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 __call__(self, x): # x is space time element im x vf
ret = 0
xim = x[0]
xvf = x[1]
for i in range(self.N - 1):
ximi = xim[i]
ximii = xim[i + 1]
xvfi = xvf[i]
res = self.residual(ximii, ximi, xvfi)
ret += L1Norm(res.space)(res)
return self.alpha * ret
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