Robust optimisation

First optimise with full cost function before starting iterative cost function

Also, fixed the cost function for DWI

src/build_sequences.jl

@@ -24,6 +24,10 @@ function iterate_cost()
     end
 end
 
+function total_cost_func()
+    return sum([10^(-w) * f for (w, f) in GLOBAL_MODEL[][2]]; init=0.)
+end
+
 
 """
 Wrapper to build a sequence.
@@ -71,11 +75,27 @@ function build_sequence(f::Function, scanner::Union{Nothing, Scanner}, model::Tu
         if optimise
             jump_model = GLOBAL_MODEL[][1]
             if !iszero(num_variables(jump_model))
+                optimise_with_cost_func(jump_model, total_cost_func(), n_attempts)
                 prev_cost_func = nothing
                 for cost_func in iterate_cost()
                     if !isnothing(prev_cost_func)
                         @constraint jump_model prev_cost_func == objective_value(jump_model)
                     end
+                    optimise_with_cost_func(jump_model, cost_func, n_attempts)
+                    prev_cost_func = cost_func
+                end
+            end
+            return fixed(sequence)
+        else
+            return sequence
+        end
+    finally
+        GLOBAL_MODEL[] = prev_model
+        GLOBAL_SCANNER[] = prev_scanner
+    end
+end
+
+function optimise_with_cost_func(jump_model::Model, cost_func, n_attempts)
     @objective jump_model Min cost_func
     min_objective = Inf
     for attempt in 1:n_attempts
@@ -104,17 +124,6 @@ function build_sequence(f::Function, scanner::Union{Nothing, Scanner}, model::Tu
         println(solution_summary(jump_model))
         error("Optimisation failed to converge.")
     end
-                    prev_cost_func = cost_func
-                end
-            end
-            return fixed(sequence)
-        else
-            return sequence
-        end
-    finally
-        GLOBAL_MODEL[] = prev_model
-        GLOBAL_SCANNER[] = prev_scanner
-    end
 end
 
 function build_sequence(f::Function, scanner::Union{Nothing, Scanner}, optimiser_constructor; optimise=true, n_attempts=100, kwargs...)