Solving many problems in mechanics, engineering, medicine and other (e.g., diffusion tensor magnetic resonance imaging or finite element modeling) requires the efficient solving of algebraic equations. In many cases, such systems are very complex with a large number of linear equations, which are symmetric positive-defined (SPD). This paper is focused on improving the computational efficiency of the solvers dedicated for the linear systems based on incomplete and noisy SPD matrices by using preconditioning technique – Incomplete Cholesky Factorization, and modern set of processor instructions – Advanced Vector Extension. Application of these techniques allows to fairly reduce the computational time, number of iterations of conventional algo...
Applying a finite difference approximation to a biharmonic equation results in a very ill-conditione...
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
This paper deals with background and practical experience with preconditioned gradient methods for s...
Solving many problems in mechanics, engineering, medicine and other (e.g., diffusion tensor magnetic...
Abstract: In the paper we consider the iterative solution of linear systemby the conjugate...
A frequently used iterative algorithm for solving large, sparse, symmetric and positiv definite syst...
In this note we examine the performance of a few iterative methods to solve linear systems of equati...
This paper includes a solver for a large sparse set of linear algebraic equations which are obtained...
This report presents preconditioning techniques for the conjugate gradient method (CG), an iterative...
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner fo...
Iterative methods for solving large-scale linear systems have been gaining popularity in many areas ...
This report presents preconditioning techniques for the conjugate gradient method (CG), an iterative...
The implementation of accelerated conjugated gradients for the solution of large sparse systems of l...
In this article the preconditioned conjugate gradient (PCG) method, realized on GPU and intended to ...
AbstractIn this paper we show how an algebraically reduced system can be constructed, for which the ...
Applying a finite difference approximation to a biharmonic equation results in a very ill-conditione...
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
This paper deals with background and practical experience with preconditioned gradient methods for s...
Solving many problems in mechanics, engineering, medicine and other (e.g., diffusion tensor magnetic...
Abstract: In the paper we consider the iterative solution of linear systemby the conjugate...
A frequently used iterative algorithm for solving large, sparse, symmetric and positiv definite syst...
In this note we examine the performance of a few iterative methods to solve linear systems of equati...
This paper includes a solver for a large sparse set of linear algebraic equations which are obtained...
This report presents preconditioning techniques for the conjugate gradient method (CG), an iterative...
In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner fo...
Iterative methods for solving large-scale linear systems have been gaining popularity in many areas ...
This report presents preconditioning techniques for the conjugate gradient method (CG), an iterative...
The implementation of accelerated conjugated gradients for the solution of large sparse systems of l...
In this article the preconditioned conjugate gradient (PCG) method, realized on GPU and intended to ...
AbstractIn this paper we show how an algebraically reduced system can be constructed, for which the ...
Applying a finite difference approximation to a biharmonic equation results in a very ill-conditione...
The computational aspects of the simplex algorithm are investigated, and high performance computing ...
This paper deals with background and practical experience with preconditioned gradient methods for s...