Fix bug in how optimized variance is stored in MLBLUE

Author: jdjakem

pyapprox/multifidelity/monte_carlo_estimators.py

@@ -1255,7 +1255,13 @@ def allocate_samples(self, target_cost, asketch,
 
         # set attributes needed for self._estimate
         self.nsamples_per_subset = nsamples_per_subset
-        self.optimized_variance = variance
+        # variance for unrounded nsamples_per_subset
+        # self.optimized_variance = variance/target_cost
+        # variance for rounded nsamples_per_subset
+        self.optimized_variance = BLUE_variance(
+            asketch, self.cov, None, self._reg_blue,
+            self.nsamples_per_subset)
+        print(self.optimized_variance, variance/target_cost)
         self.rounded_target_cost = rounded_target_cost
         self.subsets = subsets
         return nsamples_per_subset, variance, rounded_target_cost

tutorials/multi_fidelity/plot_many_model_approximate_control_variate_monte_carlo.py

@@ -334,7 +334,6 @@
 #%%
 #The variance of the best ACV-GMFB still converges to the lowest possible variance. But its variance at small sample sizes is better than ACV-MF  and comparable to MLMC.
 #
-#TODO Make note about how this scheme is useful when one model may have multiple discretizations.!!!!
 
 #%%
 #Optimal Sample Allocation