[[abstract]]We propose a minimax scaling procedure for second order polynomial matrices that aims to minimize the backward errors incurred in solving a particular linearized generalized eigenvalue problem. We give numerical examples to illustrate that it can significantly improve the backward errors of the computed eigenvalue-eigenvector pairs.[[fileno]]2010223010019[[department]]數學
Abstract. The standard way of solving the polynomial eigenvalue problem of degree m in n×n matrices ...
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
The standard way to solve polynomial eigenvalue problems is through linearizations. The family of F...
We propose a minimax scaling procedure for second order polynomial matrices that aims to minimize th...
Abstract. Scaling is a commonly used technique for standard eigenvalue problems to improve the sensi...
Abstract. Scaling is a commonly used technique for standard eigenvalue problems to improve the sensi...
One of the most frequently used techniques to solve polynomial eigenvalue problems is linearization,...
The most widely used approach for solving the polynomial eigenvalue problem $P(\lambda)x = \bigl(\su...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
We perform a structured backward error analysis of polynomial eigenvalue problems solved via lineari...
Large Solving polynomial eigenvalue problems by a scaled block companion linearization Marc Van Bare...
Backward error analyses of algorithms for solving polynomial eigenproblems can be "local" or "global...
We perform a backward error analysis of polynomial eigenvalue problems solved via linearization. Thr...
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
Abstract. The standard way of solving the polynomial eigenvalue problem of degree m in n×n matrices ...
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
The standard way to solve polynomial eigenvalue problems is through linearizations. The family of F...
We propose a minimax scaling procedure for second order polynomial matrices that aims to minimize th...
Abstract. Scaling is a commonly used technique for standard eigenvalue problems to improve the sensi...
Abstract. Scaling is a commonly used technique for standard eigenvalue problems to improve the sensi...
One of the most frequently used techniques to solve polynomial eigenvalue problems is linearization,...
The most widely used approach for solving the polynomial eigenvalue problem $P(\lambda)x = \bigl(\su...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
We perform a structured backward error analysis of polynomial eigenvalue problems solved via lineari...
Large Solving polynomial eigenvalue problems by a scaled block companion linearization Marc Van Bare...
Backward error analyses of algorithms for solving polynomial eigenproblems can be "local" or "global...
We perform a backward error analysis of polynomial eigenvalue problems solved via linearization. Thr...
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
Abstract. The standard way of solving the polynomial eigenvalue problem of degree m in n×n matrices ...
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
The standard way to solve polynomial eigenvalue problems is through linearizations. The family of F...