Simultaneous iteration for computing invariant subspaces of non-Hermitian matrices

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization (2019, background): “Here, we simply note that for any non-square matrix A , we can instead consider which is symmetric and has the same eigenvectors and eigenvalues as the original matrix A —see Stewart (1976) for more details on handling such cases.”
Eigenvalue computation in the 20th century (2000, background): “Its convergence behaviour for general unsymmetric matrices was studied by Stewart [126] in 1976.”
Inexact Inverse Iteration for Generalized Eigenvalue Problems (2000, methodology): “It is usually used in combination with an iterative method such as the power method (inverse iteration), subspace iteration [ 19 ], the Lanczos algorithm [7] and the Arnoldi algorithm [1] (see also [4, 13]).”
The Quadratic Eigenvalue Problem (2001, methodology): “The orthogonal Schur basis is useful for building a stable method for purging and locking [50], [93], [123], [136], as we now briefly describe.”