20 pages. Comments welcomeThe QR-algorithm is one of the most important algorithms in linear algebra. Its several variants make feasible the computation of the eigenvalues and eigenvectors of a numerical real or complex matrix, even when the dimensions of the matrix are enormous. The first adaptation of the QR-algorithm to local fields was given by the first author in 2019. However, in this version the rate of convergence is only linear and in some cases the decomposition into invariant subspaces is incomplete. We present a refinement of this algorithm with a super-linear convergence rate in many cases
AbstractIn the year 2000 the dominant method for solving matrix eigenvalue problems is still the QR ...
Abstract. We show that the shifted QR iteration applied to a companion matrix F maintains the weakly...
在計算矩陣的特徵值(eigenvalues)中,QR演算法是一種常見的技巧. 尤其如果使用適當的移位,將可以較快得到特徵值. 在本文中提出一種新的移位策略, 我們證明這各方法是可行的,而且可適用於任何...
Abstract: Spectral computations of infinite-dimensional operators are notoriously difficult, yet ubi...
Spectral computations of infinite-dimensional operators are notoriously difficult, yet ubiquitous in...
The QR algorithm computes a Schur decomposition of a matrix. It is certainly one of the most importa...
Recently a generalization of Francis’s implicitly shifted QR-algorithm was proposed, notably widenin...
Aggressive early deflation has proven to significantly enhance the convergence of the QR algorithm f...
Abstract. The convergence results obtained by J. H. Wilkinson [Linear Algebra Appl. 1 (1968) 409420]...
Abstract—In this paper, we present the QR Algorithm with Permutations that shows an improved converg...
AbstractWe examine global convergence properties of the Francis shifted QR algorithm on real, normal...
Rapid convergence of the shifted QR algorithm on symmetric matrices was shown more than fifty years ...
We give a self-contained randomized algorithm based on shifted inverse iteration which provably comp...
We present a new deflation criterion for the multishift QR algorithm motivated by convergence analys...
Most reduced Hessian methods for equality constrained problems use a basis for the null space of th...
AbstractIn the year 2000 the dominant method for solving matrix eigenvalue problems is still the QR ...
Abstract. We show that the shifted QR iteration applied to a companion matrix F maintains the weakly...
在計算矩陣的特徵值(eigenvalues)中,QR演算法是一種常見的技巧. 尤其如果使用適當的移位,將可以較快得到特徵值. 在本文中提出一種新的移位策略, 我們證明這各方法是可行的,而且可適用於任何...
Abstract: Spectral computations of infinite-dimensional operators are notoriously difficult, yet ubi...
Spectral computations of infinite-dimensional operators are notoriously difficult, yet ubiquitous in...
The QR algorithm computes a Schur decomposition of a matrix. It is certainly one of the most importa...
Recently a generalization of Francis’s implicitly shifted QR-algorithm was proposed, notably widenin...
Aggressive early deflation has proven to significantly enhance the convergence of the QR algorithm f...
Abstract. The convergence results obtained by J. H. Wilkinson [Linear Algebra Appl. 1 (1968) 409420]...
Abstract—In this paper, we present the QR Algorithm with Permutations that shows an improved converg...
AbstractWe examine global convergence properties of the Francis shifted QR algorithm on real, normal...
Rapid convergence of the shifted QR algorithm on symmetric matrices was shown more than fifty years ...
We give a self-contained randomized algorithm based on shifted inverse iteration which provably comp...
We present a new deflation criterion for the multishift QR algorithm motivated by convergence analys...
Most reduced Hessian methods for equality constrained problems use a basis for the null space of th...
AbstractIn the year 2000 the dominant method for solving matrix eigenvalue problems is still the QR ...
Abstract. We show that the shifted QR iteration applied to a companion matrix F maintains the weakly...
在計算矩陣的特徵值(eigenvalues)中,QR演算法是一種常見的技巧. 尤其如果使用適當的移位,將可以較快得到特徵值. 在本文中提出一種新的移位策略, 我們證明這各方法是可行的,而且可適用於任何...