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version 1.7.1
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Nathanael Perraudin committed Dec 27, 2015
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4 changes: 2 additions & 2 deletions demos/demo_dequantization.m
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%DEMO_DEQUANTIZATION
%DEMO_DEQUANTIZATION Dequantization demo
% This demo shows how a quantized signal, sparse in the DCT domain, can be dequantized
% solving a convex problem using Douglas-Rachford algorithm
%
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% .. C = { x | ||Dx - y_Q||_infty <= alpha/2 }
%
% .. math:: C = \{ x | \|Dx - y_Q\|_\infty <= \frac{\alpha}{2} \}
%d
%
% Its prox is the orthogonal projection onto that set, which is realized
% by entry-wise 1D projections onto the quantization stripes. This is
% realized for all the entries at once by function proj_box.
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1 change: 1 addition & 0 deletions prox/Contents.m
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% PROX_TV1D - Proximal operator of the 1D TV norm
% PROX_TV4D - Proximal operator of the 4D TV norm
% PROX_SUM_LOG - Proximal operor for the sum of logarithms.
% PROX_SUM_LOG_NORM2 - Proximal operator of log-barrier - sum(log(x))
%
% Projection operators
% PROJ_B1 - Projection on a B1-Ball
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4 changes: 2 additions & 2 deletions prox/prox_sum_log.m
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% sol : Solution.
% info : Structure summarizing informations at convergence
%
% `prox_l1(x, gamma, param)` solves:
% `prox_sum_log(x, gamma, param)` solves:
%
% .. sol = argmin_{z} 0.5*||x - z||_2^2 - gamma * sum(log(z))
%
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% * *info.crit* : Stopping critterion used
%
%
% See also: prox_l1 prox_l2 prox_tv
% See also: prox_l1 prox_l2 prox_tv prox_sum_log_norm2
%

% Author: Vassilis Kalofolias
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7 changes: 3 additions & 4 deletions prox/prox_sum_log_norm2.m
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function [sol,info] = prox_sum_log_norm2(x, alpha, beta, gamma, param)
%PROX_SUM_LOG_NORM2 Proximal operator of log-barrier - sum(log(x))
% Usage: sol = prox_sum_log(x, alpha, beta, gamma)
% sol = prox_sum_log(x, alpha, beta, gamma, param)
% Usage: sol = prox_sum_log_norm2(x, alpha, beta, gamma)
% sol = prox_sum_log_norm2(x, alpha, beta, gamma, param)
% [sol, info] = prox_sum_log_norm2(x, alpha, beta, gamma, param)
%
% Input parameters:
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%
% `prox_l1(x, gamma, param)` solves:
%
% .. sol = argmin_{z} 0.5*||x - z||_2^2 - gamma * (alpha*sum(log(z)) +
% beta/2 ||z||^2)
% .. sol = argmin_{z} 0.5*||x - z||_2^2 - gamma * (alpha*sum(log(z)) + beta/2 ||z||^2)
%
% .. math:: sol = \arg\min_{z} \frac{1}{2} \|x - z\|_2^2 - \gamma (\alpha \sum_i(log(z_i)) + \beta/2 \|z\|_2^2)
%
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6 changes: 3 additions & 3 deletions solver/fbf_primal_dual.m
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% Warning! This stopping criterion is different from other solvers!
%
% * *param.mu* : parameter mu of paper [1]
% * *param.epsilon: parameter epsilon of paper [1]
% * *param.normalized_timestep: from 0 to 1, mapping to [epsilon,
% (1-epsilon)/mu]
% * *param.epsilon*: parameter epsilon of paper [1]
% * *param.normalized_timestep*: from 0 to 1, mapping to [epsilon,
% (1-epsilon)/mu]
%
% See also: solvep fb_based_primal_dual
%
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2 changes: 1 addition & 1 deletion solver/solvep.m
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%
% For the 'rel_norm' stopping criterion, the algorithm end if
%
% .. || n(t) - n(t-1) ||_2 / || n(t) ||_2 < tol,
% .. ' || n(t) - n(t-1) ||_2 / || n(t) ||_2 < tol,
%
% .. math:: \frac{ \| n(t) - n(t-1) \|_2 }{\| n(t)\|_2} < tol,
%
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2 changes: 1 addition & 1 deletion unlocbox_version
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1.6.3
1.7.1
2 changes: 1 addition & 1 deletion utils/Contents.m
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% Other
% snr - Signal to noise ratio
% soft_threshold - Soft thresholding operation
% seet_seed - Set a new seed
% set_seed - Set a new seed
% vec - Vectorize
% svdecon - Acceleleration of svd
% svdsecon - Acceleleration of svds
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15 changes: 8 additions & 7 deletions utils/set_seed.m
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function set_seed(my_seed)
%SET_SEED sets the seed of the default random random generator
% Usage: set_seed(new_seed)
% set_seed
% Usage: set_seed(my_seed)
% set_seed()
%
% Input parameters:
% new_seed
%
% Output parameters:
% none
%
% my_seed : new_seed
%
% Set the seed of the default random random generator

% code author: Vassilis Kalofolias
% date: August 2013

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%rng('default');
end

end



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