Merged

dlib/global_optimization/upper_bound_function.h

@@ -140,8 +140,6 @@ namespace dlib
         {
             const long dims = points[0].x.size();
 
-            using sample_type = std::vector<std::pair<size_t,double>>;
-
             using sample_type = std::vector<std::pair<size_t,double>>;
             using kernel_type = sparse_linear_kernel<sample_type>;
             std::vector<sample_type> x;

dlib/global_optimization/upper_bound_function_abstract.h

@@ -54,6 +54,7 @@ namespace dlib
                             double local_bound = p.y + sqrt(noise_terms[i] + trans(p.x-x)*M*(p.x-x))
                             min_ub = min(min_ub, local_bound)
                         }
+                        return min_ub;
                     }
                 Where POINTS is an array of function_evaluation instances drawn from F(x),
                 M is a diagonal matrix, and noise_terms is an array of scalars.