AbstractIn this paper, continuous methods are introduced to compute both the extreme and interior eigenvalues and their corresponding eigenvectors for real symmetric matrices. The main idea is to convert the extreme and interior eigenvalue problems into some optimization problems. Then a continuous method which includes both a merit function and an ordinary differential equation (ODE) is introduced for each resulting optimization problem. The convergence of each ODE solution is proved for any starting point. The limit of each ODE solution for any starting point is fully studied. Both the extreme and the interior eigenvalues and their corresponding eigenvectors can be easily obtained under a very mild condition. Promising numerical results a...
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
AbstractGeneralized eigenvalue problems play a significant role in many applications. In this paper,...
AbstractRecently, a continuous method has been proposed by Golub and Liao as an alternative way to s...
AbstractRecently, a continuous method has been proposed by Golub and Liao as an alternative way to s...
The computation of eigenvalues of a matrix is still of importance from both theoretical and practica...
AbstractOptimization involving eigenvalues arise in many engineering problems. We propose a new algo...
In many applications, it is desired to obtain extreme eigenvalues and eigenvectors of large Hermitia...
In many applications, it is desired to obtain extreme eigenvalues and eigenvectors of large Hermitia...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
In this work, we interpret real symmetric eigenvalue problems in an unconstrained global optimizatio...
AbstractOptimization problems involving eigenvalues arise in many engineering problems. In this pape...
In this work, we interpret real symmetric eigenvalue problems in an unconstrained global optimizatio...
AbstractFor a self-adjoint linear operator with a discrete spectrum or a Hermitian matrix, the “extr...
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
AbstractGeneralized eigenvalue problems play a significant role in many applications. In this paper,...
AbstractRecently, a continuous method has been proposed by Golub and Liao as an alternative way to s...
AbstractRecently, a continuous method has been proposed by Golub and Liao as an alternative way to s...
The computation of eigenvalues of a matrix is still of importance from both theoretical and practica...
AbstractOptimization involving eigenvalues arise in many engineering problems. We propose a new algo...
In many applications, it is desired to obtain extreme eigenvalues and eigenvectors of large Hermitia...
In many applications, it is desired to obtain extreme eigenvalues and eigenvectors of large Hermitia...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
In this work, we interpret real symmetric eigenvalue problems in an unconstrained global optimizatio...
AbstractOptimization problems involving eigenvalues arise in many engineering problems. In this pape...
In this work, we interpret real symmetric eigenvalue problems in an unconstrained global optimizatio...
AbstractFor a self-adjoint linear operator with a discrete spectrum or a Hermitian matrix, the “extr...
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...