updated the docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402452

docs/docs/algorithms.xml

@@ -328,7 +328,7 @@
          <file>dlib/optimization.h</file>
          <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
          <description>
-             Performs an unconstrained minimization of the function f() using the 
+             Performs an unconstrained minimization of the potentially nonlinear function f() using the 
              BFGS quasi newton method.  
          </description>
                                  
@@ -341,7 +341,7 @@
          <file>dlib/optimization.h</file>
          <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
          <description>
-             Performs an unconstrained minimization of the function f() using a 
+             Performs an unconstrained minimization of the potentially nonlinear function f() using a 
               conjugate gradient method. 
          </description>
                                  
@@ -354,7 +354,7 @@
          <file>dlib/optimization.h</file>
          <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
          <description>
-             Performs an unconstrained minimization of the function f() using the
+             Performs an unconstrained minimization of the potentially nonlinear function f() using the
              BFGS quasi newton method.  This version doesn't take a gradient function of f()
               but instead numerically approximates the gradient.
          </description>
@@ -368,7 +368,7 @@
          <file>dlib/optimization.h</file>
          <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
          <description>
-             Performs an unconstrained minimization of the function f() using a 
+             Performs an unconstrained minimization of the potentially nonlinear function f() using a 
               conjugate gradient method. This version doesn't take a gradient function of f()
               but instead numerically approximates the gradient.
          </description>

docs/docs/index.xml

@@ -106,7 +106,7 @@
                      <a href="dlib/matrix/matrix_utilities_abstract.h.html#svd">singular value decomposition</a>, 
                      <a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>, 
                      <a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc...</li>
-                  <li>Unconstrained optimization algorithms such as 
+                  <li>Unconstrained non-linear optimization algorithms such as 
                      <a href="algorithms.html#find_min_conjugate_gradient">conjugate gradient</a> and <a href="algorithms.html#find_min_quasi_newton">quasi newton</a> techniques</li>
                   <li>A <a href="algorithms.html#bigint">big integer</a> object</li>
                   <li>A <a href="algorithms.html#rand">random number</a> object</li>