some cleanup

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -190,8 +190,7 @@ discreteElimination(const HybridGaussianFactorGraph &factors,
 /* ************************************************************************ */
 // If any GaussianFactorGraph in the decision tree contains a nullptr, convert
 // that leaf to an empty GaussianFactorGraph. Needed since the DecisionTree will
-// otherwise create a GFG with a single (null) factor.
-// TODO(dellaert): still a mystery to me why this is needed.
+// otherwise create a GFG with a single (null) factor, which doesn't register as null.
 GaussianFactorGraphTree removeEmpty(const GaussianFactorGraphTree &sum) {
   auto emptyGaussian = [](const GaussianFactorGraph &graph) {
     bool hasNull =
@@ -275,9 +274,9 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
     };
 
     DecisionTree<Key, double> probabilities(eliminationResults, probability);
-    return {std::make_shared<HybridConditional>(gaussianMixture),
-            std::make_shared<DecisionTreeFactor>(discreteSeparator,
-                                                   probabilities)};
+    return {
+        std::make_shared<HybridConditional>(gaussianMixture),
+        std::make_shared<DecisionTreeFactor>(discreteSeparator, probabilities)};
   } else {
     // Otherwise, we create a resulting GaussianMixtureFactor on the separator,
     // taking care to correct for conditional constant.

gtsam/linear/HessianFactor.h

@@ -131,7 +131,7 @@ namespace gtsam {
      * term, and f the constant term.
      * JacobianFactor error is \f[ 0.5* (Ax-b)' M (Ax-b) = 0.5*x'A'MAx - x'A'Mb + 0.5*b'Mb \f]
      * HessianFactor  error is \f[ 0.5*(x'Gx - 2x'g + f) = 0.5*x'Gx    - x'*g   + 0.5*f    \f]
-     * So, with \f$ A = [A1 A2] \f$ and \f$ G=A*'M*A = [A1';A2']*M*[A1 A2] \f$ we have
+     * So, with \f$ A = [A1 A2] \f$ and \f$ G=A'*M*A = [A1';A2']*M*[A1 A2] \f$ we have
      \code
       n1*n1 G11 = A1'*M*A1
       n1*n2 G12 = A1'*M*A2