def gradient(self):
grad_df = self.data_fit.gradient
grad_of = self.of_constr.gradient
if self.aug_lagr is True:
grad_al = self.aug_lagr_term.gradient
return BroadcastOperator(*[
grad_df[i] + grad_of[i] + grad_al[i] for i in range(2 * self.N)
])
else:
return BroadcastOperator(
*[grad_df[i] + grad_of[i] for i in range(2 * self.N)])
def _reconstruct(self, observation, out):
observation = self.observation_space.element(observation)
out_ = out
if out not in self.reco_space:
out_ = self.reco_space.zero()
out_[:] = self.x0
gradient = Gradient(self.op.domain)
L = BroadcastOperator(self.op, gradient)
f = ZeroFunctional(self.op.domain)
l2_norm = L2NormSquared(self.op.range).translated(observation)
l1_norm = self.lam * L1Norm(gradient.range)
g = SeparableSum(l2_norm, l1_norm)
op_norm = 1.1 * power_method_opnorm(L, maxiter=20)
sigma = self.tau * op_norm**2
admm.admm_linearized(out_,
f,
g,
L,
self.tau,
sigma,
self.niter,
callback=self.callback)
if out not in self.reco_space:
out[:] = out_
return out
def _reconstruct(self, observation, out):
observation = self.observation_space.element(observation)
out[:] = self.x0
gradient = Gradient(self.op.domain)
L = BroadcastOperator(self.op, gradient)
f = ZeroFunctional(self.op.domain)
l2_norm = L2NormSquared(self.op.range).translated(observation)
l1_norm = self.lam * L1Norm(gradient.range)
g = [l2_norm, l1_norm]
tau, sigma = douglas_rachford.douglas_rachford_pd_stepsize(L)
douglas_rachford.douglas_rachford_pd(out,
f,
g,
L,
self.niter,
tau,
sigma,
callback=self.callback)
return out
def _reconstruct(self, observation, out):
observation = self.observation_space.element(observation)
out[:] = self.x0
gradient = Gradient(self.op.domain)
L = BroadcastOperator(self.op, gradient)
f = ZeroFunctional(self.op.domain)
l2_norm = L2NormSquared(self.op.range).translated(observation)
l1_norm = self.lam * L1Norm(gradient.range)
g = SeparableSum(l2_norm, l1_norm)
tau, sigma = primal_dual_hybrid_gradient.pdhg_stepsize(L)
primal_dual_hybrid_gradient.pdhg(out,
f,
g,
L,
self.niter,
tau,
sigma,
callback=self.callback)
return out
def gradient(self):
return BroadcastOperator(
*[self.partial_gradient(i) for i in range(2 * self.N)])