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Context

Attempting to reproduce an illustrative example of compressive sampling from Candes-Wakin 2008. Specifically, the L1 recovery of a sparse signal shown on pg 5 in Fig. 2. Using my code (below), results in ~12 - 20% error rates for exact reconstruction whereas the example seems to suggest we should get closer to 0%.

Question

Where does my implementation go wrong? Perhaps it's i) the order of sampling indices first, followed by inverse dct or ii) the ECOS solver that cvxpy invokes (instead of a more appropriate solver?)

Note: Fig 2 in Candes-Wakin suggests reconstruction from complex valued Fourier coefficients, whereas I use discrete cosine transform along the lines of Rish-Grabarnik section 3.2 in their book (link from Prof. Rish's website)

Code numpy (1.20.x), scipy, cvxpy (1.1.14)

import numpy as np
import scipy.fftpack as spfft
import cvxpy as cvx
from matplotlib import pyplot as plt


def set_signal(low: int = -1, high: int = 1, n: int = 10) -> np.ndarray:
    """Return a bounded uniform random vector in R^n."""
    s = np.random.uniform(low=low, high=high, size=(n, 1))
    return s


def sparsify_signal(s: np.ndarray, n_sparse: int = 1) -> np.ndarray:
    """Set random n_sparse many elements of s to zero."""
    if not isinstance(s, np.ndarray):
        s = np.array(s)
    indices_to_zero = np.random.choice(a=len(s), size=n_sparse, replace=False)
    s[indices_to_zero] = 0
    return s


# PARAMS
N_dim = 512  # real-dim of where original signal lives
S_dim = 30  # support size: num non-zero entries in original signal
null_dim = N_dim - S_dim
m_samples = 60  # alternative: S_sparse * log(N_dim), for some log base...
delta = 1e-5  # lower bound nonzero solution terms; used in post-opt analysis

# INITIALIZE
X = set_signal(n=N_dim)  # won't reconstruct this, but sparse version of it
X_sparse = sparsify_signal(s=X, n_sparse=null_dim)
yt = spfft.dct(X_sparse, norm='ortho')   # project sparse signal to (real) Fourier

# PROJECT AND SAMPLE
sample_indices = np.random.choice(N_dim, m_samples, replace=False)  # fix coordinates to sample
sample_indices.sort()  # plotting convenience
yt_sample = yt[sample_indices]  # restrict sparse signal to random sample indices
yt_sample = yt_sample.squeeze()
# Discrete cosine transform as matrix representation
A = spfft.dct(np.identity(N_dim), norm='ortho', axis=0)
A = A[sample_indices]

# Optimization
vx = cvx.Variable(N_dim)  # reconstruct a signal in N_dim space
objective = cvx.Minimize(cvx.norm(vx, 1))
constraints = [A@vx == yt_sample]  # constraint in Fourier space
opt_problem = cvx.Problem(objective, constraints)
result = opt_problem.solve(verbose=True)

# Summarize optimization
print("status: {}".format(opt_problem.status))
optimal_x = vx.value.reshape(N_dim, 1)  # unsqueeze

# Number of nonzero elements in the solution (its cardinality or diversity).
nnz_l1 = (np.absolute(optimal_x) > delta).sum()
E = np.isclose(optimal_x, X_sparse)
E_support = np.isclose(optimal_x[sample_indices], X_sparse[sample_indices])
# we could computed L2 error, but the claim is 100% on the nose reconstruction...probabilistically
error_rate = 1 - (E.sum() / N_dim)
S_error_rate = 1 - (E_support.sum() / m_samples)
print('Found a feasible x in R^{} that has {} nonzeros.'.format(N_dim, nnz_l1))
print('Reconstruction error rate is %0.2f' % error_rate)
print('Support Reconstruction error rate is %0.2f' % S_error_rate)

# PLOT
plt.plot(X_sparse, '.', color='b', label='original sparse signal')
plt.plot(optimal_x, '.', color='g', label='approximation')
plt.legend()```
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We're after the problem:

$$\begin{aligned} \arg \min_{\boldsymbol{x}} \quad & {\left\| \boldsymbol{x} \right\|}_{1} \\ \text{subject to} \quad & \hat{F} \boldsymbol{x} = \boldsymbol{y} \end{aligned}$$

Where $ \hat{F} $ is a sub set of rows of a unitary matrix.
In the case of the example of Emmanuel J. Candes, Michael B. Wakin - An Introduction To Compressive Sampling it is sub sampled DFT Matrix.

The actual analysis of the example is given by Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. In this case we have 3 main parameters:

  1. The Number of Samples ($ N $) - 256 (In their code 512).
  2. The Number of Non Zero Samples ($ \left| T \right| $) - Changes from 2 to 32.
  3. The Number of Given Frequency Samples ($ \left| \Omega \right| $) - Changes from 4 to 64.

I solved the problem using my solution to How Can L1 Norm Minimization with Linear Equality Constraints (Basis Pursuit / Sparse Representation) Be Formulated as Linear Programming.

The results I get are:

enter image description here

Which are according to their numerical examples.
The code is available at my StackExchange Codes Signal Processing Q78143 GitHub Repository (Look at the SignalProcessing\Q78143 folder).

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  • $\begingroup$ thank you -- if I'm reading your code right, one of the key elements of your solution is to i) use a discrete fourier transform (not a dct as alluded to in the Rishik reference) and ii) apply equality constraints to the corresponding real and imaginary separately as in this line . $\endgroup$ Sep 24 '21 at 2:11
  • $\begingroup$ You could use the DCT matrix if you build it as an orthogonal matrix. Since it is a real transform you won’t need to do the trick I did. $\endgroup$
    – Royi
    Sep 24 '21 at 3:27

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