solutions for the system of nonlinear matrix equations and , where...
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