Performance of Sparse Recovery Using L1 Minimization

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This tour explores theoritical garantees for the performance of recovery using $\ell^1$ minimization.

In [1]:
from __future__ import division

import numpy as np
import scipy as scp
import pylab as pyl
import matplotlib.pyplot as plt

from nt_toolbox.general import *
from nt_toolbox.signal import *

import warnings

%matplotlib inline
%load_ext autoreload
%autoreload 2

Sparse $\ell^1$ Recovery

We consider the inverse problem of estimating an unknown signal $x_0 \in \RR^N$ from noisy measurements $y=\Phi x_0 + w \in \RR^P$ where $\Phi \in \RR^{P \times N}$ is a measurement matrix with $P \leq N$, and $w$ is some noise.

This tour is focused on recovery using $\ell^1$ minimization $$ x^\star \in \uargmin{x \in \RR^N} \frac{1}{2}\norm{y-\Phi x}^2 + \la \norm{x}_1. $$

Where there is no noise, we consider the problem $ \Pp(y) $ $$ x^\star \in \uargmin{\Phi x = y} \norm{x}_1. $$

We are not concerned here about the actual way to solve this convex problem (see the other numerical tours on sparse regularization) but rather on the theoritical analysis of wether $x^\star$ is close to $x_0$.

More precisely, we consider the following three key properties

  • Noiseless identifiability: $x_0$ is the unique solution of $ \Pp(y) $ for $y=\Phi x_0$.
  • Robustess to small noise: one has $\norm{x^\star - x_0} = O(\norm{w})$ for $y=\Phi x_0+w$ if $\norm{w}$ is smaller than an arbitrary small constant that depends on $x_0$ if $\la$ is well chosen according to $\norm{w}$.
  • Robustess to bounded noise: same as above, but $\norm{w}$ can be arbitrary.

Note that noise robustness implies identifiability, but the converse is not true in general.

Coherence Criteria

The simplest criteria for identifiality are based on the coherence of the matrix $\Phi$ and depends only on the sparsity $\norm{x_0}_0$ of the original signal. This criteria is thus not very precise and gives very pessimistic bounds.

The coherence of the matrix $\Phi = ( \phi_i )_{i=1}^N \in \RR^{P \times N}$ with unit norm colum $\norm{\phi_i}=1$ is $$ \mu(\Phi) = \umax{i \neq j} \abs{\dotp{\phi_i}{\phi_j}}. $$

Compute the correlation matrix (remove the diagonal of 1's).

In [2]:
remove_diag = lambda C: C-np.diag(np.diag(C))
Correlation = lambda Phi: remove_diag(abs(,Phi)))

Compute the coherence $\mu(\Phi)$.

In [3]:
mu = lambda Phi: np.max(Correlation(Phi))

The condition $$ \norm{x_0}_0 < \frac{1}{2}\pa{1 + \frac{1}{\mu(\Phi)}} $$ implies that $x_0$ is identifiable, and also implies to robustess to small and bounded noise.

Equivalently, this condition can be written as $\text{Coh}(\norm{x_0}_0)<1$ where $$ \text{Coh}(k) = \frac{k \mu(\Phi)}{ 1 - (k-1)\mu(\Phi) } $$

In [4]:
Coh = lambda Phi, k: (k*mu(Phi))/(1 - (k-1)*mu(Phi))

Generate a matrix with random unit columns in $\RR^P$.

In [5]:
from numpy import random

normalize = lambda Phi:  Phi/np.tile(np.sqrt(np.sum(Phi**2,0)), (np.shape(Phi)[0],1))
PhiRand = lambda P, N: normalize(random.randn(P, N))
Phi = PhiRand(250, 1000)

Compute the coherence and the maximum possible sparsity to ensure recovery using the coherence bound.

In [6]:
c = mu(Phi)
print("Coherence:    %.2f" %c)
print("Sparsity max: %d" %np.floor(1/2*(1 + 1/c)))
Coherence:    0.30
Sparsity max: 2

Exercise 1

Display how the average coherence of a random matrix decays with the redundancy $\eta = P/N$ of the matrix $\Phi$. Can you derive an empirical law between $P$ and the maximal sparsity?

In [7]:
run -i nt_solutions/sparsity_6_l1_recovery/exo1
In [8]:
## Insert your code here.

Support and Sign-based Criteria

In the following we will consider the support $$ \text{supp}(x_0) = \enscond{i}{x_0(i) \neq 0} $$ of the vector $x_0$. The co-support is its complementary $I^c$.

In [9]:
supp   = lambda s: np.where(abs(s) > 1e-5)[0]
cosupp = lambda s: np.where(abs(s) < 1e-5)[0]

Given some support $ I \subset \{0,\ldots,N-1\} $, we will denote as $ \Phi = (\phi_m)_{m \in I} \in \RR^{N \times \abs{I}}$ the sub-matrix extracted from $\Phi$ using the columns indexed by $I$.

J.J. Fuchs introduces a criteria $F$ for identifiability that depends on the sign of $x_0$.

J.J. Fuchs. Recovery of exact sparse representations in the presence of bounded noise. IEEE Trans. Inform. Theory, 51(10), p. 3601-3608, 2005

Under the condition that $\Phi_I$ has full rank, the $F$ measure of a sign vector $s \in \{+1,0,-1\}^N$ with $\text{supp}(s)=I$ reads $$ \text{F}(s) = \norm{ \Psi_I s_I }_\infty \qwhereq \Psi_I = \Phi_{I^c}^* \Phi_I^{+,*} $$ where $ A^+ = (A^* A)^{-1} A^* $ is the pseudo inverse of a matrix $A$.

The condition $$ \text{F}(\text{sign}(x_0))<1 $$ implies that $x_0$ is identifiable, and also implies to robustess to small noise. It does not however imply robustess to a bounded noise.

Compute $\Psi_I$ matrix.

In [10]:
from numpy import linalg

PsiI = lambda Phi,I:[:,np.setdiff1d(np.arange(0,np.shape(Phi)[1]), I)]),np.transpose(linalg.pinv(Phi[:,I])))

Compute $\text{F}(s)$.

In [11]:
F = lambda Phi,s: linalg.norm(, supp(s)),s[supp(s)]), np.float("inf"))

The Exact Recovery Criterion (ERC) of a support $I$, introduced by Tropp in

J. A. Tropp. Just relax: Convex programming methods for identifying sparse signals. IEEE Trans. Inform. Theory, vol. 52, num. 3, pp. 1030-1051, Mar. 2006.

Under the condition that $\Phi_I$ has full rank, this condition reads $$ \text{ERC}(I) = \norm{\Psi_{I}}_{\infty,\infty} = \umax{j \in I^c} \norm{ \Phi_I^+ \phi_j }_1. $$ where $\norm{A}_{\infty,\infty}$ is the $\ell^\infty-\ell^\infty$ operator norm of a matrix $A$.

In [12]:
erc = lambda Phi, I: linalg.norm(PsiI(Phi, I), np.float("inf"))

The condition $$ \text{ERC}(\text{supp}(x_0))<1 $$ implies that $x_0$ is identifiable, and also implies to robustess to small and bounded noise.

One can prove that the ERC is the maximum of the F criterion for all signs of the given support $$ \text{ERC}(I) = \umax{ s, \text{supp}(s) \subset I } \text{F}(s). $$

The weak-ERC is an approximation of the ERC using only the correlation matrix $$ \text{w-ERC}(I) = \frac{ \umax{j \in I^c} \sum_{i \in I} \abs{\dotp{\phi_i}{\phi_j}} }{ 1-\umax{j \in I} \sum_{i \neq j \in I} \abs{\dotp{\phi_i}{\phi_j}} }$$

In [13]:
g = lambda C,I: np.sum(C[:,I], 1)
werc_g = lambda g,I,J: np.max(g[J])/(1-np.max(g[I]))
werc = lambda Phi,I: werc_g(g(Correlation(Phi), I), I, np.setdiff1d(np.arange(0,np.shape(Phi)[1]), I))

One has, if $\text{w-ERC}(I)>0$, for $ I = \text{supp}(s) $, $$ \text{F}(s) \leq \text{ERC}(I) \leq \text{w-ERC}(I) \leq \text{Coh}(\abs{I}). $$

This shows in particular that the condition $$ \text{w-ERC}(\text{supp}(x_0))<1 $$ implies identifiability and robustess to small and bounded noise.

Exercise 2

Show that this inequality holds on a given matrix. What can you conclude about the sharpness of these criteria ?

In [14]:
run -i nt_solutions/sparsity_6_l1_recovery/exo2
N = 2000, P = 1990, |I| = 6
F(s)     = 0.21
ERC(I)   = 0.23
w-ERC(s) = 0.26
Coh(|s|) = 1.47
In [15]:
## Insert your code here.

N = 2000
P = N-10
Phi = PhiRand(N, P)
s = np.zeros(N)
s[:6] = 1
I = supp(s)
k = len(I)

print("N = %d, P = %d, |I| = %d" %(N,P,k))
print("F(s)     = %.2f"  %F(Phi, s))
print("ERC(I)   = %.2f"  %erc(Phi, I))
print("w-ERC(s) = %.2f" %werc(Phi, I))
print("Coh(|s|) = %.2f" %Coh(Phi, k))
N = 2000, P = 1990, |I| = 6
F(s)     = 0.20
ERC(I)   = 0.24
w-ERC(s) = 0.26
Coh(|s|) = 1.44

Exercise 3

For a given matrix $\Phi$ generated using PhiRand, draw as a function of the sparsity $k$ the probability that a random sign vector $s$ of sparsity $\norm{s}_0=k$ satisfies the conditions $\text{F}(x_0)<1$, $\text{ERC}(x_0)<1$ and $\text{w-ERC}(x_0)<1$

In [16]:
run -i nt_solutions/sparsity_6_l1_recovery/exo3
In [17]:
## Insert your code here.

N = 600
P = N/2
Phi = PhiRand(P, N)
klist = np.round(np.linspace(1, P/7., 20))
ntrials = 60
proba = np.zeros([len(klist),3])

for i in range(len(klist)):
    proba[i,:3] = 0
    k = klist[i]
    for j in range(ntrials):
        s = np.zeros(N)
        I = random.permutation(N)
        I = I[:k]
        s[I] = np.sign(random.randn(k, 1))
        proba[i, 0] = proba[i, 0] + (F(Phi, s) < 1)
        proba[i, 1] = proba[i, 1] + (erc(Phi, I) < 1)
        proba[i, 2] = proba[i, 2] + (werc(Phi, I) > 0)*(werc(Phi, I) < 1)
plt.figure(figsize = (8,5))
plt.plot(klist, proba/ntrials, linewidth=2)
plt.legend(['F <1', 'ERC <1', 'w-ERC <1'])
plt.title("N = %d, P = %d" %(N,P))

Restricted Isometry Criteria

The restricted isometry constants $\de_k^1,\de_k^2$ of a matrix $\Phi$ are the smallest $\de^1,\de^2$ that satisfy $$ \forall x \in \RR^N, \quad \norm{x}_0 \leq k \qarrq (1-\de^1)\norm{x}^2 \leq \norm{\Phi x}^2 \leq (1+\de^2)\norm{x}^2. $$

E. Candes shows in

E. J. Cand s. The restricted isometry property and its implications for compressed sensing. Compte Rendus de l'Academie des Sciences, Paris, Serie I, 346 589-592

that if $$ \de_{2k} \leq \sqrt{2}-1 ,$$ then $\norm{x_0} \leq k$ implies identifiability as well as robustness to small and bounded noise.

The stability constant $\la^1(A), \la^2(A)$ of a matrix $A = \Phi_I$ extracted from $\Phi$ is the smallest $\tilde \la_1,\tilde \la_2$ such that $$ \forall \al \in \RR^{\abs{I}}, \quad (1-\tilde\la_1)\norm{\al}^2 \leq \norm{A \al}^2 \leq (1+\tilde \la_2)\norm{\al}^2. $$

These constants $\la^1(A), \la^2(A)$ are easily computed from the largest and smallest eigenvalues of $A^* A \in \RR^{\abs{I} \times \abs{I}}$

In [18]:
minmax = lambda v: (1-np.min(v), max(v)-1)
ric = lambda A: minmax(linalg.eig(,A))[0])

The restricted isometry constant of $\Phi$ are computed as the largest stability constants of extracted matrices $$ \de^\ell_k = \umax{ \abs{I}=k } \la^\ell( \Phi_I ). $$

The eigenvalues of $\Phi$ are essentially contained in the interval $ [a,b] $ where $a=(1-\sqrt{\be})^2$ and $b=(1+\sqrt{\be})^2$ with $\beta = k/P$ More precisely, as $k=\be P$ tends to infinity, the distribution of the eigenvalues tends to the Marcenko-Pastur law $ f_\be(\la) = \frac{1}{2\pi \be \la}\sqrt{ (\la-b)^+ (a-\la)^+ }. $

Exercise 4

Display, for an increasing value of $k$ the histogram of repartition of the eigenvalues $A^* A$ where $A$ is a Gaussian matrix of size $(P,k)$ and variance $1/P$. For this, accumulate the eigenvalues for many realizations of $A$.

In [19]:
run -i nt_solutions/sparsity_6_l1_recovery/exo4
In [20]:
## Insert your code here.

Exercise 5

Estimate numerically lower bound on $\de_k^1,\de_k^2$ by Monte-Carlo sampling of sub-matrices.

In [21]:
run -i nt_solutions/sparsity_6_l1_recovery/exo5
In [22]:
## Insert your code here.

Sparse Spikes Deconvolution

We now consider a convolution dictionary $ \Phi $. Such a dictionary is used with sparse regulariz

Second derivative of Gaussian kernel $g$ with a given variance $\si^2$.

In [23]:
sigma = 6
g = lambda x: (1-x**2/sigma**2)*np.exp(-x**2/(2*sigma**2))

Create a matrix $\Phi$ so that $\Phi x = g \star x$ with periodic boundary conditions.

In [24]:
P = 1024
[Y, X] = np.meshgrid(np.arange(0,P), np.arange(0,P))
Phi = normalize(g((X - Y + P/2.)%P-P/2.))

To improve the conditionning of the dictionary, we sub-sample its atoms, so that $ P = \eta N > N $.

In [25]:
eta = 2
N = P/eta
Phi = Phi[:,::eta]

Plot the correlation function associated to the filter. Can you determine the value of the coherence $\mu(\Phi)$?

In [26]:
c =,Phi)
c = np.abs(c[:,np.shape(c)[1]/2])

plt.plot(c[len(c)/2 - 50:len(c)/2 + 50], '.-')

Create a data a sparse $x_0$ with two diracs of opposite signes with spacing $d$.

In [27]:
twosparse = lambda d: np.roll(np.concatenate(([1.], np.zeros(d), [-1.], np.zeros(N-d-2))), int(N/2 - d/2))

Display $x_0$ and $\Phi x_0$.

In [28]:
x0 = twosparse(50)

plt.subplot(2, 1, 1)
plt.plot(x0, 'r', linewidth=2)
plt.subplot(2, 1, 2)
plt.plot(Phi*x0, 'b', linewidth=2)

Exercise 6

Plot the evolution of the criteria F, ERC and Coh as a function of $d$. Do the same plot for other signs patterns for $x_0$. Do the same plot for a Dirac comb with a varying spacing $d$.

In [29]:
run -i nt_solutions/sparsity_6_l1_recovery/exo6
In [30]:
## Insert your code here.