def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
YU = self.Y - self.U
self.X = np.asarray(sl.cho_solve_ATAI(
self.D, 1.0, self.block_sep0(YU) +
self.D.T.dot(self.block_sep1(YU)), self.lu, self.piv),
dtype=self.dtype)
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
YU = self.Y - self.U
self.X = np.asarray(sl.cho_solve_ATAI(
self.D, 1.0, self.block_sep0(YU) +
self.D.T.dot(self.block_sep1(YU)), self.lu, self.piv),
dtype=self.dtype)
def test_05(self):
rho = 1e-1
N = 64
M = 128
K = 32
D = np.random.randn(N, M)
X = np.random.randn(M, K)
S = D.dot(X)
Z = (D.T.dot(D).dot(X) + rho*X - D.T.dot(S)) / rho
c, lwr = linalg.cho_factor(D, rho)
Xslv = linalg.cho_solve_ATAI(D, rho, D.T.dot(S) + rho*Z, c, lwr)
assert(linalg.rrs(D.T.dot(D).dot(Xslv) + rho*Xslv,
D.T.dot(S) + rho*Z) < 1e-11)
def test_06(self):
rho = 1e-1
N = 128
M = 64
K = 32
D = np.random.randn(N, M)
X = np.random.randn(M, K)
S = D.dot(X)
Z = (D.T.dot(D).dot(X) + rho*X - D.T.dot(S)) / rho
c, lwr = linalg.cho_factor(D, rho)
Xslv = linalg.cho_solve_ATAI(D, rho, D.T.dot(S) + rho*Z, c, lwr)
assert(linalg.rrs(D.T.dot(D).dot(Xslv) + rho*Xslv,
D.T.dot(S) + rho*Z) < 1e-14)
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
self.X = np.asarray(sl.cho_solve_ATAI(
self.D, self.rho, self.DTS + self.rho * (self.Y - self.U),
self.lu, self.piv), dtype=self.dtype)
if self.opt['LinSolveCheck']:
b = self.DTS + self.rho * (self.Y - self.U)
ax = self.D.T.dot(self.D.dot(self.X)) + self.rho*self.X
self.xrrs = sl.rrs(ax, b)
else:
self.xrrs = None
def xstep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{x}`.
"""
self.X = np.asarray(sl.cho_solve_ATAI(
self.D, self.rho, self.DTS + self.rho * (self.Y - self.U),
self.lu, self.piv), dtype=self.dtype)
if self.opt['LinSolveCheck']:
b = self.DTS + self.rho * (self.Y - self.U)
ax = self.D.T.dot(self.D.dot(self.X)) + self.rho*self.X
self.xrrs = sl.rrs(ax, b)
else:
self.xrrs = None
