Compressed sensing using Douglas-Rachford

This tutorial presents a compressed sensing problem solved by the Douglas-Rachford splitting algorithm. The convex optimization problem, a term which expresses a prior on the sparsity of the solution constrained by some data fidelity, is given by

\[\min\limits_x \|x\|_1 \text{ s.t. } \|Ax-y\|_2 \leq \epsilon\]

where y are the measurements and A is the measurement matrix.

The number of necessary measurements m is computed with respect to the signal size n and the sparsity level S in order to very often perform a perfect reconstruction. See [CR07] for details.

>>> n = 900
>>> S = 45
>>> import numpy as np
>>> m = int(np.ceil(S * np.log(n)))
>>> print('Number of measurements: {}'.format(m))
Number of measurements: 307
>>> print('Compression ratio: {:3.2f}'.format(float(n) / m))
Compression ratio: 2.93

We generate a random measurement matrix A:

>>> np.random.seed(1)  # Reproducible results.
>>> A = np.random.normal(size=(m, n))

Create the S sparse signal x:

>>> x = np.zeros(n)
>>> I = np.random.permutation(n)
>>> x[I[0:S]] = np.random.normal(size=S)
>>> x = x / np.linalg.norm(x)

Generate the measured signal y:

>>> y =, x)

The first objective function to minimize is defined by

\[f_1(x) = \|x\|_1\]

which can be expressed by the toolbox L1-norm function object. It can be instantiated as follows:

>>> from pyunlocbox import functions
>>> f1 = functions.norm_l1()

The second objective function to minimize is defined by

\[f_2(x) = \iota_C(x)\]

where \(\iota_C()\) is the indicator function of the set \(C = \left\{z \in \mathbb{R}^n \mid \|Az-y\|_2 \leq \epsilon \right\}\) which is zero if \(z\) is in the set and infinite otherwise. This function can be expressed by the toolbox L2-ball function object which can be instantiated as follows:

>>> f2 = functions.proj_b2(epsilon=1e-7, y=y, A=A, tight=False,
... nu=np.linalg.norm(A, ord=2)**2)

Now that the two function objects to minimize (the L1-norm and the L2-ball) are instantiated, we can instantiate the solver object. To solve this problem, we use the Douglas-Rachford splitting algorithm which is instantiated as follows:

>>> from pyunlocbox import solvers
>>> solver = solvers.douglas_rachford(step=1e-2)

After the instantiations of the functions and solver objects, the setting of a starting point x0, the problem is solved by the toolbox solving function as follows:

>>> x0 = np.zeros(n)
>>> ret = solvers.solve([f1, f2], x0, solver, rtol=1e-4, maxit=300)
Solution found after 43 iterations:
    objective function f(sol) = 5.607407e+00
    stopping criterion: RTOL

Let’s display the results:

>>> import matplotlib.pyplot as plt
>>> _ = plt.figure()
>>> _ = plt.plot(x, 'o', label='Original')
>>> _ = plt.plot(ret['sol'], 'xr', label='Reconstructed')
>>> _ = plt.grid(True)
>>> _ = plt.title('Achieved reconstruction')
>>> _ = plt.legend(numpoints=1)
>>> _ = plt.xlabel('Signal dimension number')
>>> _ = plt.ylabel('Signal value')

The above figure shows a good reconstruction which is both sparse (thanks to the L1-norm objective) and close to the measurements (thanks to the L2-ball constraint).

Let’s display the convergence of the objective function:

>>> objective = np.array(ret['objective'])
>>> _ = plt.figure()
>>> _ = plt.semilogy(objective[:, 0], label='L1-norm objective')
>>> _ = plt.grid(True)
>>> _ = plt.title('Convergence')
>>> _ = plt.legend()
>>> _ = plt.xlabel('Iteration number')
>>> _ = plt.ylabel('Objective function value')