def init_problem(prox_fns, implem, scale, try_split, merge, absorb):
"""
Initializes a problem.
:param prox_fns: Proxable functions which define the problem.
:type prox_fns: List
:param implem: Implementation modus ("halide" or "numpy")
:type implem: String
:param scale: If the problem should be scaled
:type scale: Bool
:param try_split: If a problem split should be tried
:type try_split: Bool
:param merge: If problem should be merged
:type merge: Bool
:param absorb: If linear operators should be absorbed
:type absorb: Bool
:returns: Initialized problem
:rtype: proximal.Problem
"""
prob = px.Problem(prox_fns,
implem=Impl[implem],
scale=scale,
try_split=try_split,
merge=merge,
absorb=absorb)
return prob
# Create ODL operator for the Laplacian
laplacian = odl.Laplacian(space)
# Create right hand side
phantom = odl.phantom.shepp_logan(space, modified=True)
phantom.show('original image')
rhs = laplacian(phantom)
rhs += odl.phantom.white_noise(space) * np.std(rhs) * 0.1
rhs.show('rhs')
# Convert laplacian to ProxImaL operator
proximal_lang_laplacian = odl.as_proximal_lang_operator(laplacian)
# Convert to array
rhs_arr = rhs.asarray()
# Set up optimization problem
x = proximal.Variable(space.shape)
funcs = [
10 * proximal.sum_squares(proximal_lang_laplacian(x) - rhs_arr),
proximal.norm1(proximal.grad(x))
]
# Solve the problem using ProxImaL
prob = proximal.Problem(funcs)
prob.solve(verbose=True)
# Convert back to odl and display result
result_odl = space.element(x.value)
result_odl.show('result from ProxImaL')