This paper describes a modular software package for solving systems of nonlinear equations and nonlinear least squares problems, using a new class of methods called tensor methods. It is intended for small to medium--sized problems, say with up to 100 equations and unknowns, in cases where it is reasonable to calculate the Jacobian matrix or approximate it by finite differences at each iteration. The software allows the user to select between a tensor method and a standard method based upon a linear model. The tensor method models F (x) by a quadratic model, where the second--order term is chosen so that the model is hardly more expensive to form, store, or solve than the standard linear model. Moreover, the software provides two different ...
Problem statement: The major weaknesses of Newton method for nonlinear equations entail computation ...
This paper investigates the performance of tensor methods for solving small- and large-scale systems...
We propose a novel combination of the reduced basis method with low-rank tensor techniques for the e...
This paper describes a modular software package for solving systems of nonlinear equa-tions and nonl...
. This paper introduces tensor methods for solving large sparse systems of nonlinear equations. Tens...
We describe a new package for minimizing an unconstrained nonlinear function where the Hessian is la...
We describe the design and computational performance of parallel row-oriented tensor algorithms for ...
We describe a new package for minimizing an unconstrained nonlinear function where the Hessian is la...
We describe a new package for minimizing an unconstrained nonlinear function, where the Hessian is l...
Abstract It has turned out that the tensor expansion model has better approximation to the objective...
. In this paper, we describe tensor methods for large systems of nonlinear equations based on Krylov...
This report documents research to develop robust and efficient solution techniques for solving large...
Abstract. In this paper, we describe tensor methods for large systems of nonlinear equa-tions based ...
This paper develops and investigates iterative tensor methods for solving large-scale systems of non...
In this paper, we describe tensor methods for large sparse systems of nonlinear equations based on K...
Problem statement: The major weaknesses of Newton method for nonlinear equations entail computation ...
This paper investigates the performance of tensor methods for solving small- and large-scale systems...
We propose a novel combination of the reduced basis method with low-rank tensor techniques for the e...
This paper describes a modular software package for solving systems of nonlinear equa-tions and nonl...
. This paper introduces tensor methods for solving large sparse systems of nonlinear equations. Tens...
We describe a new package for minimizing an unconstrained nonlinear function where the Hessian is la...
We describe the design and computational performance of parallel row-oriented tensor algorithms for ...
We describe a new package for minimizing an unconstrained nonlinear function where the Hessian is la...
We describe a new package for minimizing an unconstrained nonlinear function, where the Hessian is l...
Abstract It has turned out that the tensor expansion model has better approximation to the objective...
. In this paper, we describe tensor methods for large systems of nonlinear equations based on Krylov...
This report documents research to develop robust and efficient solution techniques for solving large...
Abstract. In this paper, we describe tensor methods for large systems of nonlinear equa-tions based ...
This paper develops and investigates iterative tensor methods for solving large-scale systems of non...
In this paper, we describe tensor methods for large sparse systems of nonlinear equations based on K...
Problem statement: The major weaknesses of Newton method for nonlinear equations entail computation ...
This paper investigates the performance of tensor methods for solving small- and large-scale systems...
We propose a novel combination of the reduced basis method with low-rank tensor techniques for the e...