Norms of random submatrices and sparse approximation
نویسنده
چکیده
Many problems in the theory of sparse approximation require bounds on operator norms of a random submatrix drawn from a fixed matrix. The purpose of this note is to collect estimates for several different norms that are most important in the analysis of `1 minimization algorithms. Several of these bounds have not appeared in detail. Résumé Sur la norme de sous-matrice tirée aléatoirement. Beaucoup de problèmes en théorie d’approximation non linéaire demandent de majorer la norme d’une matrice aléatoirement extraite d’une matrice fixe de plus grandes dimensions. L’objectif de cette note est de présenter quelques estimations de ces normes qui se revèlent être importantes pour l’étude des algorithmes de minimisation de type `1. Plusieurs de ces bornes n’ont pas encore été publiées explicitement.
منابع مشابه
Uniform uncertainty principle for Bernoulli and subgaussian ensembles
In [CT1] Candes and Tao studied problems of approximate and exact reconstruction of sparse signals from incomplete random measurements and related them to the eigenvalue behavior of submatrices of matrices of random measurements. In particular they introduced the notion they called the uniform uncertainty principle (UUP, defined below) and studied it for Gaussian, Bernoulli and Fourier ensemble...
متن کاملApproximating Eigenvectors by Subsampling
We show that averaging eigenvectors of randomly sampled submatrices efficiently approximates the true eigenvectors of the original matrix under certain conditions on the incoherence of the spectral decomposition. This incoherence assumption is typically milder than those made in matrix completion and allows eigenvectors to be sparse. We discuss applications to spectral methods in dimensionality...
متن کاملSuperboolean Rank and the Size of the Largest Triangular Submatrix of a Random Matrix
We explore the size of the largest (permuted) triangular submatrix of a random matrix, and more precisely its asymptotical behavior as the size of the ambient matrix tends to infinity. The importance of such permuted triangular submatrices arises when dealing with certain combinatorial algebraic settings in which these submatrices determine the rank of the ambient matrix, and thus attract a spe...
متن کاملApproximation of matrices
We improve here two results from the preprints [DK], [AFKK] on approximating matrices by random submatrices. We use a construction of a random subset of a finite set, which is different from what was suggested in [AFKK]. Instead of taking a random q-element subset of an n-element set, we consider independent {0, 1}-valued random variables δ1, . . . , δn, taking value 1 with probability δ = q/n....
متن کاملAlgebraic Sub-structuring for Electromagnetic Applications
Algebraic sub-structuring refers to the process of applying matrix reordering and partitioning algorithms to divide a large sparse matrix into smaller submatrices from which a subset of spectral components are extracted and combined to form approximate solutions to the original problem. In this paper, we show that algebraic sub-structuring can be effectively used to solve generalized eigenvalue...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008