Brief introduction in greedy approximation

Fuente: arXiv
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Autore principale: Temlyakov, V.
Natura: Preprint
Pubblicazione: 2025
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author Temlyakov, V.
author_facet Temlyakov, V.
contents Sparse approximation is important in many applications because of concise form of an approximant and good accuracy guarantees. The theory of compressed sensing, which proved to be very useful in the image processing and data sciences, is based on the concept of sparsity. A fundamental issue of sparse approximation is the problem of construction of efficient algorithms, which provide good approximation. It turns out that greedy algorithms with respect to dictionaries are very good from this point of view. They are simple in implementation and there are well developed theoretical guarantees of their efficiency. This survey/tutorial paper contains brief description of different kinds of greedy algorithms and results on their convergence and rate of convergence. Also, Chapter IV gives some typical proofs of convergence and rate of convergence results for important greedy algorithms and Chapter V gives some open problems.
format Preprint
id arxiv_https___arxiv_org_abs_2502_13432
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Brief introduction in greedy approximation
Temlyakov, V.
Numerical Analysis
Functional Analysis
Sparse approximation is important in many applications because of concise form of an approximant and good accuracy guarantees. The theory of compressed sensing, which proved to be very useful in the image processing and data sciences, is based on the concept of sparsity. A fundamental issue of sparse approximation is the problem of construction of efficient algorithms, which provide good approximation. It turns out that greedy algorithms with respect to dictionaries are very good from this point of view. They are simple in implementation and there are well developed theoretical guarantees of their efficiency. This survey/tutorial paper contains brief description of different kinds of greedy algorithms and results on their convergence and rate of convergence. Also, Chapter IV gives some typical proofs of convergence and rate of convergence results for important greedy algorithms and Chapter V gives some open problems.
title Brief introduction in greedy approximation
topic Numerical Analysis
Functional Analysis
url https://arxiv.org/abs/2502.13432