AbstractThe aim of this paper is to reduce the eigenvalue problem of a diagonalizable matrix to the eigenvalue problem of an equivalent normal matrix. We use for this purpose a minimization strategy, which is also applicable for transforming an arbitrary nondiagonalizable matrix to an almost normal one
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
0.80SIGLELD:7074.135(E.E/CON--80.5). / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Computing all eigenvalues of a modest size matrix typically proceeds in two phases. In a first phase...
AbstractThe aim of this paper is to reduce the eigenvalue problem of a diagonalizable matrix to the ...
AbstractA new norm decreasing Jacobi-like method for reducing a non-normal matrix to a normal one is...
AbstractIn this paper, the constrained inverse eigenvalue problem and associated approximation probl...
AbstractA new norm decreasing Jacobi-like method for reducing a non-normal matrix to a normal one is...
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...
Solving the quadratic eigenvalue problem is critical in several applications in control and systems ...
An n-by-n matrix is called almost normal if the maximal cardinality of a set of orthogonal eigenvect...
AbstractWe first present a constructive matrix procedure to assign an arbitrary nonderogatory matrix...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
In mathematics, matrices have many uses, they are finding solutions of a linear equation system, loo...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
0.80SIGLELD:7074.135(E.E/CON--80.5). / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Computing all eigenvalues of a modest size matrix typically proceeds in two phases. In a first phase...
AbstractThe aim of this paper is to reduce the eigenvalue problem of a diagonalizable matrix to the ...
AbstractA new norm decreasing Jacobi-like method for reducing a non-normal matrix to a normal one is...
AbstractIn this paper, the constrained inverse eigenvalue problem and associated approximation probl...
AbstractA new norm decreasing Jacobi-like method for reducing a non-normal matrix to a normal one is...
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...
Solving the quadratic eigenvalue problem is critical in several applications in control and systems ...
An n-by-n matrix is called almost normal if the maximal cardinality of a set of orthogonal eigenvect...
AbstractWe first present a constructive matrix procedure to assign an arbitrary nonderogatory matrix...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
In mathematics, matrices have many uses, they are finding solutions of a linear equation system, loo...
Scaling is a commonly used technique for standard eigenvalue problems to improve the sensitivity of ...
It has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimi...
0.80SIGLELD:7074.135(E.E/CON--80.5). / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Computing all eigenvalues of a modest size matrix typically proceeds in two phases. In a first phase...
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