Use CVX and Matpower in Matlab Online

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Diana Vazquez Romo
Diana Vazquez Romo 2018-12-18
评论： Walter Roberson 2023-4-29
Hi everyone,
I would like to use Matlab however I can't download the software on my computer as I do not have admin rights.
Thus I am using Matlab Online. I would like to use CVX and Matpower and was wondering what is the best way to do so.
I know that to install CVX you must download the zip folder, extract it, navigate to it on Matlab and run the cvx_setup command. I thought about doing this on Matlab Online. I'd just download the folder into Matlab Drive and navigate to it on Matlab Online.
However.... this drive only allows for file downloads. So if I'd want to download the CVX folder I'd have to download file by file while making sure file hiearchies are intact (I think this last part is necessary for CVX to run properly). The same goes for Matpower.
Any suggestions?
Thank You
-Diana
  4 个评论
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Deep Kiran
Deep Kiran 2021-5-28
Hi Diana,
Can you please write the steps for running these two for online matlab?
Thanks
Gian Albert Alfani
Gian Albert Alfani 2021-6-8
@Deep Kiran Download linux version and upload to drive. then just setup

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回答（1 个）

swapnil
swapnil 2023-4-29
% Define the signal as a combination of 4 harmonics
t = linspace(0, 10, 5000);
x = 0.2*cos(2*pi*t) + 0.5*sin(4*pi*t) + 0.35*cos(16*pi*t);
% Plot the original signal
figure;
plot(t, x);
title('Original Signal');
% Sample the signal
fs = 120; % Minimum sampling frequency = 2*16 Hz
Ts = 1/fs;
n = floor(10/Ts);
ts = linspace(0, 10, n);
xs = 0.2*cos(2*pi*ts) + 0.5*sin(4*pi*ts) + 0.35*cos(16*pi*ts);
% Plot the samples
figure;
stem(ts, xs);
title('Sampled Signal');
% Compute the DCT of the signal
d = dct(x);
% Compute the norm zero of the DCT
norm0_dct = nnz(d == 0);
% Plot the DCT of the signal
figure;
stem(d);
title('DCT of Signal');
% Define the measurement matrix phi
m = 100; % Number of measurements
phi = randn(m, length(x));
% Measure the DCT in the form of y = phi * d
y = phi * d;
% Solve the convex optimization problem to recover d
cvx_begin
variable d_hat(length(x))
minimize(norm(d_hat, 1))
subject to
phi * d_hat == y
cvx_end
% Plot the recovered DCT of the signal
figure;
stem(d_hat);
title('Recovered DCT of Signal');
% Find the inverse DCT of the recovered signal
x_hat = idct(d_hat);
% Plot the reconstructed signal
figure;
plot(t, x, 'b', t, x_hat, 'r');
title('Reconstructed Signal');
legend('Original', 'Recovered');
% Increase the number of rows in phi
m = 500; % Number of measurements
phi = randn(m, length(x));
y = phi * d;
cvx_begin
variable d_hat(length(x))
minimize(norm(d_hat, 1))
subject to
phi * d_hat == y
cvx_end
x_hat = idct(d_hat);
figure;
plot(t, x, 'b', t, x_hat, 'r');
title('Reconstructed Signal (Increased Number of Rows in Phi)');
legend('Original', 'Recovered');
% Increase the sparsity of d
d_sparse = zeros(size(d));
d_sparse(1:10:end) = d(1:10:end); % Set every 10th coefficient to its original value
y = phi * d_sparse;
cvx_begin
variable d_sparse_hat(length(x))
minimize(norm(d_sparse_hat, 1))
subject to
phi * d_sparse_hat == y
cvx_end
x_sparse_hat = idct(d_sparse_hat);
figure;
plot(t, x, 'b', t, x_sparse_hat, 'r');
title('Reconstructed Signal (Increased Sparsity)');
legend('Original', 'Recovered');
% Compute the compression ratio
cr = length(x)/m;
fprintf('Compression ratio: %f\n', cr);

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