updated docs

docs/docs/index.xml

@@ -118,9 +118,13 @@
                   <li>Box-constrained derivative-free optimization via the 
                       <a href="optimization.html#find_min_bobyqa">BOBYQA</a> algorithm</li> 
                   <li>An implementation of the <a href="optimization.html#oca">Optimized Cutting Plane Algorithm</a></li> 
-                  <li>Several <preserve_space><a href="optimization.html#solve_qp2_using_smo">quadratic</a> 
+                  <li><preserve_space><a href="optimization.html#solve_qp_using_smo">Several</a> 
+                                      <a href="optimization.html#solve_qp2_using_smo">quadratic</a> 
                                       <a href="optimization.html#solve_qp3_using_smo">program</a>
-                              <a href="optimization.html#solve_qp_using_smo">solvers</a></preserve_space> </li> 
+                                      <a href="optimization.html#solve_qp4_using_smo">solvers</a></preserve_space> </li> 
+                  <li>Combinatorial optimization tools for solving 
+                     <a href="optimization.html#max_cost_assignment">optimal assignment</a> or 
+                     <a href="optimization.html#min_cut">min cut/max flow</a> problems.</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>
                </ul>
@@ -141,6 +145,7 @@
                   <li>Structural SVM tools for <a href="ml.html#structural_sequence_labeling_trainer">sequence labeling</a> </li>  
                   <li>Structural SVM tools for solving <a href="ml.html#structural_assignment_trainer">assignment problems</a> </li>  
                   <li>Structural SVM tools for <a href="ml.html#structural_object_detection_trainer">object detection</a> in images </li>  
+                  <li>Structural SVM tools for <a href="ml.html#structural_graph_labeling_trainer">labeling nodes</a> in graphs </li>  
                   <li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
                   <li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
                   <li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector</li> and
@@ -158,7 +163,8 @@
                   <li><a href="bayes.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> markov chain monte 
                   carlo algorithm for approximate inference in a Bayesian network.</li>
                   <li>Routines for performing MAP inference in 
-                     <a href="optimization.html#find_max_factor_graph_viterbi">chain-structured</a> or 
+                     <a href="optimization.html#find_max_factor_graph_viterbi">chain-structured</a>,
+                     <a href="optimization.html#find_max_factor_graph_potts">Potts</a>, or 
                      <a href="optimization.html#find_max_factor_graph_nmplp">general</a> factor graphs.</li>
                </ul>
             </li>

docs/docs/release_notes.xml

@@ -12,12 +12,53 @@
 
 <current>
 New Stuff:
+   - Improvements to linear algebra tools:
+      - Added the lowerbound() and upperbound() routines for thresholding dense 
+        matrices.
+      - Refined the tools for working with sparse vectors.  In particular, 
+        the following functions were added: min(), max(), make_sparse_vector(),
+        add(), and subtract().  A number of existing routines were also updated
+        to work with both sparse and dense vectors so that templated code which 
+        works on both vector types is simpler to write.
+      - Added the += and -= operators to the set_subm(), set_rowm(), and set_colm()
+        tools for operating on submatrices.
+   - Optimization:
+      - Added a new quadratic program solver, solve_qp4_using_smo().  This new
+        solver is useful for solving quadratic programs corresponding to
+        non-negative constrained primal quadratic programs.
+      - Added an optional non-negativity constraint to the oca optimizer.
+      - Added the min_cut object.  It provides a method to find the minimum weight 
+        cut on a graph.  
+      - Added tools for finding maximum probability assignment in a Potts style 
+        Markov random field.  See the find_max_factor_graph_potts() routine for 
+        details.
+   - Machine Learning:
+      - Added structural SVM tools for learning the parameters of a Potts style
+        Markov random field.  See the structural_graph_labeling_trainer and
+        graph_labeler objects as well as their associated example program for 
+        details.
+      - Added the ability to learn only non-negative weights to the 
+        svm_c_linear_trainer.
+   - Improved Integration with OpenCV: 
+      - Updated the cv_image object so it works with cv::Mat as well as IplImage.
+      - Added the toMat() routine for converting from a dlib style image to an
+        OpenCV cv::Mat image.
 
 Non-Backwards Compatible Changes:
+   - Removed the dlib::sparse_vector namespace.  Everything from this namespace 
+     was moved into the normal dlib:: namespace so that code which works with 
+     both sparse and dense vectors is more cohesive.
 
 Bug fixes:
+   - Fixed a bug in find_max_factor_graph_viterbi() which sometimes occurred when 
+     the model order was larger than the number of variables.
+   - Fixed a bug which caused a compiler error if you tried to call dot() on two 
+     1x1 matrices which were statically dimensioned.
 
 Other:
+   - Improved existing documentation: added pictures of the gui widgets, 
+     added documentation of the dlib::bridge protocol, and other minor 
+     usability improvements.
 
 </current>