In this paper we propose a method for the numerical solution of linear systems of equations in low rank tensor format. Such systems may arise from the discretisation of PDEs in high dimensions but our method is not limited to this type of application. We present an iterative scheme which is based on the projection of the residual to a low dimensional subspace. The subspace is spanned by vectors in low rank tensor format which — similarly to Krylov subspace methods — stem from the subsequent (approximate) application of the given matrix to the residual. All calculations are performed in hierarchical Tucker format which allows for applications in high dimensions. The mode size dependency is treated by a multilevel method. We present numerical...
Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in var...
In this paper, we propose a method for the approximation of the solution of high-dimension...
The coming century is surely the century of high dimensional data. With the rapid growth of computat...
The numerical simulation of high-dimensional partial differential equations (PDEs) is a challenging ...
We consider linear systems A(alpha)x(alpha) - b(alpha) depending on possibly many parameters alpha =...
Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear a...
International audienceWe propose an algorithm for preconditioning and solving high dimensional linea...
The numerical solution of partial differential equations on high-dimensional domains gives rise to c...
The numerical solution of partial differential equations on high-dimensional domains gives rise to c...
. In this paper, we describe tensor methods for large systems of nonlinear equations based on Krylov...
International audienceIn this paper, we propose a method for the approximation of the solution of hi...
In this paper we construct an approximation to the solution x of a linear system of equations Ax = b...
This thesis deals with tensor methods for the numerical solution of parametric partial differential ...
We propose a novel combination of the reduced basis method with low-rank tensor techniques for the e...
Abstract. In this paper, we describe tensor methods for large systems of nonlinear equa-tions based ...
Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in var...
In this paper, we propose a method for the approximation of the solution of high-dimension...
The coming century is surely the century of high dimensional data. With the rapid growth of computat...
The numerical simulation of high-dimensional partial differential equations (PDEs) is a challenging ...
We consider linear systems A(alpha)x(alpha) - b(alpha) depending on possibly many parameters alpha =...
Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear a...
International audienceWe propose an algorithm for preconditioning and solving high dimensional linea...
The numerical solution of partial differential equations on high-dimensional domains gives rise to c...
The numerical solution of partial differential equations on high-dimensional domains gives rise to c...
. In this paper, we describe tensor methods for large systems of nonlinear equations based on Krylov...
International audienceIn this paper, we propose a method for the approximation of the solution of hi...
In this paper we construct an approximation to the solution x of a linear system of equations Ax = b...
This thesis deals with tensor methods for the numerical solution of parametric partial differential ...
We propose a novel combination of the reduced basis method with low-rank tensor techniques for the e...
Abstract. In this paper, we describe tensor methods for large systems of nonlinear equa-tions based ...
Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in var...
In this paper, we propose a method for the approximation of the solution of high-dimension...
The coming century is surely the century of high dimensional data. With the rapid growth of computat...