def ystep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}`.
"""
self.Y = np.asarray(sp.proj_l1(self.AX + self.U, self.gamma, axis=0),
dtype=self.dtype)
super(BPDNProjL1, self).ystep()
def ystep(self):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}`.
"""
self.Y = np.asarray(sp.proj_l1(self.AX + self.U, self.gamma, axis=0),
dtype=self.dtype)
super(BPDNProjL1, self).ystep()
def eval_objfn(self):
"""Compute components of regularisation function as well as total
contribution to objective function.
"""
dfd = self.obfn_dfd()
prj = sp.proj_l1(self.obfn_gvar(), self.gamma, axis=0)
cns = np.linalg.norm(prj - self.obfn_gvar())
return (dfd, cns)
def eval_objfn(self):
"""Compute components of regularisation function as well as total
contribution to objective function.
"""
dfd = self.obfn_dfd()
prj = sp.proj_l1(self.obfn_gvar(), self.gamma, axis=0)
cns = np.linalg.norm(prj - self.obfn_gvar())
return (dfd, cns)