Code covered by the BSD License
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Basis Pursuit Denoising with ...
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Basis Pursuit with Douglas Ra...
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Constrained Total Variation P...
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Total Variation Denoising
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clamp(x,a,b)
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compute_correlation_error(His...
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compute_dual_prox(ProxF)
compute_dual_prox - compute the proximal operator of the dual
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compute_gaussian_filter(n,s,N...
compute_gaussian_filter - compute a 1D or 2D Gaussian filter.
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compute_operator_norm(A,n)
compute_operator_norm - compute operator norm
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crop(M,n,c)
crop - crop an image to reduce its size
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div(Px,Py, options)
div - divergence operator
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getoptions(options, name, v, ...
getoptions - retrieve options parameter
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grad(M, options)
grad - gradient, forward differences
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image_resize(M,p1,q1,r1)
image_resize - resize an image using bicubic interpolation
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imageplot(M,str, a,b,c)
imageplot - diplay an image and a title
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load_image(type, n, options)
load_image - load benchmark images.
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perform_admm(x, K, KS, ProxF...
perform_admm - preconditionned ADMM method
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perform_blurring(M, sigma, op...
perform_blurring - gaussian blurs an image
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perform_convolution(x,h, boun...
perform_convolution - compute convolution with centered filter.
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perform_dr(x,ProxF,ProxG,opti...
perform_dr - Douglas Rachford algorithm
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perform_fb(x, ProxF, GradG, L...
perform_admm - preconditionned ADMM method
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perform_fb_strongly(x, K, KS,...
perform_admm - preconditionned ADMM method
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perform_l1ball_projection(x,l...
perform_l1ball_projection - compute the projection on the L1 ball
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perform_soft_thresholding(x, ...
perform_soft_thresholding - soft thresholding
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perform_wavortho_transf(f,Jmi...
perform_wavortho_transf - compute orthogonal wavelet transform
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progressbar(n,N,w)
progressbar - display a progress bar
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rescale(x,a,b)
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s2v(s,a)
s2v - structure array to vector
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publish_all.m
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test_fbstrongly_analysis.m
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View all files
from
Toolbox Sparse Optmization
by Gabriel Peyre
Optimization codes for sparsity related signal processing
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| compute_operator_norm(A,n) |
function [L,e] = compute_operator_norm(A,n)
% compute_operator_norm - compute operator norm
%
% [L,e] = compute_operator_norm(A,n);
%
% Copyright (c) 2010 Gabriel Peyre
if length(n)==1
u = randn(n,1); u = u/norm(u);
else
u = n;
u = u/norm(u);
end
e = [];
for i=1:30
v = A(u);
e(end+1) = sum(u(:).*v(:));
u = v/norm(v(:));
end
L = e(end);
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