def __init__(self, problem, inner_product="L2", parameters=None):
"""
Create a new OptizelleSolver.
The argument inner_product specifies the inner product to be used for
the control space.
To set optizelle-specific options, do e.g.
solver = OptizelleSolver(problem, parameters={'maximum_iterations': 100,
'optizelle_parameters': {'krylov_iter_max': 100}})
"""
try:
import Optizelle
import Optizelle.Unconstrained.State
import Optizelle.Unconstrained.Functions
import Optizelle.Unconstrained.Algorithms
import Optizelle.Constrained.State
import Optizelle.Constrained.Functions
import Optizelle.Constrained.Algorithms
import Optizelle.EqualityConstrained.State
import Optizelle.EqualityConstrained.Functions
import Optizelle.EqualityConstrained.Algorithms
import Optizelle.InequalityConstrained.State
import Optizelle.InequalityConstrained.Functions
import Optizelle.InequalityConstrained.Algorithms
except ImportError:
print("Could not import Optizelle.")
raise
DolfinVectorSpace.inner_product = inner_product
OptimizationSolver.__init__(self, problem, parameters)
self.__build_optizelle_state()
def __init__(self, problem, parameters=None):
"""
Create a new OptizelleSolver.
To set optizelle-specific options, do e.g.
solver = OptizelleSolver(problem, parameters={'maximum_iterations': 100,
'optizelle_parameters': {'krylov_iter_max': 100}})
"""
try:
import Optizelle
import Optizelle.Unconstrained.State
import Optizelle.Unconstrained.Functions
import Optizelle.Unconstrained.Algorithms
import Optizelle.Constrained.State
import Optizelle.Constrained.Functions
import Optizelle.Constrained.Algorithms
import Optizelle.EqualityConstrained.State
import Optizelle.EqualityConstrained.Functions
import Optizelle.EqualityConstrained.Algorithms
import Optizelle.InequalityConstrained.State
import Optizelle.InequalityConstrained.Functions
import Optizelle.InequalityConstrained.Algorithms
except ImportError:
print("Could not import Optizelle.")
raise
OptimizationSolver.__init__(self, problem, parameters)
self.__build_optizelle_state()
def __init__(self, problem, parameters=None):
"""
Create a new OptizelleSolver.
To set optizelle-specific options, do e.g.
solver = OptizelleSolver(problem, parameters={'maximum_iterations': 100,
'optizelle_parameters': {'krylov_iter_max': 100}})
"""
try:
import Optizelle
import Optizelle.Unconstrained.State
import Optizelle.Unconstrained.Functions
import Optizelle.Unconstrained.Algorithms
import Optizelle.Constrained.State
import Optizelle.Constrained.Functions
import Optizelle.Constrained.Algorithms
import Optizelle.EqualityConstrained.State
import Optizelle.EqualityConstrained.Functions
import Optizelle.EqualityConstrained.Algorithms
import Optizelle.InequalityConstrained.State
import Optizelle.InequalityConstrained.Functions
import Optizelle.InequalityConstrained.Algorithms
except ImportError:
print("Could not import Optizelle.")
raise
OptimizationSolver.__init__(self, problem, parameters)
self.__build_optizelle_state()
def __init__(self, problem, parameters=None):
try:
import pyipopt
except ImportError:
print("You need to install pyipopt. It is recommended to install IPOPT with HSL support!")
raise
OptimizationSolver.__init__(self, problem, parameters)
self.__build_pyipopt_problem()
self.__set_parameters()
def solve(self):
# split to YUV channels
y_channel, u_channel, v_channel = self.__get_yuv_channels_from_matrices()
has_hints = abs(self.__grayscale_bgr_matrix - self.__marked_bgr_matrix).sum(2) > 0.01
wrs = self.__compute_weights(has_hints, y_channel)
# perform optimization
optimization_solver = OptimizationSolver(wrs, has_hints)
new_u, new_v = optimization_solver.optimize(u_channel, v_channel)
return yuv_channels_to_bgr_image(y_channel, new_u, new_v)
def __init__(self, problem, parameters=None, riesz_map=None, prefix=""):
try:
from petsc4py import PETSc
except:
raise Exception, "Could not find petsc4py. Please install it."
try:
TAO = PETSc.TAO
except:
raise Exception, "Your petsc4py version does not support TAO. Please upgrade to petsc4py >= 3.5."
self.PETSc = PETSc
# Use PETSc forms
if riesz_map is not None:
# Handle the case where the user supplied riesz_maps.L2(V)
if hasattr(riesz_map, "assemble"):
riesz_map = riesz_map.assemble()
self.riesz_map = as_backend_type(riesz_map).mat()
else:
self.riesz_map = None
if len(prefix) > 0 and prefix[-1] != "_":
prefix += "_"
self.prefix = prefix
OptimizationSolver.__init__(self, problem, parameters)
self.tao = PETSc.TAO().create(PETSc.COMM_WORLD)
self.__build_app_context()
self.__set_parameters()
self.__build_tao()
