diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py | 16 |
1 files changed, 6 insertions, 10 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py index 2cbd146..c0b774d 100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py @@ -19,14 +19,13 @@ class PDHG(Algorithm): '''Primal Dual Hybrid Gradient''' def __init__(self, **kwargs): - super(PDHG, self).__init__() + super(PDHG, self).__init__(max_iteration=kwargs.get('max_iteration',0)) self.f = kwargs.get('f', None) self.operator = kwargs.get('operator', None) self.g = kwargs.get('g', None) self.tau = kwargs.get('tau', None) self.sigma = kwargs.get('sigma', None) - self.memopt = kwargs.get('memopt', False) - + if self.f is not None and self.operator is not None and \ self.g is not None: print ("Calling from creator") @@ -46,25 +45,22 @@ class PDHG(Algorithm): self.opt = opt if sigma is None and tau is None: raise ValueError('Need sigma*tau||K||^2<1') - - + self.x_old = self.operator.domain_geometry().allocate() self.x_tmp = self.x_old.copy() self.x = self.x_old.copy() - + self.y_old = self.operator.range_geometry().allocate() self.y_tmp = self.y_old.copy() self.y = self.y_old.copy() - + self.xbar = self.x_old.copy() - # relaxation parameter self.theta = 1 def update(self): - # Gradient descent, Dual problem solution self.operator.direct(self.xbar, out=self.y_tmp) self.y_tmp *= self.sigma @@ -90,7 +86,7 @@ class PDHG(Algorithm): self.y_old.fill(self.y) def update_objective(self): - + p1 = self.f(self.operator.direct(self.x)) + self.g(self.x) d1 = -(self.f.convex_conjugate(self.y) + self.g.convex_conjugate(-1*self.operator.adjoint(self.y))) |