Imported from /home/lorry/working-area/delta_boost-tarball/boost_1_58_0.tar.bz2.HEADboost_1_58_0master

2 files changed, 4 insertions, 2 deletions

diff --git a/libs/geometry/doc/index/introduction.qbk b/libs/geometry/doc/index/introduction.qbk
index daf2e39f3..e0c5b03e0 100644
--- a/libs/geometry/doc/index/introduction.qbk
+++ b/libs/geometry/doc/index/introduction.qbk
@@ -56,8 +56,10 @@ The examples of structures of trees created by use of different algorithms and e
 [[*100k knn queries*]       [6.37s] [2.09s] [0.64s] [0.52s]]
 ]
 
+The configuration of the machine used for testing was: /Intel(R) Core(TM) i7 870 @ 2.93GHz, 8GB RAM, MS Windows 7 x64/.
+The code was compiled with optimization for speed (`O2`).
+
 The performance of the R-tree for different values of Max parameter and Min=0.5*Max is presented in the table below.
-The configuration of the machine used for testing is: /Intel(R) Core(TM) i7 870 @ 2.93GHz, 8GB RAM, MS Windows 7 x64/.
 In the two upper figures you can see the performance of the __rtree__ storing random, relatively small, non-overlapping, 2d boxes.
 In the lower ones, the performance of the __rtree__ also storing random, 2d boxes, but this time quite big and possibly overlapping.
 As you can see, the __rtree__ performance is different in both cases.
diff --git a/libs/geometry/doc/index/src/examples/rtree/range_adaptors.cpp b/libs/geometry/doc/index/src/examples/rtree/range_adaptors.cpp
index f01d978cf..799301682 100644
--- a/libs/geometry/doc/index/src/examples/rtree/range_adaptors.cpp
+++ b/libs/geometry/doc/index/src/examples/rtree/range_adaptors.cpp
@@ -65,7 +65,7 @@ int main()
         boxes.push_back(b);
     }
 
-    // create the rtree using default constructor
+    // create the rtree passing a Range
     bgi::rtree< value, bgi::quadratic<16> >
         rtree(boxes | boost::adaptors::indexed()
                     | boost::adaptors::transformed(pair_maker<box, size_type>()));