diff options
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py | 74 |
1 files changed, 52 insertions, 22 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py index 0b9921c..086e322 100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py @@ -45,36 +45,66 @@ class PDHG(Algorithm): self.y_old = self.operator.range_geometry().allocate() self.xbar = self.x_old.copy() - #x_tmp = x_old + self.x = self.x_old.copy() self.y = self.y_old.copy() - #y_tmp = y_old + if self.memopt: + self.y_tmp = self.y_old.copy() + self.x_tmp = self.x_old.copy() #y = y_tmp # relaxation parameter self.theta = 1 def update(self): - # Gradient descent, Dual problem solution - self.y_old += self.sigma * self.operator.direct(self.xbar) - self.y = self.f.proximal_conjugate(self.y_old, self.sigma) - - # Gradient ascent, Primal problem solution - self.x_old -= self.tau * self.operator.adjoint(self.y) - self.x = self.g.proximal(self.x_old, self.tau) - - #Update - #xbar = x + theta * (x - x_old) - self.xbar.fill(self.x) - self.xbar -= self.x_old - self.xbar *= self.theta - self.xbar += self.x - -# self.x_old.fill(self.x) -# self.y_old.fill(self.y) - self.y_old = self.y.copy() - self.x_old = self.x.copy() - #self.y = self.y_old + if self.memopt: + # Gradient descent, Dual problem solution + # self.y_old += self.sigma * self.operator.direct(self.xbar) + self.operator.direct(self.xbar, out=self.y_tmp) + self.y_tmp *= self.sigma + self.y_old += self.y_tmp + + #self.y = self.f.proximal_conjugate(self.y_old, self.sigma) + self.f.proximal_conjugate(self.y_old, self.sigma, out=self.y) + + # Gradient ascent, Primal problem solution + self.operator.adjoint(self.y, out=self.x_tmp) + self.x_tmp *= self.tau + self.x_old -= self.x_tmp + + self.g.proximal(self.x_old, self.tau, out=self.x) + + #Update + self.x.subtract(self.x_old, out=self.xbar) + #self.xbar -= self.x_old + self.xbar *= self.theta + self.xbar += self.x + + self.x_old.fill(self.x) + self.y_old.fill(self.y) + #self.y_old = self.y.copy() + #self.x_old = self.x.copy() + else: + # Gradient descent, Dual problem solution + self.y_old += self.sigma * self.operator.direct(self.xbar) + self.y = self.f.proximal_conjugate(self.y_old, self.sigma) + + # Gradient ascent, Primal problem solution + self.x_old -= self.tau * self.operator.adjoint(self.y) + self.x = self.g.proximal(self.x_old, self.tau) + + #Update + #xbar = x + theta * (x - x_old) + self.xbar.fill(self.x) + self.xbar -= self.x_old + self.xbar *= self.theta + self.xbar += self.x + + self.x_old.fill(self.x) + self.y_old.fill(self.y) + #self.y_old = self.y.copy() + #self.x_old = self.x.copy() + #self.y = self.y_old def update_objective(self): p1 = self.f(self.operator.direct(self.x)) + self.g(self.x) |