def ystep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}`."""
self.Y = np.asarray(sp.prox_sl1l2(
self.AX + self.U, (self.lmbda / self.rho) * self.wl1,
self.mu / self.rho, axis=-1), dtype=self.dtype)
GenericBPDN.ystep(self)
def ystep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}`.
"""
self.Y = sp.prox_sl1l2(self.AX + self.U,
(self.lmbda / self.rho) * self.wl1,
(self.mu / self.rho), axis=self.cri.axisC)
cbpdn.GenericConvBPDN.ystep(self)
def ystep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}`.
"""
AXU = self.AX + self.U
Y0 = sp.prox_l1(self.block_sep0(AXU) - self.S, (1.0/self.rho)*self.W)
Y1 = sp.prox_sl1l2(self.block_sep1(AXU), 0.0,
(self.lmbda/self.rho)*self.wl21,
axis=self.cri.axisC)
self.Y = self.block_cat(Y0, Y1)
cbpdn.ConvTwoBlockCnstrnt.ystep(self)
def test_12(self):
assert np.sum(np.abs(prox.prox_sl1l2(self.V1, 1e-2, 1e-2))) > 0
assert prox.norm_nuclear(self.V1) > 0
