Nonlinear inversion with box constraints#

In this tutorial we focus on a modification of the Quadratic program with box constraints tutorial where the quadratic function is replaced by a nonlinear function. For this example we will use the well-known Rosenbrock function:

\[\mathbf{x} = \argmin_\mathbf{x} f(\mathbf{x}) \quad \text{s.t.} \quad \mathbf{x} \in \mathcal{I}_{\operatorname{Box}}\]

We will learn how to handle nonlinear functionals in convex optimization, and more specifically dive into the details of the pyproximal.proximal.Nonlinear operator. This is a template operator which must be subclassed and used for a specific functional. After doing so, we will need to implement the following three method: func and grad and optimize. As the names imply, the first method takes a model vector \(x\) as input and evaluates the functional. The second method evaluates the gradient of the functional with respect to \(x\). The third method implements an optimization routine that solves the proximal operator of \(f\), more specifically:

\[\prox_{\tau f} (\mathbf{x}) = \argmin_{\mathbf{y}} f(\mathbf{y}) + \frac{1}{2 \tau}\|\mathbf{y} - \mathbf{x}\|^2_2\]

Note that when creating the optimize method a user must use the gradient of the augmented functional which is provided by the _gradprox built-in method in pyproximal.proximal.Nonlinear class.

In this example, we will consider both the pyproximal.optimization.primal.ProximalGradient and pyproximal.optimization.primal.ADMM algorithms. The former solver will simply use the grad method whilst the second solver relies on the optimize method.

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt

import pyproximal


Let’s start by defining the class for the nonlinear functional

def rosenbrock(x, y, a=1, b=10):
    f = (a - x)**2 + b*(y - x**2)**2
    return f

def rosenbrock_grad(x, y, a=1, b=10):
    dfx = -2*(a - x) - 2*b*(y - x**2) * 2 * x
    dfy = 2*b*(y - x**2)
    return dfx, dfy

def contour_rosenbrock(x, y):
    fig, ax = plt.subplots(figsize=(12, 6))

    # Evaluate the function
    x, y = np.meshgrid(x, y)
    z = rosenbrock(x, y)

    # Plot the surface.
    surf = ax.contour(x, y, z, 200, cmap='gist_heat_r', vmin=-20, vmax=200,
    fig.colorbar(surf, shrink=0.5, aspect=10)
    return fig, ax

class Rosebrock(pyproximal.proximal.Nonlinear):
    def setup(self, a=1, b=10, alpha=1.):
        self.a, self.b = a, b
        self.alpha = alpha
    def fun(self, x):
        return np.array(rosenbrock(x[0], x[1], a=self.a, b=self.b))
    def grad(self, x):
        return np.array(rosenbrock_grad(x[0], x[1], a=self.a, b=self.b))
    def optimize(self):
        self.solhist = []
        sol = self.x0.copy()
        for iiter in range(self.niter):
            x1, x2 = sol
            dfx1, dfx2 = self._gradprox(sol, self.tau)
            x1 -= self.alpha * dfx1
            x2 -= self.alpha * dfx2
            sol = np.array([x1, x2])
        self.solhist = np.array(self.solhist)
        return sol

We can now setup the problem and solve it without constraints using a simple gradient descent with fixed-step size (of course we could choose any other solver)

niters = 500
alpha = 0.02

steps = [(0, 0), ]
for iiter in range(niters):
    x, y = steps[-1]
    dfx, dfy = rosenbrock_grad(x, y)
    x -= alpha * dfx
    y -= alpha * dfy
    steps.append((x, y))

x = np.arange(-1.5, 1.5, 0.15)
y = np.arange(-0.5, 1.5, 0.15)
nx, ny = len(x), len(y)

Let’s now define the box constraint

xbound = np.arange(-1.5, 1.5, 0.01)
ybound = np.arange(-0.5, 1.5, 0.01)
X, Y = np.meshgrid(xbound, ybound, indexing='ij')
xygrid = np.vstack((X.ravel(), Y.ravel()))

lower = 0.6
upper = 1.2
indic = (xygrid > lower) & (xygrid < upper)
indic = indic[0].reshape(xbound.size, ybound.size) & \
        indic[1].reshape(xbound.size, ybound.size)

We now solve the constrained optimization using the Proximal gradient solver

fnl = Rosebrock(niter=20, x0=np.zeros(2), warm=True)
fnl.setup(1, 10, alpha=0.02)
ind = pyproximal.proximal.Box(lower, upper)

def callback(x):

x0 = np.array([0, 0])
xhist = [x0,]
xinv_pg = pyproximal.optimization.primal.ProximalGradient(fnl, ind,
                                                          x0=x0, epsg=1.,
                                                          niter=5000, show=True,
xhist_pg = np.array(xhist)
Accelerated Proximal Gradient
Proximal operator (f): <class '__main__.Rosebrock'>
Proximal operator (g): <class 'pyproximal.proximal.Box.Box'>
tau = 0.001     beta=5.000000e-01
epsg = 1.0      niter = 5000
niterback = 100 acceleration = None

   Itn       x[0]          f           g       J=f+eps*g       tau
     1   6.00000e-01   7.360e-01   0.000e+00   7.360e-01   1.000e-03
     2   6.06560e-01   6.934e-01   0.000e+00   6.934e-01   1.000e-03
     3   6.12978e-01   6.527e-01   0.000e+00   6.527e-01   1.000e-03
     4   6.19250e-01   6.138e-01   0.000e+00   6.138e-01   1.000e-03
     5   6.25375e-01   5.768e-01   0.000e+00   5.768e-01   1.000e-03
     6   6.31350e-01   5.415e-01   0.000e+00   5.415e-01   1.000e-03
     7   6.37174e-01   5.080e-01   0.000e+00   5.080e-01   1.000e-03
     8   6.42844e-01   4.763e-01   0.000e+00   4.763e-01   1.000e-03
     9   6.48361e-01   4.463e-01   0.000e+00   4.463e-01   1.000e-03
    10   6.53722e-01   4.180e-01   0.000e+00   4.180e-01   1.000e-03
   501   8.25975e-01   3.089e-02   1.000e+00   1.031e+00   1.000e-03
  1001   8.64935e-01   1.859e-02   1.000e+00   1.019e+00   1.000e-03
  1501   8.93688e-01   1.151e-02   1.000e+00   1.012e+00   1.000e-03
  2001   9.15492e-01   7.269e-03   1.000e+00   1.007e+00   1.000e-03
  2501   9.32340e-01   4.658e-03   1.000e+00   1.005e+00   1.000e-03
  3001   9.45538e-01   3.017e-03   1.000e+00   1.003e+00   1.000e-03
  3501   9.55982e-01   1.971e-03   1.000e+00   1.002e+00   1.000e-03
  4001   9.64309e-01   1.295e-03   1.000e+00   1.001e+00   1.000e-03
  4501   9.70988e-01   8.557e-04   1.000e+00   1.001e+00   1.000e-03
  4992   9.76284e-01   5.718e-04   1.000e+00   1.001e+00   1.000e-03
  4993   9.76294e-01   5.713e-04   1.000e+00   1.001e+00   1.000e-03
  4994   9.76303e-01   5.709e-04   1.000e+00   1.001e+00   1.000e-03
  4995   9.76313e-01   5.704e-04   1.000e+00   1.001e+00   1.000e-03
  4996   9.76323e-01   5.699e-04   1.000e+00   1.001e+00   1.000e-03
  4997   9.76332e-01   5.695e-04   1.000e+00   1.001e+00   1.000e-03
  4998   9.76342e-01   5.690e-04   1.000e+00   1.001e+00   1.000e-03
  4999   9.76352e-01   5.685e-04   1.000e+00   1.001e+00   1.000e-03
  5000   9.76361e-01   5.681e-04   1.000e+00   1.001e+00   1.000e-03

Total time (s) = 0.10

And using the ADMM solver

x0 = np.array([0, 0])

xhist = [x0,]
xinv_admm = pyproximal.optimization.primal.ADMM(fnl, ind,
                                                niter=30, show=True,
xhist_admm = np.array(xhist)
Proximal operator (f): <class '__main__.Rosebrock'>
Proximal operator (g): <class 'pyproximal.proximal.Box.Box'>
tau = 1.000000e+00      niter = 30

   Itn       x[0]          f           g       J = f + g
     1   4.05651e-01   3.591e-01   0.000e+00   3.591e-01
     2   7.23299e-01   7.695e-02   0.000e+00   7.695e-02
     3   8.48264e-01   2.309e-02   1.000e+00   1.023e+00
     4   9.05255e-01   8.986e-03   1.000e+00   1.009e+00
     5   9.17660e-01   6.910e-03   1.000e+00   1.007e+00
     6   9.04521e-01   9.516e-03   1.000e+00   1.010e+00
     7   9.17031e-01   7.010e-03   1.000e+00   1.007e+00
     8   9.28365e-01   5.222e-03   1.000e+00   1.005e+00
     9   9.38010e-01   3.910e-03   1.000e+00   1.004e+00
    10   9.46245e-01   2.939e-03   1.000e+00   1.003e+00
    13   9.64619e-01   1.273e-03   1.000e+00   1.001e+00
    16   9.76484e-01   5.622e-04   1.000e+00   1.001e+00
    19   9.84273e-01   2.514e-04   1.000e+00   1.000e+00
    22   9.89439e-01   1.133e-04   1.000e+00   1.000e+00
    23   9.90746e-01   8.702e-05   1.000e+00   1.000e+00
    24   9.91889e-01   6.685e-05   1.000e+00   1.000e+00
    25   9.92889e-01   5.137e-05   1.000e+00   1.000e+00
    26   9.93763e-01   3.950e-05   1.000e+00   1.000e+00
    27   9.94528e-01   3.038e-05   1.000e+00   1.000e+00
    28   9.95194e-01   2.337e-05   1.000e+00   1.000e+00
    29   9.95771e-01   1.801e-05   1.000e+00   1.000e+00
    30   9.96261e-01   1.399e-05   1.000e+00   1.000e+00

Total time (s) = 0.01

To conclude it is important to notice that whilst we implemented a vanilla gradient descent inside the optimize method, any more advanced solver can be used (here for example we will repeat the same exercise using L-BFGS from scipy.

class Rosebrock_lbfgs(Rosebrock):
    def optimize(self):
        def callback(x):

        self.solhist = []
        sol = sp.optimize.minimize(lambda x: self._funprox(x, self.tau),
                                   jac=lambda x: self._gradprox(x, self.tau),
                                   method='L-BFGS-B', callback=callback,
        sol = sol.x

        self.solhist = np.array(self.solhist)
        return sol

fnl = Rosebrock_lbfgs(niter=20, x0=np.zeros(2), warm=True)
fnl.setup(1, 10, alpha=0.02)

x0 = np.array([0, 0])
xhist = [x0,]
xinv_admm_lbfgs = pyproximal.optimization.primal.ADMM(fnl, ind,
                                                      niter=30, show=True,
xhist_admm_lbfgs = np.array(xhist)
Proximal operator (f): <class '__main__.Rosebrock_lbfgs'>
Proximal operator (g): <class 'pyproximal.proximal.Box.Box'>
tau = 1.000000e+00      niter = 30

   Itn       x[0]          f           g       J = f + g
     1   5.56967e-01   1.985e-01   0.000e+00   1.985e-01
     2   9.17533e-01   6.890e-03   1.000e+00   1.007e+00
     3   8.86812e-01   1.318e-02   1.000e+00   1.013e+00
     4   9.05365e-01   9.178e-03   1.000e+00   1.009e+00
     5   9.34197e-01   4.406e-03   1.000e+00   1.004e+00
     6   9.53886e-01   2.163e-03   1.000e+00   1.002e+00
     7   9.67451e-01   1.077e-03   1.000e+00   1.001e+00
     8   9.76913e-01   5.418e-04   1.000e+00   1.001e+00
     9   9.83569e-01   2.744e-04   1.000e+00   1.000e+00
    10   9.88278e-01   1.396e-04   1.000e+00   1.000e+00
    13   9.95709e-01   1.871e-05   1.000e+00   1.000e+00
    16   9.98421e-01   2.534e-06   1.000e+00   1.000e+00
    19   9.99414e-01   3.483e-07   1.000e+00   1.000e+00
    22   9.99784e-01   4.760e-08   1.000e+00   1.000e+00
    23   9.99870e-01   1.687e-08   1.000e+00   1.000e+00
    24   9.99873e-01   1.661e-08   1.000e+00   1.000e+00
    25   9.99872e-01   1.635e-08   1.000e+00   1.000e+00
    26   9.99875e-01   1.609e-08   1.000e+00   1.000e+00
    27   9.99874e-01   1.581e-08   1.000e+00   1.000e+00
    28   9.99877e-01   1.554e-08   1.000e+00   1.000e+00
    29   9.99877e-01   1.517e-08   1.000e+00   1.000e+00
    30   9.99879e-01   1.484e-08   1.000e+00   1.000e+00

Total time (s) = 0.02

Finally let’s compare the results.

fig, ax = contour_rosenbrock(x, y)
steps = np.array(steps)
ax.plot(steps[:, 0], steps[:, 1], '.-k', lw=2, ms=20, alpha=0.4)
ax.contour(X, Y, indic, colors='k')
ax.scatter(1, 1, c='k', s=300)
ax.plot(xhist_pg[:, 0], xhist_pg[:, 1], '.-b', ms=20, lw=2, label='PG')
ax.plot(xhist_admm[:, 0], xhist_admm[:, 1], '.-g', ms=20, lw=2, label='ADMM')
ax.plot(xhist_admm_lbfgs[:, 0], xhist_admm_lbfgs[:, 1], '.-m', ms=20, lw=2,
        label='ADMM with LBFGS')
ax.set_title('Rosenbrock optimization')
ax.set_xlim(x[0], x[-1])
ax.set_ylim(y[0], y[-1])
Rosenbrock optimization

Total running time of the script: (0 minutes 0.590 seconds)

Gallery generated by Sphinx-Gallery