53 lines
1.6 KiB
Matlab
Executable File
53 lines
1.6 KiB
Matlab
Executable File
function x_hat = recovery(A, y_noise, method)
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if method == "debiased_LASSO"
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sz = size(A);
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N = sz(2);
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LASSO_lambda = 0.1;
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gamma = sz(1) / sz(2);
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cvx_begin quiet
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variable x_LASSO(N) complex
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minimize(LASSO_lambda * norm(x_LASSO, 1) + norm(y_noise - A * x_LASSO, 2))
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cvx_end
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rho_active = sum(abs(x_LASSO) > 1e-3)/N;
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Q_hat = (gamma - rho_active)/(1 - rho_active);
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Rho = sum((abs(x_LASSO) > 1e-3).* (2 - LASSO_lambda./(Q_hat*abs(x_LASSO) + LASSO_lambda))) / 2 / N;
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diff = 1;
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while(diff > 1e-4)
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Rho_pre = Rho;
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Rho = sum((abs(x_LASSO) > 1e-3).* (2 - LASSO_lambda./((gamma-Rho)/(1-Rho)*abs(x_LASSO) + LASSO_lambda))) / 2 / N;
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diff = abs(Rho - Rho_pre);
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end
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Q_hat = (gamma-Rho)/(1-Rho);
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x_d_CROD = x_LASSO + A'*(y_noise - A*x_LASSO)/Q_hat;
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x_hat = x_d_CROD;
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elseif method == "LASSO"
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sz = size(A);
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N = sz(2);
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LASSO_lambda = 0.1;
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cvx_begin quiet
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variable x_LASSO(N) complex
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minimize(LASSO_lambda * norm(x_LASSO, 1) + norm(y_noise - A * x_LASSO, 2))
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cvx_end
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x_hat = x_LASSO;
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elseif method == "BP"
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sz = size(A);
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N = sz(2);
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cvx_begin quiet
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variable x_hat(N) complex
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minimize(norm(x_hat, 1))
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subject to
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A * x_hat == y_noise
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cvx_end
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else
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global lambda tau iter_max;
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[x_hat_wl, x_hat] = cVAMPro(y_noise, A, lambda, tau, iter_max);
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end
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end
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