close all;
clear all;
clc;
M = 4;
N = 128;
%block_sparsity = 1;
tol = 1e-4;
trial = 20;
max_sparsity = 25;
epi = 0.02;
result = zeros(N,max_sparsity);
FAR_model = get_far_model(N, M, epi);

for n = 60:N
    for s = 10:max_sparsity
        success_count = 0;
        for loop = 1:trial
            col_choose = randperm(N,n);
            FAR_model_partial = FAR_model(col_choose,:);

            sparse_signal = zeros(M,N);
            block = randperm(N,s);
            sparse_signal(:,block) = exp(1i*2*pi*rand(M,s));

            y = FAR_model_partial * sparse_signal(:);

            cvx_begin
            variable x(M,N) complex
            norm21 = 0;
            for i = 1:N
                norm21 = norm21 + norm(x(:,i));
            end
            minimize(norm21)
            subject to
            FAR_model_partial * x(:) == y
            cvx_end

            if norm(x(:)-sparse_signal(:))<tol
                success_count = success_count+1;
            end
        end
        result(n,s) = success_count/trial;
    end
end
save('FARblockepsilon4.mat');