It is a well-known fact that while reducing a symmetric matrix into a similar tridiagonal one, the already tridiagonal matrix in the partially reduced matrix has as eigenvalues the Lanczos-Ritz values. This behavior is also shared by the reduction algorithm which transforms symmetric matrices via orthogonal similarity transformations to semiseparable form. Moreover also the orthogonal reduction to Hessenberg form has a similar behavior with respect to the Arnoldi-Ritz values. In this paper we investigate the orthogonal similarity transformations creating this behavior. Two easy conditions are derived, which provide necessary and sufficient conditions, such that the partially reduced matrices have the desired convergence behavior. The cond...
AbstractThe problem of simultaneous reduction of real matrices by either orthogonal similarity or or...
Raw data in modern machine learning usually appear as similarities or dissimilarities between member...
We will discuss two inverse eigenvalue problems. First, given the eigenvalues and a weight vector a ...
AbstractIt is a well-known fact that while reducing a symmetric matrix into a similar tridiagonal on...
It is well known how any symmetric matrix can be reduced by an orthogonal similarity transformation...
An algorithm to reduce a symmetric matrix to a similar semiseparable one of semiseparability rank 1,...
It is well known how any symmetric matrix can be reduced by an orthogonal similarity transformation...
In this paper we describe an orthogonal similarity transformation for transforming arbitrary symmetr...
In this paper a new framework for transforming arbitrary matrices to compressed representations is p...
In this paper two fast algorithms that use orthogonal similarity transformations to convert a symmet...
AbstractThe problem considered is that of simultaneous reduction to simple forms of pairs of upper t...
In this paper a new framework for transforming arbitrary matrices to compressed representations is p...
In inverse eigenvalue problems one tries to reconstruct a matrix, satisfying some constraints, given...
A square matrix can be reduced to simpler form via similarity transformations. Here ``simpler form''...
A new method of finding the eigenvalues and eigenvectors of an arbitrary complex matrix is presented...
AbstractThe problem of simultaneous reduction of real matrices by either orthogonal similarity or or...
Raw data in modern machine learning usually appear as similarities or dissimilarities between member...
We will discuss two inverse eigenvalue problems. First, given the eigenvalues and a weight vector a ...
AbstractIt is a well-known fact that while reducing a symmetric matrix into a similar tridiagonal on...
It is well known how any symmetric matrix can be reduced by an orthogonal similarity transformation...
An algorithm to reduce a symmetric matrix to a similar semiseparable one of semiseparability rank 1,...
It is well known how any symmetric matrix can be reduced by an orthogonal similarity transformation...
In this paper we describe an orthogonal similarity transformation for transforming arbitrary symmetr...
In this paper a new framework for transforming arbitrary matrices to compressed representations is p...
In this paper two fast algorithms that use orthogonal similarity transformations to convert a symmet...
AbstractThe problem considered is that of simultaneous reduction to simple forms of pairs of upper t...
In this paper a new framework for transforming arbitrary matrices to compressed representations is p...
In inverse eigenvalue problems one tries to reconstruct a matrix, satisfying some constraints, given...
A square matrix can be reduced to simpler form via similarity transformations. Here ``simpler form''...
A new method of finding the eigenvalues and eigenvectors of an arbitrary complex matrix is presented...
AbstractThe problem of simultaneous reduction of real matrices by either orthogonal similarity or or...
Raw data in modern machine learning usually appear as similarities or dissimilarities between member...
We will discuss two inverse eigenvalue problems. First, given the eigenvalues and a weight vector a ...