(in branch) Separate gradient functions for FactorGraph, BayesNet, BayesTree, and BayesTree with ISAM2 Cliques (specialized for using stored gradient contributions in ISAM2 cliques)

gtsam/linear/GaussianBayesNet.cpp

@@ -19,6 +19,7 @@
 #include <boost/foreach.hpp>
 #include <boost/tuple/tuple.hpp>
 
+#include <gtsam/linear/JacobianFactor.h>
 #include <gtsam/linear/GaussianBayesNet.h>
 #include <gtsam/linear/VectorValues.h>
 
@@ -213,6 +214,16 @@ double determinant(const GaussianBayesNet& bayesNet) {
 	return exp(logDet);
 }
 
+/* ************************************************************************* */
+VectorValues gradient(const GaussianBayesNet& bayesNet, const VectorValues& x0) {
+  return gradient(FactorGraph<JacobianFactor>(bayesNet), x0);
+}
+
+/* ************************************************************************* */
+void gradientAtZero(const GaussianBayesNet& bayesNet, VectorValues& g) {
+  gradientAtZero(FactorGraph<JacobianFactor>(bayesNet), g);
+}
+
 /* ************************************************************************* */
 
 } // namespace gtsam

gtsam/linear/GaussianBayesNet.h

@@ -64,19 +64,19 @@ namespace gtsam {
 	 */
 	boost::shared_ptr<VectorValues> optimize_(const GaussianBayesNet& bn);
 
-	/*
+	/**
 	 * Backsubstitute
 	 * (R*x)./sigmas = y by solving x=inv(R)*(y.*sigmas)
 	 * @param y is the RHS of the system
 	 */
 	VectorValues backSubstitute(const GaussianBayesNet& bn, const VectorValues& y);
 
-	/*
+	/**
 	 * Backsubstitute in place, y starts as RHS and is replaced with solution
 	 */
 	void backSubstituteInPlace(const GaussianBayesNet& bn, VectorValues& y);
 
-	/*
+	/**
 	 * Transpose Backsubstitute
 	 * gy=inv(L)*gx by solving L*gy=gx.
 	 * gy=inv(R'*inv(Sigma))*gx
@@ -108,4 +108,26 @@ namespace gtsam {
    */
   double determinant(const GaussianBayesNet& bayesNet);
 
+  /**
+   * Compute the gradient of the energy function,
+   * \f$ \nabla_{x=x_0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+   * centered around \f$ x = x_0 \f$.
+   * The gradient is \f$ R^T(Rx-d) \f$.
+   * @param bayesNet The Gaussian Bayes net $(R,d)$
+   * @param x0 The center about which to compute the gradient
+   * @return The gradient as a VectorValues
+   */
+  VectorValues gradient(const GaussianBayesNet& bayesNet, const VectorValues& x0);
+
+  /**
+   * Compute the gradient of the energy function,
+   * \f$ \nabla_{x=0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+   * centered around zero.
+   * The gradient about zero is \f$ -R^T d \f$.  See also gradient(const GaussianBayesNet&, const VectorValues&).
+   * @param bayesNet The Gaussian Bayes net $(R,d)$
+   * @param [output] g A VectorValues to store the gradient, which must be preallocated, see allocateVectorValues
+   * @return The gradient as a VectorValues
+   */
+  void gradientAtZero(const GaussianBayesNet& bayesNet, VectorValues& g);
+
 } /// namespace gtsam

gtsam/linear/JacobianFactor.cpp

@@ -582,17 +582,28 @@ namespace gtsam {
   }
 
   /* ************************************************************************* */
-  VectorValues gradient(const FactorGraph<JacobianFactor>& fg, const VectorValues& x) {
+  VectorValues gradient(const FactorGraph<JacobianFactor>& fg, const VectorValues& x0) {
     // It is crucial for performance to make a zero-valued clone of x
-    VectorValues g = VectorValues::Zero(x);
+    VectorValues g = VectorValues::Zero(x0);
     Errors e;
     BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
-      e.push_back(factor->error_vector(x));
+      e.push_back(factor->error_vector(x0));
     }
     transposeMultiplyAdd(fg, 1.0, e, g);
     return g;
   }
 
+  /* ************************************************************************* */
+  void gradientAtZero(const FactorGraph<JacobianFactor>& fg, VectorValues& g) {
+    // Zero-out the gradient
+    g.setZero();
+    Errors e;
+    BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
+      e.push_back(-factor->getb());
+    }
+    transposeMultiplyAdd(fg, 1.0, e, g);
+  }
+
   /* ************************************************************************* */
   void residual(const FactorGraph<JacobianFactor>& fg, const VectorValues &x, VectorValues &r) {
     Index i = 0 ;

gtsam/linear/JacobianFactor.h

@@ -318,11 +318,26 @@ namespace gtsam {
   void transposeMultiplyAdd(const FactorGraph<JacobianFactor>& fg, double alpha, const Errors& e, VectorValues& x);
 
   /**
-   * Calculate Gradient of A^(A*x-b) for a given config
+   * Compute the gradient of the energy function,
-   * @param x: VectorValues specifying where to calculate gradient
+   * \f$ \nabla_{x=x_0} \left\Vert \Sigma^{-1} A x - b \right\Vert^2 \f$,
-   * @return gradient, as a VectorValues as well
+   * centered around \f$ x = x_0 \f$.
+   * The gradient is \f$ A^T(Ax-b) \f$.
+   * @param fg The Jacobian factor graph $(A,b)$
+   * @param x0 The center about which to compute the gradient
+   * @return The gradient as a VectorValues
    */
-  VectorValues gradient(const FactorGraph<JacobianFactor>& fg, const VectorValues& x);
+  VectorValues gradient(const FactorGraph<JacobianFactor>& fg, const VectorValues& x0);
+
+  /**
+   * Compute the gradient of the energy function,
+   * \f$ \nabla_{x=0} \left\Vert \Sigma^{-1} A x - b \right\Vert^2 \f$,
+   * centered around zero.
+   * The gradient is \f$ A^T(Ax-b) \f$.
+   * @param fg The Jacobian factor graph $(A,b)$
+   * @param [output] g A VectorValues to store the gradient, which must be preallocated, see allocateVectorValues
+   * @return The gradient as a VectorValues
+   */
+  void gradientAtZero(const FactorGraph<JacobianFactor>& fg, VectorValues& g);
 
   // matrix-vector operations
   void residual(const FactorGraph<JacobianFactor>& fg, const VectorValues &x, VectorValues &r);

gtsam/nonlinear/DoglegOptimizerImpl.h

@@ -233,14 +233,14 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
 template<class M>
 VectorValues DoglegOptimizerImpl::ComputeSteepestDescentPoint(const M& Rd) {
 
-  // Compute gradient
+  // Compute gradient (call gradientAtZero function, which is defined for various linear systems)
-  // Convert to JacobianFactor's to use existing gradient function
+  VectorValues grad = *allocateVectorValues(Rd);
-  FactorGraph<JacobianFactor> jfg(Rd);
+  gradientAtZero(Rd, grad);
-  VectorValues grad = gradient(jfg, VectorValues::Zero(*allocateVectorValues(Rd)));
   double gradientSqNorm = grad.dot(grad);
 
   // Compute R * g
-  Errors Rg = jfg * grad;
+  FactorGraph<JacobianFactor> Rd_jfg(Rd);
+  Errors Rg = Rd_jfg * grad;
 
   // Compute minimizing step size
   double step = -gradientSqNorm / dot(Rg, Rg);

gtsam/nonlinear/GaussianISAM2-inl.h

@@ -1,10 +1,22 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
 /**
  * @file    GaussianISAM2
  * @brief   Full non-linear ISAM
  * @author  Michael Kaess
  */
 
-#include <gtsam/nonlinear/GaussianISAM2.h>
+#include <gtsam/inference/FactorGraph.h>
+#include <gtsam/linear/JacobianFactor.h>
 
 using namespace std;
 using namespace gtsam;

gtsam/nonlinear/GaussianISAM2.cpp

@@ -0,0 +1,61 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    GaussianISAM2
+ * @brief   Full non-linear ISAM
+ * @author  Michael Kaess
+ */
+
+#include <gtsam/inference/FactorGraph.h>
+#include <gtsam/linear/JacobianFactor.h>
+
+using namespace std;
+using namespace gtsam;
+
+#include <boost/bind.hpp>
+
+namespace gtsam {
+
+  /* ************************************************************************* */
+  VectorValues gradient(const BayesTree<GaussianConditional>& bayesTree, const VectorValues& x0) {
+    return gradient(FactorGraph<JacobianFactor>(bayesTree), x0);
+  }
+
+  /* ************************************************************************* */
+  void gradientAtZero(const BayesTree<GaussianConditional>& bayesTree, VectorValues& g) {
+    gradientAtZero(FactorGraph<JacobianFactor>(bayesTree), g);
+  }
+
+  /* ************************************************************************* */
+  VectorValues gradient(const BayesTree<GaussianConditional, ISAM2Clique<GaussianConditional> >& bayesTree, const VectorValues& x0) {
+    return gradient(FactorGraph<JacobianFactor>(bayesTree));
+  }
+
+  /* ************************************************************************* */
+  void gradientAtZero(const BayesTree<GaussianConditional, ISAM2Clique<GaussianConditional> >& bayesTree, VectorValues& g) {
+    // Zero-out gradient
+    g.setZero();
+
+    // Sum up contributions for each clique
+    typedef boost::shared_ptr<ISAM2Clique<GaussianConditional> > sharedClique;
+    BOOST_FOREACH(const sharedClique& clique, bayesTree.nodes()) {
+      // Loop through variables in each clique, adding contributions
+      int variablePosition = 0;
+      for(GaussianConditional::const_iterator jit = clique->conditional()->beginFrontals(); jit != clique->conditional()->endFrontals(); ++jit) {
+        const int dim = clique->conditional()->dim(jit);
+        x0[*jit] += clique->gradientContribution().segment(variablePosition, dim);
+        variablePosition += dim;
+      }
+    }
+  }
+
+}

gtsam/nonlinear/GaussianISAM2.h

@@ -1,3 +1,14 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
 /**
  * @file    GaussianISAM
  * @brief   Full non-linear ISAM.
@@ -41,26 +52,74 @@ public:
   }
 };
 
-// optimize the BayesTree, starting from the root
+/** optimize the BayesTree, starting from the root */
 template<class CLIQUE>
 void optimize2(const boost::shared_ptr<CLIQUE>& root, VectorValues& delta);
 
-// optimize the BayesTree, starting from the root; "replaced" needs to contain
+/// optimize the BayesTree, starting from the root; "replaced" needs to contain
-// all variables that are contained in the top of the Bayes tree that has been
+/// all variables that are contained in the top of the Bayes tree that has been
-// redone; "delta" is the current solution, an offset from the linearization
+/// redone; "delta" is the current solution, an offset from the linearization
-// point; "threshold" is the maximum change against the PREVIOUS delta for
+/// point; "threshold" is the maximum change against the PREVIOUS delta for
-// non-replaced variables that can be ignored, ie. the old delta entry is kept
+/// non-replaced variables that can be ignored, ie. the old delta entry is kept
-// and recursive backsubstitution might eventually stop if none of the changed
+/// and recursive backsubstitution might eventually stop if none of the changed
-// variables are contained in the subtree.
+/// variables are contained in the subtree.
-// returns the number of variables that were solved for
+/// returns the number of variables that were solved for
 template<class CLIQUE>
 int optimize2(const boost::shared_ptr<CLIQUE>& root,
     double threshold, const std::vector<bool>& replaced, Permuted<VectorValues>& delta);
 
-// calculate the number of non-zero entries for the tree starting at clique (use root for complete matrix)
+/// calculate the number of non-zero entries for the tree starting at clique (use root for complete matrix)
 template<class CLIQUE>
 int calculate_nnz(const boost::shared_ptr<CLIQUE>& clique);
 
+/**
+ * Compute the gradient of the energy function,
+ * \f$ \nabla_{x=x_0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+ * centered around \f$ x = x_0 \f$.
+ * The gradient is \f$ R^T(Rx-d) \f$.
+ * @param bayesTree The Gaussian Bayes Tree $(R,d)$
+ * @param x0 The center about which to compute the gradient
+ * @return The gradient as a VectorValues
+ */
+VectorValues gradient(const BayesTree<GaussianConditional>& bayesTree, const VectorValues& x0);
+
+/**
+ * Compute the gradient of the energy function,
+ * \f$ \nabla_{x=0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+ * centered around zero.
+ * The gradient about zero is \f$ -R^T d \f$.  See also gradient(const GaussianBayesNet&, const VectorValues&).
+ * @param bayesTree The Gaussian Bayes Tree $(R,d)$
+ * @param [output] g A VectorValues to store the gradient, which must be preallocated, see allocateVectorValues
+ * @return The gradient as a VectorValues
+ */
+VectorValues gradientAtZero(const BayesTree<GaussianConditional>& bayesTree, VectorValues& g);
+
+/**
+ * Compute the gradient of the energy function,
+ * \f$ \nabla_{x=x_0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+ * centered around \f$ x = x_0 \f$.
+ * The gradient is \f$ R^T(Rx-d) \f$.
+ * This specialized version is used with ISAM2, where each clique stores its
+ * gradient contribution.
+ * @param bayesTree The Gaussian Bayes Tree $(R,d)$
+ * @param x0 The center about which to compute the gradient
+ * @return The gradient as a VectorValues
+ */
+VectorValues gradient(const BayesTree<GaussianConditional, ISAM2Clique<GaussianConditional> >& bayesTree, const VectorValues& x0);
+
+/**
+ * Compute the gradient of the energy function,
+ * \f$ \nabla_{x=0} \left\Vert \Sigma^{-1} R x - d \right\Vert^2 \f$,
+ * centered around zero.
+ * The gradient about zero is \f$ -R^T d \f$.  See also gradient(const GaussianBayesNet&, const VectorValues&).
+ * This specialized version is used with ISAM2, where each clique stores its
+ * gradient contribution.
+ * @param bayesTree The Gaussian Bayes Tree $(R,d)$
+ * @param [output] g A VectorValues to store the gradient, which must be preallocated, see allocateVectorValues
+ * @return The gradient as a VectorValues
+ */
+VectorValues gradientAtZero(const BayesTree<GaussianConditional, ISAM2Clique<GaussianConditional> >& bayesTree, VectorValues& g);
+
 }/// namespace gtsam
 
 #include <gtsam/nonlinear/GaussianISAM2-inl.h>

gtsam/nonlinear/Makefile.am

@@ -30,7 +30,8 @@ headers += DoglegOptimizer.h DoglegOptimizer-inl.h
 # Nonlinear iSAM(2)
 headers += NonlinearISAM.h NonlinearISAM-inl.h
 headers += ISAM2.h ISAM2-inl.h ISAM2-impl-inl.h
-headers += GaussianISAM2.h GaussianISAM2-inl.h
+sources += GaussianISAM2.cpp
+headers += GaussianISAM2-inl.h
 
 # Nonlinear constraints 
 headers += NonlinearEquality.h