def _get_GradientMethod(self):
def gradf(x):
with self.y_device:
r = self.A(x)
r -= self.y
with self.x_device:
gradf_x = self.A.H(r)
if self.lamda != 0:
if self.R is None:
util.axpy(gradf_x, self.lamda, x)
else:
util.axpy(gradf_x, self.lamda, self.R.H(self.R(x)))
if self.mu != 0:
util.axpy(gradf_x, self.mu, x - self.z)
return gradf_x
I = linop.Identity(self.x.shape)
AHA = self.A.H * self.A
if self.lamda != 0:
if self.R is None:
AHA += self.lamda * I
else:
AHA += self.lamda * self.R.H * self.R
if self.mu != 0:
AHA += self.mu * I
max_eig = MaxEig(AHA,
dtype=self.x.dtype,
device=self.x_device,
max_iter=self.max_power_iter,
show_pbar=self.show_pbar).run()
if max_eig == 0:
self.alpha = 1
else:
self.alpha = 1 / max_eig
self.alg = GradientMethod(gradf,
self.x,
self.alpha,
proxg=self.proxg,
max_iter=self.max_iter,
accelerate=self.accelerate)
def _get_GradientMethod(self):
with self.y_device:
AHy = self.A.H(self.y)
def gradf(x):
with self.x_device:
gradf_x = self.A.N(x) - AHy
if self.lamda != 0:
if self.z is None:
util.axpy(gradf_x, self.lamda, x)
else:
util.axpy(gradf_x, self.lamda, x - self.z)
return gradf_x
if self.alpha is None:
I = linop.Identity(self.x.shape)
AHA = self.A.N
if self.lamda != 0:
AHA += self.lamda * I
max_eig = MaxEig(AHA, dtype=self.x.dtype, device=self.x_device,
max_iter=self.max_power_iter,
show_pbar=self.show_pbar).run()
if max_eig == 0:
self.alpha = 1
else:
self.alpha = 1 / max_eig
self.alg = GradientMethod(
gradf,
self.x,
self.alpha,
proxg=self.proxg,
max_iter=self.max_iter,
accelerate=self.accelerate, tol=self.tol)