documentation fixes

1 files changed, 15 insertions, 17 deletions

diff --git a/unsupported/Eigen/NonLinearOptimization b/unsupported/Eigen/NonLinearOptimization
index b73935b3b..601b1abc7 100644
--- a/unsupported/Eigen/NonLinearOptimization
+++ b/unsupported/Eigen/NonLinearOptimization
@@ -1,4 +1,4 @@
-// This file is part of Eigen, a lightweight C++ template library
+// This file is part of Eugenio, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
@@ -59,15 +59,14 @@ namespace Eigen {
   * 
   * Finally, we ported this code to Eigen, creating classes and API
   * coherent with Eigen. When possible, we switched to Eigen
-  * implementation, such as most linear algebra (vectors, matrices, "good" norms).
+  * implementation, such as most linear algebra (vectors, matrices, stable norms).
   *
   * Doing so, we were very careful to check the tests we setup at the very
-  * beginning, which ensure that the same results are found, with the same
-  * number of iterations.
+  * beginning, which ensure that the same results are found.
   *
   * \section Tests Tests
   * 
-  * The tests are placed in the directory unsupported/test/NonLinear.cpp.
+  * The tests are placed in the file unsupported/test/NonLinear.cpp.
   * 
   * There are two kinds of tests : those that come from examples bundled with cminpack.
   * They guaranty we get the same results as the original algorithms (value for 'x',
@@ -81,32 +80,31 @@ namespace Eigen {
   * code. Please note that we do not always get the exact same decimals as they do,
   * but this is ok : they use 128bits float, and we do the tests using the C type 'double',
   * which is 64 bits on most platforms (x86 and amd64, at least).
-  * 
   * I've performed those tests on several other implementations of levenberg-marquardt, and
-  * (c)minpack perform VERY well compared to those, both in accuracy and speed.
+  * (c)minpack performs VERY well compared to those, both in accuracy and speed.
   * 
-  * The documentation for running the test is on the wiki
+  * The documentation for running the tests is on the wiki
   * http://eigen.tuxfamily.org/index.php?title=Developer%27s_Corner#Running_the_unit_tests
   * 
   * \section API API : overview of methods
   * 
-  * All algorithms can use either the jacobian (provided by the user) or compute 
-  * an approximation by themselves (or rather, using Eigen \ref NumericalDiff_Module)
-  * The part of API referring to the latter use 'NumericalDiff' in the method name
+  * Both algorithms can use either the jacobian (provided by the user) or compute 
+  * an approximation by themselves (actually using Eigen \ref NumericalDiff_Module).
+  * The part of API referring to the latter use 'NumericalDiff' in the method names
   * (exemple: LevenbergMarquardt.minimizeNumericalDiff() ) 
   * 
   * The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and 
   * HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original 
-  * minpack package that you probably should NOT use but if you port a code that was 
-  * previously using minpack. They just define a 'simple' API with default values 
+  * minpack package that you probably should NOT use until you are porting a code that
+  *  was previously using minpack. They just define a 'simple' API with default values 
   * for some parameters.
   * 
   * All algorithms are provided using Two APIs :
-  *     - one where you init the algorithm, and use '*OneStep()' as much as you want : 
+  *     - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants : 
   * this way the caller have control over the steps
-  *     - one where you just call a method (optimize() or solve()) which will 
-  * basically do exactly the same : init + loop until a stop condition is met. 
-  * Those are provided for convenience.
+  *     - one where the user just calls a method (optimize() or solve()) which will 
+  * handle the loop: init + loop until a stop condition is met. Those are provided for
+  *  convenience.
   * 
   * As an example, the method LevenbergMarquardt.minimizeNumericalDiff() is 
   * implemented as follow :