- Flexibility - ADMM handles a number of non-trivial problems in a common framework
- Efficiency - Often the subproblems have efficient, often embarassingly-parallel, solvers

I've quickly implemented ADMM for combined deblurring and denoising of 1D input signals using the total-variation regularization in a Generalized-Lasso problem definition. Unlike the Boyd paper, I've chosen to use Landweber iterations to solve the data subproblem as these are commonly used in large-scale deblurring and tomography. My well-commented sample implementation (see the end of this post) allows all the method parameters to be tweaked to see the effect of using inexact solves, different penalty parameters and different regularization weights.

As an example, the image below shows a 100 sample square-wave signal, blurred by a Gaussian with standard deviation of 3 and corrupted by Gaussian noise with a sigma of 5%. Only two inner Landweber iterations were used for each outer iteration. The red line shows the original blurred and noisy input, while the blue line shows the reconstructed result after 100 iterations.

Overall I find that the method works as advertised. It's fairly easy to implement, either in Matlab or in C/C++, can handle 1-norms of general linear regularizers and converges quickly. It also allows matrix-free solvers to be used for the data-subproblem, while the solve for the prior is embarrassingly-parallel, so it should scale quite well too.

You can get the implementation that produced this plot below. Note that you may get slightly different results since the noise is generated randomly.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% %%% %%% Sample signal denoising & deblurring using ADMM %%% %%% code written by James Gregson (james.gregson@gmail.com), 2013 %%% %%% Use the code for whatever you'd like %%% %%% %%% %%% Demonstrates performing Total-Variation (TV) denoising and %%% %%% deblurring of a 1D input signal using the ADMM iterative scheme %%% %%% applied to a Generalized-Lasso problem. More information on the %%% %%% approach can be found in [1]. The approach from [1] is modified %%% %%% slightly by using gradient-descent (Landweber iterations) to solve %%% %%% the first subproblem in place of a direct solver or iterative %%% %%% method such as conjugate gradient. Matrix-free Landweber %%% %%% iterations are commonly used for large-scale linear inverse %%% %%% problems and this sample code allows the accuracy of the %%% %%% subproblem solves to be adjusted to see the effect on the final %%% %%% reconstructions. %%% %%% %%% %%% [1] Boyd et al., Distributed Optimization and Statistical Learning %%% %%% via the Alternating Direction Method of Multipliers, 2010 %%% %%% %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% close all; clear all; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Problem Parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% N = 100; % Signal sample count lambda = 0.1; % Total-variation weight sigma = 3.0; % PSF sigma for generating blurred input noise = 0.05; % Gaussian-noise sigma %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% ADMM Parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% rho = 1.0; % ADMM constraint weight 0 < rho <= 2 outer_iters = 100; % Number of ADMM iterations inner_iters = 2; % Number of Landweber steps per ADMM iteration relax = 1.9; % Under-relaxation factor for Landweber steps [0,2] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Construct System PSF & Image Formation Model %%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% off = -ceil(sigma*3):ceil(sigma*3); % psf pixel offsets psf = exp( -off.^2/sigma^2 ); % psf values for offsets psf = psf/sum(psf); % normalize to 1.0 % generate psf matrix by setting diagonals based on 1D psf above M = zeros( N, N ); for i=1:numel(psf), M = M + diag( psf(i)*ones(N-abs(off(i)),1), off(i) ); end M = sparse( M ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Generate noisy and blurred synthetic input %%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% input = zeros( N, 1 ); % start with zeros input(floor(N/4):ceil(3*N/4),:) = 1.0; % make a center region at 1.0 blur = M * input + noise*(randn(N,1)); % blur the input by the psf %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Construct the difference matrix that computes image gradients %%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% A = sparse( -diag( ones(N,1), 0 ) + diag( ones(N-1,1), 1 ) ); A(N,N-1)=1; % use a backward difference for the final point %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Setup ADMM and perform iterations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % compute the eigenvalues of the first subproblem % system matrix and use the largest to define a % Landweber step size, for large system a a power- % iteration should be used instead. tmp = eigs(M'*M + rho*A'*A); step = relax/tmp(1); % initialize the ADMM variables x = blur; % intrinsic (sharp) signal z = zeros( N, 1 ); % splitting variable u = zeros( N, 1 ); % scaled Lagrange multipliers % define an anonymous shrinkage operator to implement % the second sub-problem solve shrink = @(kappa,x) max( abs(x)-kappa, 0 ).*sign(x); % perform outer_iters outer iterations, plot the % solver progress as the iterations proceed for k=1:outer_iters, fprintf( 1, 'iteration %d of %d\n', k, outer_iters ); plot( x, 'b-+' ); drawnow; % define an anonymous function returning the gradient % of the first subproblem w.r.t. x, holding z and u % fixed, then perform gradient-descent (Landweber % iterations). gradF = @(x) M'*(M*x) - M'*blur + rho*A'*( A*x - z + u ); x = gradient_descent( gradF, x, step, inner_iters ); % solve the second sub-problem using the anonymous % shrinkage operator z = shrink( lambda/rho, A*x + u ); % update the scaled Lagrange multipliers u = u + A*x - z; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Setup ADMM and perform iterations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% hold on; plot( blur, 'r-o' ); plot( x, 'b-o' );

## 1 comment:

gradient_descent is undefined. Could you provide the function definition please?

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