Presentation on theme: "L1-magic : Recovery of Sparse Signals via Convex programming by Emmanuel Candès and Justin Romberg CaltechOctober 2005 Compressive Sensing Tutorial PART."— Presentation transcript:
L1-magic : Recovery of Sparse Signals via Convex programming by Emmanuel Candès and Justin Romberg CaltechOctober 2005 Compressive Sensing Tutorial PART 2 Svetlana Avramov-Zamurovic January 22, 2009.
Definitions X desired vector (N elements), K sparse Y measurements (M elements), K
MATLAB programs Gabriel Peyré CNRS, CEREMADE, Université Paris Dauphine. CNRSCEREMADEUniversité Paris Dauphine Justin Romberg School of Electrical and Computer Engineering Georgia Tech omberg.html
When x, A, b have real-valued entries, (P1) can be recast as an LP. Min-L1 with equality constraints % load random states for repeatable experiments rand_state=1;randn_state=1;rand('state', rand_state);randn('state', randn_state); N = 512;% signal length T = 20;% number of spikes in the signal K = 120;% number of observations to make x = zeros(N,1);q = randperm(N);x(q(1:T)) = sign(randn(T,1)); % random +/- 1 signal% %SAZ original signal to be recovered disp('Creating measurment matrix...');A = randn(K,N);A = orth(A')';disp('Done.'); y = A*x;% observations SAZ measurements x0 = A'*y;% initial guess = min energy xp = l1eq_pd(x0, A, , y, 1e-3); % solve the LP