Fixed Doxygen warnings.

gtsam/base/LieScalar.h

@@ -36,7 +36,7 @@ namespace gtsam {
 		/** access the underlying value */
 		double value() const { return d_; }
 
-		/** print @param s optional string naming the object */
+		/** print @param name optional string naming the object */
 		inline void print(const std::string& name="") const {
 	    std::cout << name << ": " << d_ << std::endl;
 		}

gtsam/base/LieVector.h

@@ -52,7 +52,7 @@ struct LieVector : public Vector, public DerivedValue<LieVector> {
 		return static_cast<Vector>(*this);
 	}
 
-	/** print @param s optional string naming the object */
+	/** print @param name optional string naming the object */
 	inline void print(const std::string& name="") const {
 		gtsam::print(vector(), name);
 	}

gtsam/base/Matrix.h

@@ -217,8 +217,8 @@ void insertSub(Matrix& fullMatrix, const Matrix& subMatrix, size_t i, size_t j);
 
 /**
  * Extracts a column view from a matrix that avoids a copy
- * @param matrix to extract column from
+ * @param A matrix to extract column from
- * @param index of the column
+ * @param j index of the column
  * @return a const view of the matrix
  */
 template<class MATRIX>
@@ -228,8 +228,8 @@ const typename MATRIX::ConstColXpr column(const MATRIX& A, size_t j) {
 
 /**
  * Extracts a row view from a matrix that avoids a copy
- * @param matrix to extract row from
+ * @param A matrix to extract row from
- * @param index of the row
+ * @param j index of the row
  * @return a const view of the matrix
  */
 template<class MATRIX>
@@ -362,7 +362,7 @@ Vector backSubstituteUpper(const Vector& b, const Matrix& U, bool unit=false);
  * @param unit, set true if unit triangular
  * @return the solution x of L*x=b
  */ 
-Vector backSubstituteLower(const Matrix& L, const Vector& d, bool unit=false);
+Vector backSubstituteLower(const Matrix& L, const Vector& b, bool unit=false);
 
 /**
  * create a matrix by stacking other matrices

gtsam/base/TestableAssertions.h

@@ -81,7 +81,7 @@ bool assert_equal(const V& expected, const boost::optional<const V&>& actual, do
 
 /**
  * Version of assert_equals to work with vectors
- * @DEPRECIATED: use container equals instead
+ * \deprecated: use container equals instead
  */
 template<class V>
 bool assert_equal(const std::vector<V>& expected, const std::vector<V>& actual, double tol = 1e-9) {

gtsam/base/Vector.h

@@ -73,8 +73,8 @@ Vector Vector_(const std::vector<double>& data);
 
 /**
  * Create vector initialized to a constant value
- * @param size
+ * @param n is the size of the vector
- * @param constant value
+ * @param value is a constant value to insert into the vector
  */
 Vector repeat(size_t n, double value);
 
@@ -82,7 +82,7 @@ Vector repeat(size_t n, double value);
  * Create basis vector of dimension n,
  * with a constant in spot i
  * @param n is the size of the vector
- * @param index of the one
+ * @param i index of the one
  * @param value is the value to insert into the vector
  * @return delta vector
  */
@@ -92,20 +92,20 @@ Vector delta(size_t n, size_t i, double value);
  * Create basis vector of dimension n,
  * with one in spot i
  * @param n is the size of the vector
- * @param index of the one
+ * @param i index of the one
  * @return basis vector
  */
 inline Vector basis(size_t n, size_t i) { return delta(n, i, 1.0); }
 
 /**
  * Create zero vector
- * @param size
+ * @param n size
  */
 inline Vector zero(size_t n) { return Vector::Zero(n);}
 
 /**
  * Create vector initialized to ones
- * @param size
+ * @param n size
  */
 inline Vector ones(size_t n) { return Vector::Ones(n); }
 	
@@ -249,35 +249,35 @@ double sum(const Vector &a);
 /**
  * Calculates L2 norm for a vector
  * modeled after boost.ublas for compatibility
- * @param vector
+ * @param v vector
  * @return the L2 norm
  */
 double norm_2(const Vector& v);
 
 /**
- * elementwise reciprocal of vector elements
+ * Elementwise reciprocal of vector elements
  * @param a vector
  * @return [1/a(i)]
  */
 Vector reciprocal(const Vector &a);
 
 /**
- * elementwise sqrt of vector elements
+ * Elementwise sqrt of vector elements
- * @param a vector
+ * @param v is a vector
  * @return [sqrt(a(i))]
  */
 Vector esqrt(const Vector& v);
 
 /**
- * absolut values of vector elements
+ * Absolute values of vector elements
- * @param a vector
+ * @param v is a vector
  * @return [abs(a(i))]
  */
 Vector abs(const Vector& v);
 
 /**
- * return the max element of a vector
+ * Return the max element of a vector
- * @param a vector
+ * @param a is a vector
  * @return max(a)
  */
 double max(const Vector &a);
@@ -300,13 +300,13 @@ inline double inner_prod(const V1 &a, const V2& b) {
 
 /**
  * BLAS Level 1 scal: x <- alpha*x
- * @DEPRECIATED: use operators instead
+ * \deprecated: use operators instead
  */
 inline void scal(double alpha, Vector& x) { x *= alpha; }
 
 /**
  * BLAS Level 1 axpy: y <- alpha*x + y
- * @DEPRECIATED: use operators instead
+ * \deprecated: use operators instead
  */
 template<class V1, class V2>
 inline void axpy(double alpha, const V1& x, V2& y) {

gtsam/discrete/DiscreteConditional.h

@@ -89,14 +89,14 @@ namespace gtsam {
 
 		/**
 		 * solve a conditional
-		 * @param parentsAssignment Known values of the parents
+		 * @param parentsValues Known values of the parents
 		 * @return MPE value of the child (1 frontal variable).
 		 */
 		size_t solve(const Values& parentsValues) const;
 
 		/**
 		 * sample
-		 * @param parentsAssignment Known values of the parents
+		 * @param parentsValues Known values of the parents
 		 * @return sample from conditional
 		 */
 		size_t sample(const Values& parentsValues) const;

gtsam/discrete/DiscreteMarginals.h

@@ -55,7 +55,7 @@ namespace gtsam {
 	}
 
 	/** Compute the marginal of a single variable
-	 * 	@param KEY DiscreteKey of the Variable
+	 * 	@param key DiscreteKey of the Variable
 	 * 	@return Vector of marginal probabilities
 	 */
 	Vector marginalProbabilities(const DiscreteKey& key) const {

gtsam/geometry/Rot2.h

@@ -83,7 +83,7 @@ namespace gtsam {
 		/**
 		 * Named constructor with derivative
 		 * Calculate relative bearing to a landmark in local coordinate frame
-		 * @param point 2D location of landmark
+		 * @param d 2D location of landmark
 		 * @param H optional reference for Jacobian
 		 * @return 2D rotation \in SO(2)
 		 */

gtsam/geometry/Rot3.h

@@ -394,7 +394,7 @@ namespace gtsam {
    * such that A = R*Q = R*Qz'*Qy'*Qx'. When A is a rotation matrix, R will
    * be the identity and Q is a yaw-pitch-roll decomposition of A.
    * The implementation uses Givens rotations and is based on Hartley-Zisserman.
-   * @param a 3 by 3 matrix A=RQ
+   * @param A 3 by 3 matrix A=RQ
    * @return an upper triangular matrix R
    * @return a vector [thetax, thetay, thetaz] in radians.
    */

gtsam/inference/EliminationTree.h

@@ -157,7 +157,7 @@ public:
  * disconnected graphs, a workaround is to create zero-information factors to
  * bridge the disconnects.  To do this, create any factor type (e.g.
  * BetweenFactor or RangeFactor) with the noise model
- * <tt>\ref sharedPrecision(dim, 0.0)</tt>, where \c dim is the appropriate
+ * <tt>sharedPrecision(dim, 0.0)</tt>, where \c dim is the appropriate
  * dimensionality for that factor.
  */
 struct DisconnectedGraphException : public std::exception {

gtsam/inference/Factor.h

@@ -45,7 +45,7 @@ template<class KEY> class Conditional;
  * typedefs to refer to the associated conditional and shared_ptr types of the
  * derived class.  See IndexFactor, JacobianFactor, etc. for examples.
  *
- * This class is \bold{not} virtual for performance reasons - derived symbolic classes,
+ * This class is \b not virtual for performance reasons - derived symbolic classes,
  * IndexFactor and IndexConditional, need to be created and destroyed quickly
  * during symbolic elimination.  GaussianFactor and NonlinearFactor are virtual.
  * \nosubgrouping

gtsam/inference/FactorGraph.h

@@ -245,8 +245,8 @@ template<class CONDITIONAL, class CLIQUE> class BayesTree;
 
 	/**
    * static function that combines two factor graphs
-   * @param const &fg1 Linear factor graph
+   * @param fg1 Linear factor graph
-   * @param const &fg2 Linear factor graph
+   * @param fg2 Linear factor graph
    * @return a new combined factor graph
    */
 	template<class FACTORGRAPH>

gtsam/linear/GaussianFactorGraph.h

@@ -151,9 +151,9 @@ namespace gtsam {
     }
 
     /**
-     * static function that combines two factor graphs
+     * Static function that combines two factor graphs.
-     * @param const &lfg1 Linear factor graph
+     * @param lfg1 Linear factor graph
-     * @param const &lfg2 Linear factor graph
+     * @param lfg2 Linear factor graph
      * @return a new combined factor graph
      */
     static GaussianFactorGraph combine2(const GaussianFactorGraph& lfg1,

gtsam/linear/GaussianMultifrontalSolver.h

@@ -116,7 +116,7 @@ public:
    * all of the other variables.  This function returns the result as a mean
    * vector and covariance matrix.  Compared to marginalCanonical, which
    * returns a GaussianConditional, this function back-substitutes the R factor
-   * to obtain the mean, then computes \Sigma = (R^T * R)^-1.
+   * to obtain the mean, then computes \f$ \Sigma = (R^T * R)^{-1} \f$.
    */
    Matrix marginalCovariance(Index j) const;
 

gtsam/linear/GaussianSequentialSolver.h

@@ -115,7 +115,7 @@ public:
    * all of the other variables.  This function returns the result as a mean
    * vector and covariance matrix.  Compared to marginalCanonical, which
    * returns a GaussianConditional, this function back-substitutes the R factor
-   * to obtain the mean, then computes \Sigma = (R^T * R)^-1.
+   * to obtain the mean, then computes \f$ \Sigma = (R^T * R)^{-1} \f$.
    */
   Matrix marginalCovariance(Index j) const;
 

gtsam/linear/HessianFactor.h

@@ -237,7 +237,7 @@ namespace gtsam {
     /** Return the number of columns and rows of the Hessian matrix */
     size_t rows() const { return info_.rows(); }
 
-    /** Return a view of the block at (j1,j2) of the <emph>upper-triangular part</emph> of the
+    /** Return a view of the block at (j1,j2) of the <em>upper-triangular part</em> of the
      * information matrix \f$ H \f$, no data is copied.  See HessianFactor class documentation
      * above to explain that only the upper-triangular part of the information matrix is stored
      * and returned by this function.
@@ -249,7 +249,7 @@ namespace gtsam {
      */
     constBlock info(const_iterator j1, const_iterator j2) const { return info_(j1-begin(), j2-begin()); }
 
-    /** Return the <emph>upper-triangular part</emph> of the full *augmented* information matrix,
+    /** Return the <em>upper-triangular part</em> of the full *augmented* information matrix,
      * as described above.  See HessianFactor class documentation above to explain that only the
      * upper-triangular part of the information matrix is stored and returned by this function.
      */

gtsam/linear/JacobianFactorGraph.cpp

@@ -1,7 +1,7 @@
 /**
  * @file   JacobianFactorGraph.h
  * @date   Jun 6, 2012
- * @Author Yong Dian Jian
+ * @author Yong Dian Jian
  */
 
 #include <gtsam/linear/JacobianFactorGraph.h>

gtsam/linear/JacobianFactorGraph.h

@@ -13,7 +13,7 @@
  * @file   JacobianFactorGraph.cpp
  * @date   Jun 6, 2012
  * @brief  Linear Algebra Operations for a JacobianFactorGraph
- * @Author Yong Dian Jian
+ * @author Yong Dian Jian
  */
 #pragma once
 

gtsam/linear/VectorValues.h

@@ -147,7 +147,7 @@ namespace gtsam {
 
     /** Insert a vector \c value with index \c j.
      * Causes reallocation. Can be used to insert values in any order, but
-     * throws an invalid_argument exception if the index \j is already used.
+     * throws an invalid_argument exception if the index \c j is already used.
      * @param value The vector to be inserted.
      * @param j The index with which the value will be associated.
      */
@@ -171,10 +171,10 @@ namespace gtsam {
     /** equals required by Testable for unit testing */
     bool equals(const VectorValues& x, double tol = 1e-9) const;
 
-    /// @}
+    /// @{
     /// \anchor AdvancedConstructors
     /// @name Advanced Constructors
-    /// @{
+    /// @}
 
     /** Construct from a container of variable dimensions (in variable order), without initializing any values. */
     template<class CONTAINER>

gtsam/nonlinear/DoglegOptimizer.cpp

@@ -13,7 +13,7 @@
  * @file    DoglegOptimizer.cpp
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date 	Feb 26, 2012
  */
 
 #include <gtsam/nonlinear/DoglegOptimizer.h>

gtsam/nonlinear/DoglegOptimizer.h

@@ -13,7 +13,7 @@
  * @file    DoglegOptimizer.h
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date 	Feb 26, 2012
  */
 
 #pragma once

gtsam/nonlinear/DoglegOptimizerImpl.h

@@ -72,7 +72,7 @@ struct DoglegOptimizerImpl {
    *
    * The update is computed using a quadratic approximation \f$ M(\delta x) \f$
    * of an original nonlinear error function (a NonlinearFactorGraph) \f$ f(x) \f$.
-   * The quadratic approximation is represented as a GaussianBayesNet \bayesNet, which is
+   * The quadratic approximation is represented as a GaussianBayesNet \f$ \bayesNet \f$, which is
    * obtained by eliminating a GaussianFactorGraph resulting from linearizing
    * the nonlinear factor graph \f$ f(x) \f$.  Thus, \f$ M(\delta x) \f$ is
    * \f[
@@ -93,8 +93,8 @@ struct DoglegOptimizerImpl {
    * @param Rd The Bayes' net or tree as described above.
    * @param f The original nonlinear factor graph with which to evaluate the
    * accuracy of \f$ M(\delta x) \f$ to adjust \f$ \Delta \f$.
-   * @param x0 The linearization point about which \bayesNet was created
+   * @param x0 The linearization point about which \f$ \bayesNet \f$ was created
-   * @param ordering The variable ordering used to create \bayesNet
+   * @param ordering The variable ordering used to create\f$ \bayesNet \f$
    * @param f_error The result of <tt>f.error(x0)</tt>.
    * @return A DoglegIterationResult containing the new \c Delta, the linear
    * update \c dx_d, and the resulting nonlinear error \c f_error.
@@ -111,7 +111,7 @@ struct DoglegOptimizerImpl {
    *
    * The update is computed using a quadratic approximation \f$ M(\delta x) \f$
    * of an original nonlinear error function (a NonlinearFactorGraph) \f$ f(x) \f$.
-   * The quadratic approximation is represented as a GaussianBayesNet \bayesNet, which is
+   * The quadratic approximation is represented as a GaussianBayesNet \f$ \bayesNet \f$, which is
    * obtained by eliminating a GaussianFactorGraph resulting from linearizing
    * the nonlinear factor graph \f$ f(x) \f$.  Thus, \f$ M(\delta x) \f$ is
    * \f[
@@ -134,8 +134,8 @@ struct DoglegOptimizerImpl {
    * \f$ \| (1-\tau)\delta x_u + \tau\delta x_n \| = \Delta \f$, where \f$ \Delta \f$
    * is the trust region radius.
    * @param Delta Trust region radius
-   * @param xu Steepest descent minimizer
+   * @param x_u Steepest descent minimizer
-   * @param xn Newton's method minimizer
+   * @param x_n Newton's method minimizer
    */
   static VectorValues ComputeBlend(double Delta, const VectorValues& x_u, const VectorValues& x_n, const bool verbose=false);
 };

gtsam/nonlinear/GaussNewtonOptimizer.cpp

@@ -13,7 +13,7 @@
  * @file    GaussNewtonOptimizer.cpp
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date 	Feb 26, 2012
  */
 
 #include <gtsam/inference/EliminationTree.h>

gtsam/nonlinear/GaussNewtonOptimizer.h

@@ -13,7 +13,7 @@
  * @file    GaussNewtonOptimizer.h
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date 	Feb 26, 2012
  */
 
 #pragma once

gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp

@@ -13,7 +13,7 @@
  * @file    LevenbergMarquardtOptimizer.cpp
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date	Feb 26, 2012
  */
 
 #include <cmath>

gtsam/nonlinear/LevenbergMarquardtOptimizer.h

@@ -13,7 +13,7 @@
  * @file    LevenbergMarquardtOptimizer.h
  * @brief   
  * @author  Richard Roberts
- * @created Feb 26, 2012
+ * @date 	Feb 26, 2012
  */
 
 #pragma once

gtsam/nonlinear/NonlinearOptimizer.h

@@ -229,7 +229,7 @@ protected:
 };
 
 /** Check whether the relative error decrease is less than relativeErrorTreshold,
- * the absolute error decrease is less than absoluteErrorTreshold, <emph>or</emph>
+ * the absolute error decrease is less than absoluteErrorTreshold, <em>or</em>
  * the error itself is less than errorThreshold.
  */
 bool checkConvergence(double relativeErrorTreshold,