small changes to syntax (sapyb) in main, README udated with algorithm…

… name in title

README.md

@@ -1,6 +1,6 @@
 # PETRIC: PET Rapid Image reconstruction Challenge
 
-## Stochastic Optimisation with Subsets (SOS)
+## Stochastic Optimisation with Subsets (SOS) - SAGA
 
 Authors: Sam Porter 
 
@@ -27,4 +27,4 @@ The algorithm is warm started with 5 iterations of preconditioned gradient desce
 
 Acknowledgements: 
 - Thanks to Margaret Duff, Casper da Costa-Luis and Edoardo Pasca for all their help
-- This algorithm lies heavily on the stochastic implementations in CIL so thanks to all those wo've helped there, as well
+- This relies heavily on the stochastic implementations in CIL so thanks to all those wo've helped there, as well

main.py

@@ -59,7 +59,7 @@ def __init__(self, acq_models, freeze_iter = np.inf, epsilon=1e-6):
 
         self.epsilon = epsilon
         self.freeze_iter = freeze_iter
-        self.x_freeze = None
+        self.freeze = None
 
         for i,el in enumerate(acq_models):
             if i == 0:
@@ -72,13 +72,12 @@ def __init__(self, acq_models, freeze_iter = np.inf, epsilon=1e-6):
             self.s_sum_inv += s_inv
 
     def apply(self, algorithm, gradient, out=None):
-
         if algorithm.iteration < self.freeze_iter:
-            ret =  gradient * ((algorithm.solution.copy() * self.s_sum_inv) + self.epsilon)
+            ret = gradient * ((algorithm.solution * self.s_sum_inv) + self.epsilon)
         else:
-            if self.x_freeze is None:
-                self.x_freeze = (algorithm.solution.copy() * self.s_sum_inv) + self.epsilon
-            ret =  gradient * self.x_freeze
+            if self.freeze is None:
+                self.freeze = ((algorithm.solution * self.s_sum_inv) + self.epsilon)
+            ret =  gradient * self.freeze
         if out is not None:
             out.fill(ret)
         else:
@@ -135,7 +134,8 @@ def get_step_size(self, algorithm):
             # Armijo step size search
             for _ in range(self.max_iter):
                 # Proximal step
-                x_new = algorithm.g.proximal(algorithm.solution.copy() - step_size * precond_grad, step_size)
+                x_new = algorithm.solution.copy().sapyb(1, precond_grad, -step_size)
+                algorithm.g.proximal(x_new, step_size, out=x_new)
                 f_x_new = algorithm.f(x_new) + algorithm.g(x_new)
                 print("f_x_new: ", f_x_new)
                 # Armijo condition check
@@ -151,7 +151,6 @@ def get_step_size(self, algorithm):
             # Update the internal state with the new step size as the minimum of the current and previous step sizes
             self.step_size = min(step_size, self.step_size)
 
-            self.initial_step_size = step_size / self.beta   # Adjust initial step size for next search
             if self.counter < self.steps:
                 self.counter += 1
 
@@ -225,7 +224,6 @@ def __init__(self, data: Dataset, update_objective_interval=10):
         """
         Initialisation function, setting up data & (hyper)parameters.
         """
-
         # Very simple heuristic to determine the number of subsets
         self.num_subsets = calculate_subsets(data.acquired_data, min_counts_per_subset=2**20, max_num_subsets=16) 
         update_interval = self.num_subsets
@@ -242,12 +240,9 @@ def __init__(self, data: Dataset, update_objective_interval=10):
 
         data.prior.set_penalisation_factor(data.prior.get_penalisation_factor() / len(obj_funs))
         data.prior.set_up(data.OSEM_image)
-
-        grad = data.OSEM_image.get_uniform_copy(0)
 
         for f, d in zip(obj_funs, data_subs): # add prior to every objective function
             f.set_prior(data.prior)
-            grad -= f.gradient(data.OSEM_image)
 
         sampler = Sampler.random_without_replacement(len(obj_funs))
         f = -FullGradientInitialiserFunction(obj_funs, sampler=sampler, init_steps=5)