ﻻ يوجد ملخص باللغة العربية
Pseudospectra and structured pseudospectra are important tools for the analysis of matrices. Their computation, however, can be very demanding for all but small matrices. A new approach to compute approximations of pseudospectra and structured pseudospectra, based on determining the spectra of many suitably chosen rank-one or projected rank-one perturbations of the given matrix is proposed. The choice of rank-one or projected rank-one perturbations is inspired by Wilkinsons analysis of eigenvalue sensitivity. Numerical examples illustrate that the proposed approach gives much better insight into the pseudospectra and structured pseudospectra than random or structured random rank-one perturbations with lower computational burden. The latter approach is presently commonly used for the determination of structured pseudospectra.
This manuscripts contains the proofs for A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction.
We study two techniques for correcting the geometrical error associated with domain approximation by a polygon. The first was introduced some time ago cite{bramble1972projection} and leads to a nonsymmetric formulation for Poissons equation. We intro
We present a class of fast subspace tracking algorithms based on orthogonal iterations for structured matrices/pencils that can be represented as small rank perturbations of unitary matrices. The algorithms rely upon an updated data sparse factorizat
The goal of the emph{alignment problem} is to align a (given) point cloud $P = {p_1,cdots,p_n}$ to another (observed) point cloud $Q = {q_1,cdots,q_n}$. That is, to compute a rotation matrix $R in mathbb{R}^{3 times 3}$ and a translation vector $t in
The task of predicting missing entries of a matrix, from a subset of known entries, is known as textit{matrix completion}. In todays data-driven world, data completion is essential whether it is the main goal or a pre-processing step. Structured matr