Journal Article10.1007/BF02124742
Some remarks on greedy algorithms
TL;DR: Three greedy algorithms are discussed: the Pure GreedyAlgorithm, an Orthogonal Greedy Algorithm, and a Relaxed Gre greedy Algorithm.
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Abstract: Estimates are given for the rate of approximation of a function by means of greedy algorithms. The estimates apply to approximation from an arbitrary dictionary of functions. Three greedy algorithms are discussed: the Pure Greedy Algorithm, an Orthogonal Greedy Algorithm, and a Relaxed Greedy Algorithm.
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Citations
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A wavelet tour of signal processing
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TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
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Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
- 01 Aug 2007
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
Atomic Decomposition by Basis Pursuit
TL;DR: This work gives examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution, and obtains reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.
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A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training
TL;DR: In this article, a general convergence criterion for certain iterative sequences in Hilbert space is presented, and estimates of the rate of convergence are given for an important subclass of these sequences.
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