sparsify_0_4
所属分类：matlab编程
开发工具：matlab
文件大小：1100KB
下载次数：15
上传日期：2010-07-19 08:34:30
上 传 者：
johnchy
说明： sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.
(sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.)
文件列表:
sparsify_0_4 (0, 2009-01-21)
sparsify_0_4\Examples (0, 2008-02-08)
sparsify_0_4\Examples\@MyObjectName (0, 2008-02-08)
sparsify_0_4\Examples\@MyObjectName\.DS_Store (6148, 2007-11-22)
__MACOSX (0, 2009-01-21)
__MACOSX\sparsify_0_4 (0, 2009-01-21)
__MACOSX\sparsify_0_4\Examples (0, 2009-01-21)
__MACOSX\sparsify_0_4\Examples\@MyObjectName (0, 2009-01-21)
__MACOSX\sparsify_0_4\Examples\@MyObjectName\._.DS_Store (82, 2007-11-22)
sparsify_0_4\Examples\@MyObjectName\MyObjectName.m (609, 2007-04-30)
__MACOSX\sparsify_0_4\Examples\@MyObjectName\._MyObjectName.m (82, 2007-04-30)
sparsify_0_4\Examples\@MyObjectName\ctranspose.m (422, 2007-04-30)
__MACOSX\sparsify_0_4\Examples\@MyObjectName\._ctranspose.m (82, 2007-04-30)
sparsify_0_4\Examples\@MyObjectName\mtimes.m (1414, 2007-04-30)
__MACOSX\sparsify_0_4\Examples\@MyObjectName\._mtimes.m (82, 2007-04-30)
sparsify_0_4\Examples\.DS_Store (6148, 2007-11-22)
__MACOSX\sparsify_0_4\Examples\._.DS_Store (82, 2007-11-22)
sparsify_0_4\Examples\Example_function.m (1408, 2007-05-01)
__MACOSX\sparsify_0_4\Examples\._Example_function.m (82, 2007-05-01)
sparsify_0_4\Examples\Example_matrix.m (1015, 2007-05-01)
__MACOSX\sparsify_0_4\Examples\._Example_matrix.m (82, 2007-05-01)
sparsify_0_4\Examples\Example_object.m (1229, 2007-05-01)
__MACOSX\sparsify_0_4\Examples\._Example_object.m (82, 2007-05-01)
sparsify_0_4\Examples\MyOpTranspose_witharg.m (722, 2007-05-01)
__MACOSX\sparsify_0_4\Examples\._MyOpTranspose_witharg.m (82, 2007-05-01)
sparsify_0_4\Examples\MyOp_witharg.m (561, 2007-05-01)
__MACOSX\sparsify_0_4\Examples\._MyOp_witharg.m (82, 2007-05-01)
sparsify_0_4\GreedLab (0, 2008-02-08)
sparsify_0_4\GreedLab\OMP_algos (0, 2008-02-08)
sparsify_0_4\GreedLab\OMP_algos\.19495-199122737-5.m (12786, 2007-04-24)
sparsify_0_4\GreedLab\OMP_algos\.DS_Store (12292, 2007-11-22)
__MACOSX\sparsify_0_4\GreedLab (0, 2009-01-21)
__MACOSX\sparsify_0_4\GreedLab\OMP_algos (0, 2009-01-21)
__MACOSX\sparsify_0_4\GreedLab\OMP_algos\._.DS_Store (82, 2007-11-22)
sparsify_0_4\GreedLab\OMP_algos\greed_omp_cg.m (13053, 2008-02-08)
__MACOSX\sparsify_0_4\GreedLab\OMP_algos\._greed_omp_cg.m (82, 2008-02-08)
sparsify_0_4\GreedLab\OMP_algos\greed_omp_cgp.m (15336, 2008-02-08)
__MACOSX\sparsify_0_4\GreedLab\OMP_algos\._greed_omp_cgp.m (82, 2008-02-08)
sparsify_0_4\GreedLab\OMP_algos\greed_omp_chol.m (14064, 2008-02-08)
__MACOSX\sparsify_0_4\GreedLab\OMP_algos\._greed_omp_chol.m (82, 2008-02-08)
... ...
% __________________sparsify Version 0.4__________________
%
%
% Copyright (c) 2007 Thomas Blumensath
%
% The University of Edinburgh
% Email: thomas.blumensath@ed.ac.uk
% Comments and bug reports welcome
%
% This file is part of sparsity Version 0.4
% Created: January 2009
%
% Part of this toolbox was developed with the support of EPSRC Grant
% D000246/1
%
% Please read COPYRIGHT.m for terms and conditions.
%
%
% __________________INSTALLATION__________________
%
% 1) Copy the folder sparsify wherever you like.
% 2) Include the folder sparsify and all sub-folders in the Matlab search path.
%
% __________________OVERVIEW__________________
%
% sparsify is a set of Matlab m files implementing a range of different algorithms
% to calculate sparse signal approximations. See ALGORITHMS below for a list of
% available algorithms.
%
% __________________COMPATIBILITY__________________
%
% sparsify was tested with Matlab 7.2 and 7.4.
% sparsify does not require any additional toolboxes.
%
% All functions are designed to work with different input formats.
% The specific formatting instructions can be found in function_format.m and
% object_format.m
%
% The function format is compatible to the l1-magic toolbox [1] and with the GPSR software [4].
% The object format is compatible with the l1_ls.m algorithm [3].
% Unfortunately, SparseLab [5] uses a different function format, however, this
% can easily be converted into the format required for sparsify using:
% D =@(z) P(1, m, n, z, I, dim)
% and
% Dt =@(z) P(2, m, n, z, I, dim)
% where P is the function used in SparseLab. D and Dt are then the functions
% required for sparsify. See function_format.m for more information.
%
%
% __________________STRUCTURE__________________
%
% sparsify contains four sub-folders with the following contents:
% GreedLab : A range of greedy algorithms (see ALGORITHMS FOR DETAIL)
% HardLab : A range of iterative hard-thresholding algorithms
% Examples : Example code demonstrating the use of the algorithms
% TestMethod : A function to test the available algorithms
%
%
% The folder GreedLab contains:
%
% greed_omp.m
% greed_ols.m
% greed_mp.m
% greed_gp.m
% greed_nomp.m
% greed_nomppgc.m
% nonlin_gg.m
% The subfolder OMP_algos containing:
% greed_omp_qr.m
% greed_omp_chol.m
% greed_omp_cg.m
% greed_omp_cgp.m
% greed_omp_pinv.m
% greed_omp_linsolve.m
%
% The folder HardLab contains:
%
% hard_l0_reg
% hard_lo_Mterm
%
% The folder Examples contains:
%
% Example_object,m
% Example_function.m
% Example_matrix.m
% MyOp_witharg.m
% MyOpTranspose_witharg.m
% The subfolder @MyObjectName containing:
% mtimes.m
% MyObjectName.m
% ctranspose.m
%
% The folder TestMethod contains:
%
% Testsparsify.m
%
% The folder Papers contains:
%
%
%
% __________________ALGORITHMS__________________
%
% For more information on each algorithm type "help ALGORITHMNAME.m"
%
% greed_omp.m Orthogonal matching Pursuit algorithm. Different implementations are
% accessible through greed_omp.m. These are also available directly:
% greed_omp_qr.m OMP using QR factorisation (Fastest algorithm but
% requires most storage.)
% greed_omp_chol.m OMP using Cholesky factorisation (Slower than QR
% based method but less storage required. Useful up
% to around 10 000 non-zero coefficients)
% greed_omp_cg.m OMP using full conjugate gradient solver in each
% iteration (Only option if everything else fails. But
% can be slow.)
% greed_omp_cgp.m OMP using Conjugate Gradient Pursuit algorithm [1]
% (Similar to QR based method)
% greed_omp_pinv.m OMP using pinv command (NOT RECOMMENDED, for
% reference only.)
% greed_omp_linsolve.m OMP using linsolve command (NOT RECOMMENDED, for
% reference only.)
% greed_ols.m Orthogonal Least Squares Algorithm (similar to OMP but with a
% different element selection rule) see [6]. (Requires storage of Dictionary as matrix, so only applicable for small problems.)
% greed_mp.m Matching Pursuit algorithm
% greed_gp.m Greedy Pursuit algorithm from [1] (Can be used if OMP is too costly.)
% greed_nomp.m Approximate Conjugate Gradient Pursuit (ACGP) from [1] (Can be used
% if OMP is too costly. Slower but better than GP in general.)
% greed_nomppgc.m Nearly Orthogonal Matching Pursuit with partial Conjugate
% Gradients (NOMPpCG) a mixture between Gradient Pursuit and
% Approximate Conjugate Gradient Pursuit using a gradient step
% when a new element is selected and an ACGP step when no new
% element is selected. This guarantees convergence in a finite
% number of steps but can give worse results in practice than ACGP.
%
% nonlin_gg Gradient based greedy algorithm to solve non-linear sparse problems [8].
%
% hard_l0_reg Iterative hard thresholding algorithm keeping elements larger than fixed
% threshold in each iteration [7].
% hard_lo_Mterm Iterative hard thresholding algorithm keeping largest M elements
% in each iteration [7, 9, 10]. This algorithm now includes an
% automatic stepsize calculation as derived in [10]
%
%
% __________________NAMING CONVENTION__________________
%
% All algorithm names are preceded by the identifiers greed_ or hard_ (see above)
% This ensures that conflicts with other toolboxes implementing the same algorithm
% are avoided.
%
% __________________REFERENCES__________________
% [1] T. Blumensath and M.E. Davies, "Gradient Pursuits", submitted, 2007
% [2] E. Candes and J. Romberg "l1-magic" http://www.acm.caltech.edu/l1magic/
% [3] K. Koh, S-J Kim and S. Boyd, "l1_ls.m", http://www.stanford.edu/~boyd/l1_ls/
% [4] M. Figueiredo, R. D. Nowak and S. J. Wright "Gradient Projection for Sparse
% Reconstruction (GPSR)", http://www.lx.it.pt/~mtf/GPSR/
% [5] D. Donoho et al., "SparseLab", http://sparselab.stanford.edu/
% [6] S. Chen and S. A. Billings Modelling and analysis of non-linear time series.
% International Journal of Control, 50 pp. 2151-2171, 1***9
% [7] T. Blumensath and M. Davies "Iterative Thresholding for Sparse Approximations" accepted for publication, 2007
% [8] T. Blumensath and M. E. Davies; "Gradient Pursuit for Non-Linear Sparse Signal Modelling", submitted to EUSIPCO, 2008
% [9] T. Blumensath and M. Davies; "Iterative Hard Thresholding for Compressed Sensing" to appear Applied and Computational Harmonic Analysis
% [10] T. Blumensath and M. Davies; "A modified Iterative Hard Thresholding algorithm with guaranteed performance and stability" in preparation (title may change)
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