fix comments

gtsam_unstable/linear/ActiveSetSolver.h

@@ -25,7 +25,7 @@
 namespace gtsam {
 
 /**
- * This class implementations the active set algorithm for solving convex
+ * This class implements the active set algorithm for solving convex
  * Programming problems.
  *
  * @tparam PROBLEM Type of the problem to solve, e.g. LP (linear program) or 
@@ -36,18 +36,15 @@ namespace gtsam {
 template <class PROBLEM, class POLICY, class INITSOLVER>
 class ActiveSetSolver {
 public:
-  /**
-   * This struct contains the state information for a single iteration of an
-   * active set method iteration.
-   */
+  /// This struct contains the state information for a single iteration
   struct State {
     VectorValues values;  //!< current best values at each step
     VectorValues duals;   //!< current values of dual variables at each step
     InequalityFactorGraph workingSet; /*!< keep track of current active/inactive
                                            inequality constraints */
     bool converged;     //!< True if the algorithm has converged to a solution
-    size_t iterations;  //!< number of iterations. Incremented at the end of
-                        //each iter.
+    size_t iterations;  /*!< Number of iterations. Incremented at the end of
+                        each iteration. */
 
     /// Default constructor
     State()

gtsam_unstable/linear/LPSolver.h

@@ -28,7 +28,8 @@ namespace gtsam {
 
 /// Policy for ActivetSetSolver to solve Linear Programming \sa LP problems
 struct LPPolicy {
-  /// Maximum alpha for line search. For LP, it's infinity
+  /// Maximum alpha for line search x'=xk + alpha*p, where p is the cost gradient
+  /// For LP, maxAlpha = Infinity
   static constexpr double maxAlpha = std::numeric_limits<double>::infinity();
 
   /**

gtsam_unstable/linear/QPSolver.h

@@ -27,7 +27,8 @@ namespace gtsam {
 
 /// Policy for ActivetSetSolver to solve Linear Programming \sa QP problems
 struct QPPolicy {
-  /// Maximum alpha for line search. For QP, it's 1
+  /// Maximum alpha for line search x'=xk + alpha*p, where p is the cost gradient
+  /// For QP, maxAlpha = 1 is the minimum point of the quadratic cost
   static constexpr double maxAlpha = 1.0;
 
   /// Simply the cost of the QP problem