def xstep(self):
"""Minimise Augmented Lagrangian with respect to x."""
self.cgit = None
self.YU[:] = self.Y - self.U
b = self.ASf + self.rho * sl.rfftn(self.YU, None, self.cri.axisN)
if self.opt['LinSolve'] == 'SM':
self.Xf[:] = sl.solvemdbi_ism(self.Af, self.rho, b, self.cri.axisM,
self.cri.axisK)
else:
self.Xf[:], cgit = sl.solvemdbi_cg(self.Af, self.rho, b,
self.cri.axisM, self.cri.axisK,
self.opt['CG', 'StopTol'],
self.opt['CG',
'MaxIter'], self.Xf)
self.cgit = cgit
self.X = sl.irfftn(self.Xf, self.cri.Nv, self.cri.axisN)
if self.opt['LinSolveCheck']:
Aop = lambda x: np.sum(
self.Af * x, axis=self.cri.axisM, keepdims=True)
AHop = lambda x: np.sum(
np.conj(self.Af) * x, axis=self.cri.axisK, keepdims=True)
ax = AHop(Aop(self.Xf)) + self.rho * self.Xf
self.xrrs = sl.rrs(ax, b)
else:
self.xrrs = None
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to :math:`\mathbf{x}`.
"""
self.cgit = None
self.YU[:] = self.Y - self.U
b = self.ZSf + self.rho * sl.rfftn(self.YU, None, self.cri.axisN)
self.Xf[:], cgit = sl.solvemdbi_cg(self.Zf, self.rho, b,
self.cri.axisM, self.cri.axisK,
self.opt['CG', 'StopTol'],
self.opt['CG', 'MaxIter'], self.Xf)
self.cgit = cgit
self.X = sl.irfftn(self.Xf, self.cri.Nv, self.cri.axisN)
self.xstep_check(b)
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to :math:`\mathbf{x}`.
"""
self.cgit = None
self.YU[:] = self.Y - self.U
b = self.ZSf + self.rho*sl.rfftn(self.YU, None, self.cri.axisN)
self.Xf[:], cgit = sl.solvemdbi_cg(self.Zf, self.rho, b,
self.cri.axisM, self.cri.axisK,
self.opt['CG', 'StopTol'],
self.opt['CG', 'MaxIter'], self.Xf)
self.cgit = cgit
self.X = sl.irfftn(self.Xf, self.cri.Nv, self.cri.axisN)
self.xstep_check(b)
def test_15(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = complex_randn(N, N, 1, 1, M)
X = complex_randn(N, N, 1, K, M)
S = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.conj(X) * x, axis=3, keepdims=True)
Z = (XHop(Xop(D)) + rho*D - XHop(S)) / rho
Dslv, cgit = linalg.solvemdbi_cg(X, rho, XHop(S)+rho*Z, 4, 3, tol=1e-6)
assert linalg.rrs(XHop(Xop(Dslv)) + rho*Dslv, XHop(S) + rho*Z) <= 1e-6
def test_15(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = util.complex_randn(N, N, 1, 1, M)
X = util.complex_randn(N, N, 1, K, M)
S = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.conj(X) * x, axis=3, keepdims=True)
Z = (XHop(Xop(D)) + rho*D - XHop(S)) / rho
Dslv, cgit = linalg.solvemdbi_cg(X, rho, XHop(S)+rho*Z, 4, 3, tol=1e-6)
assert linalg.rrs(XHop(Xop(Dslv)) + rho*Dslv, XHop(S) + rho*Z) <= 1e-6
def test_10(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = np.random.randn(N, N, 1, 1, M).astype('complex') + \
np.random.randn(N, N, 1, 1, M).astype('complex') * 1.0j
X = np.random.randn(N, N, 1, K, M).astype('complex') + \
np.random.randn(N, N, 1, K, M).astype('complex') * 1.0j
S = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.conj(X) * x, axis=3, keepdims=True)
Z = (XHop(Xop(D)) + rho*D - XHop(S)) / rho
Dslv, cgit = linalg.solvemdbi_cg(X, rho, XHop(S)+rho*Z, 4, 3, tol=1e-6)
assert(linalg.rrs(XHop(Xop(Dslv)) + rho*Dslv, XHop(S) + rho*Z) <= 1e-6)
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
self.cgit = None
self.YU[:] = self.Y - self.U
self.block_sep0(self.YU)[:] += self.S
YUf = sl.rfftn(self.YU, None, self.cri.axisN)
b = sl.inner(np.conj(self.Zf), self.block_sep0(YUf),
axis=self.cri.axisK) + self.block_sep1(YUf)
self.Xf[:], cgit = sl.solvemdbi_cg(
self.Zf, 1.0, b, self.cri.axisM, self.cri.axisK,
self.opt['CG', 'StopTol'], self.opt['CG', 'MaxIter'], self.Xf)
self.cgit = cgit
self.X = sl.irfftn(self.Xf, self.cri.Nv, self.cri.axisN)
self.xstep_check(b)
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
self.cgit = None
self.YU[:] = self.Y - self.U
self.block_sep0(self.YU)[:] += self.S
YUf = sl.rfftn(self.YU, None, self.cri.axisN)
b = sl.inner(np.conj(self.Zf), self.block_sep0(YUf),
axis=self.cri.axisK) + self.block_sep1(YUf)
self.Xf[:], cgit = sl.solvemdbi_cg(self.Zf, 1.0, b,
self.cri.axisM, self.cri.axisK,
self.opt['CG', 'StopTol'],
self.opt['CG', 'MaxIter'], self.Xf)
self.cgit = cgit
self.X = sl.irfftn(self.Xf, self.cri.Nv, self.cri.axisN)
self.xstep_check(b)
