def prox(self, w, index, factor=1.0, eps=consts.TOLERANCE, max_iter=100):
"""The proximal operator of the non-differentiable part of the
function with the given index.
From the interface "MultiblockProximalOperator".
Parameters
----------
w : list of numpy arrays
The parameter vectors at which to compute the proximal operator.
index : int
Non-negative integer. The variable for which to compute the
proximal operator.
factor : float
Positive float. A factor by which the Lagrange multiplier is
scaled. This is usually the step size.
"""
prox = self._p[index]
proj = self._c[index]
# We have no penalties with proximal operators and no constraints:
if len(prox) == 0 and len(proj) == 0:
prox_w = w[index] # Do nothing!
# There is one proximal operator and no constraints:
elif len(prox) == 1 and len(proj) == 0:
prox_w = prox[0].prox(w[index],
factor=factor,
eps=consts.TOLERANCE,
max_iter=100)
# There are two proximal operators, and no constraints:
elif len(prox) == 2 and len(proj) == 0:
from parsimony.algorithms.proximal import DykstrasProximalAlgorithm
prox_combo = DykstrasProximalAlgorithm(eps=eps, max_iter=max_iter)
prox_w = prox_combo.run(prox, w[index], factor=factor)
# There are no proximal operators, but one or two constraints:
elif len(prox) == 0 and (len(proj) == 1 or len(proj) == 2):
prox_w = self.proj(w, index, eps=eps, max_iter=max_iter)
# There are at least one proximal operator and at least one constraint:
else:
from parsimony.algorithms.proximal \
import ParallelDykstrasProximalAlgorithm
combo = ParallelDykstrasProximalAlgorithm(eps=eps,
max_iter=max_iter,
min_iter=1)
prox_w = combo.run(w[index], prox=prox, proj=proj, factor=factor)
return prox_w
def prox(self, w, index, factor=1.0, eps=consts.TOLERANCE, max_iter=100):
"""The proximal operator of the non-differentiable part of the
function with the given index.
From the interface "MultiblockProximalOperator".
Parameters
----------
w : list of numpy arrays
The parameter vectors at which to compute the proximal operator.
index : int
Non-negative integer. The variable for which to compute the
proximal operator.
factor : float
Positive float. A factor by which the Lagrange multiplier is
scaled. This is usually the step size.
"""
prox = self._p[index]
proj = self._c[index]
# We have no penalties with proximal operators and no constraints:
if len(prox) == 0 and len(proj) == 0:
prox_w = w[index] # Do nothing!
# There is one proximal operator and no constraints:
elif len(prox) == 1 and len(proj) == 0:
prox_w = prox[0].prox(w[index], factor=factor,
eps=consts.TOLERANCE, max_iter=100)
# There are two proximal operators, and no constraints:
elif len(prox) == 2 and len(proj) == 0:
from parsimony.algorithms.proximal import DykstrasProximalAlgorithm
prox_combo = DykstrasProximalAlgorithm(eps=eps, max_iter=max_iter)
prox_w = prox_combo.run(prox, w[index], factor=factor)
# There are no proximal operators, but one or two constraints:
elif len(prox) == 0 and (len(proj) == 1 or len(proj) == 2):
prox_w = self.proj(w, index, eps=eps, max_iter=max_iter)
# There are at least one proximal operator and at least one constraint:
else:
from parsimony.algorithms.proximal \
import ParallelDykstrasProximalAlgorithm
combo = ParallelDykstrasProximalAlgorithm(eps=eps,
max_iter=max_iter,
min_iter=1)
prox_w = combo.run(w[index], prox=prox, proj=proj, factor=factor)
return prox_w
def prox(self, w, index, factor=1.0, eps=consts.TOLERANCE, max_iter=100):
"""The proximal operator of the non-differentiable part of the
function with the given index.
From the interface "MultiblockProximalOperator".
Parameters
----------
w : List of numpy arrays. The weight vectors.
index : Non-negative integer. Which variable the step is for.
factor : Positive float. A factor by which the Lagrange multiplier is
scaled. This is usually the step size.
"""
prox = self._prox[index]
proj = self._c[index]
if len(prox) == 0 and len(proj) == 0:
prox_w = w[index]
elif len(prox) == 1 and len(proj) == 0:
prox_w = prox[0].prox(w[index])
elif len(prox) == 0 and (len(proj) == 1 or len(proj) == 2):
prox_w = self.proj(w, index, eps=eps, max_iter=max_iter)
else:
from parsimony.algorithms.proximal \
import ParallelDykstrasProximalAlgorithm
combo = ParallelDykstrasProximalAlgorithm(output=False,
eps=eps,
max_iter=max_iter,
min_iter=1)
prox_w = combo.run(w[index], prox=prox, proj=proj, factor=factor)
return prox_w
def prox(self, w, index, factor=1.0, eps=consts.TOLERANCE, max_iter=100):
"""The proximal operator of the non-differentiable part of the
function with the given index.
From the interface "MultiblockProximalOperator".
Parameters
----------
w : List of numpy arrays. The weight vectors.
index : Non-negative integer. Which variable the step is for.
factor : Positive float. A factor by which the Lagrange multiplier is
scaled. This is usually the step size.
"""
prox = self._prox[index]
proj = self._c[index]
if len(prox) == 0 and len(proj) == 0:
prox_w = w[index]
elif len(prox) == 1 and len(proj) == 0:
prox_w = prox[0].prox(w[index])
elif len(prox) == 0 and (len(proj) == 1 or len(proj) == 2):
prox_w = self.proj(w, index, eps=eps, max_iter=max_iter)
else:
from parsimony.algorithms.proximal \
import ParallelDykstrasProximalAlgorithm
combo = ParallelDykstrasProximalAlgorithm(eps=eps,
max_iter=max_iter,
min_iter=1)
prox_w = combo.run(w[index], prox=prox, proj=proj, factor=factor)
return prox_w
