Source code for sporco.fista.bpdn
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2019 by Brendt Wohlberg <brendt@ieee.org>
# Cristina Garcia-Cardona <cgarciac@lanl.gov>
# All rights reserved. BSD 3-clause License.
# This file is part of the SPORCO package. Details of the copyright
# and user license can be found in the 'LICENSE.txt' file distributed
# with the package.
"""Classes for FISTA algorithm for the BPDN problem"""
from __future__ import division, absolute_import, print_function
import copy
import numpy as np
from sporco.util import u
from sporco.prox import prox_l1
from sporco.fista import fista
__author__ = """Cristina Garcia-Cardona <cgarciac@lanl.gov>"""
[docs]class BPDN(fista.FISTA):
r"""
Class for FISTA algorithm for the Basis Pursuit DeNoising (BPDN)
:cite:`chen-1998-atomic` problem.
|
.. inheritance-diagram:: BPDN
:parts: 2
|
The problem form is
.. math::
\mathrm{argmin}_\mathbf{x} \; (1/2) \| D \mathbf{x} - \mathbf{s}
\|_2^2 + \lambda \| \mathbf{x} \|_1
where :math:`\mathbf{s}` is the input vector/matrix, :math:`D` is
the dictionary, and :math:`\mathbf{x}` is the sparse representation.
After termination of the :meth:`solve` method, attribute
:attr:`itstat` is a list of tuples representing statistics of each
iteration. The fields of the named tuple ``IterationStats`` are:
``Iter`` : Iteration number
``ObjFun`` : Objective function value
``DFid`` : Value of data fidelity term :math:`(1/2) \| D
\mathbf{x} - \mathbf{s} \|_2^2`
``RegL1`` : Value of regularisation term :math:`\lambda \|
\mathbf{x} \|_1`
``Rsdl`` : Residual
``L`` : Inverse of gradient step parameter
``Time`` : Cumulative run time
"""
[docs] class Options(fista.FISTA.Options):
r"""BPDN algorithm options
Options include all of those defined in
:class:`.fista.FISTA.Options`, together with
additional options:
``L1Weight`` : An array of weights for the :math:`\ell_1`
norm. The array shape must be such that the array is
compatible for multiplication with the X/Y variables. If this
option is defined, the regularization term is :math:`\lambda
\| \mathbf{w} \odot \mathbf{x} \|_1` where
:math:`\mathbf{w}` denotes the weighting array.
"""
defaults = copy.deepcopy(fista.FISTADFT.Options.defaults)
defaults.update({'L1Weight': 1.0})
defaults.update({'L': 500.0})
def __init__(self, opt=None):
"""
Parameters
----------
opt : dict or None, optional (default None)
BPDN algorithm options
"""
if opt is None:
opt = {}
fista.FISTA.Options.__init__(self, opt)
def __setitem__(self, key, value):
"""Set options."""
fista.FISTA.Options.__setitem__(self, key, value)
itstat_fields_objfn = ('ObjFun', 'DFid', 'RegL1')
hdrtxt_objfn = ('Fnc', 'DFid', u('Regℓ1'))
hdrval_objfun = {'Fnc': 'ObjFun', 'DFid': 'DFid', u('Regℓ1'): 'RegL1'}
def __init__(self, D, S, lmbda=None, opt=None):
"""
Parameters
----------
D : array_like
Dictionary array (2d)
S : array_like
Signal array (1d or 2d)
lmbda : float
Regularisation parameter
opt : :class:`BPDN.Options` object
Algorithm options
"""
# Set default options if none specified
if opt is None:
opt = BPDN.Options()
# Set dtype attribute based on S.dtype and opt['DataType']
self.set_dtype(opt, S.dtype)
# Set default lambda value if not specified
if lmbda is None:
DTS = D.T.dot(S)
lmbda = 0.1 * abs(DTS).max()
# Set l1 term scaling and weight array
self.lmbda = self.dtype.type(lmbda)
self.wl1 = np.asarray(opt['L1Weight'], dtype=self.dtype)
# Call parent class __init__
Nc = D.shape[1]
Nm = S.shape[1]
xshape = (Nc, Nm)
super(BPDN, self).__init__(xshape, S.dtype, opt)
self.S = np.asarray(S, dtype=self.dtype)
self.store_prev()
self.Y = self.X.copy()
self.Yprv = self.Y.copy() + 1e5
self.setdict(D)
[docs] def setdict(self, D):
"""Set dictionary array."""
self.D = np.asarray(D, dtype=self.dtype)
[docs] def getcoef(self):
"""Get final coefficient array."""
return self.X
[docs] def eval_grad(self):
"""Compute gradient in spatial domain for variable Y."""
# Compute D^T(D Y - S)
return self.D.T.dot(self.D.dot(self.Y) - self.S)
[docs] def eval_proxop(self, V):
"""Compute proximal operator of :math:`g`."""
return np.asarray(prox_l1(V, (self.lmbda / self.L) * self.wl1),
dtype=self.dtype)
[docs] def eval_objfn(self):
"""Compute components of objective function as well as total
contribution to objective function.
"""
dfd = self.obfn_f()
reg = self.obfn_reg()
obj = dfd + reg[0]
return (obj, dfd) + reg[1:]
[docs] def obfn_reg(self):
"""Compute regularisation term and contribution to objective
function.
"""
rl1 = np.linalg.norm((self.wl1 * self.X).ravel(), 1)
return (self.lmbda * rl1, rl1)
[docs] def obfn_f(self, X=None):
r"""Compute data fidelity term :math:`(1/2) \| D \mathbf{x} -
\mathbf{s} \|_2^2`.
"""
if X is None:
X = self.X
return 0.5 * np.linalg.norm((self.D.dot(X) - self.S).ravel())**2
[docs] def reconstruct(self, X=None):
"""Reconstruct representation."""
if X is None:
X = self.X
return self.D.dot(self.X)