Made SPCG unit tests compile again, needed several fixes in iterative.h
Frank Dellaert
parent
a058ecb692
commit
ab7594e8f0
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@ -48,6 +48,7 @@ namespace gtsam {
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public:
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SubgraphPreconditioner();
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/**
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* Constructor
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* @param Ab1: the Graph A1*x=b1
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@ -55,7 +56,8 @@ namespace gtsam {
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* @param Rc1: the Bayes Net R1*x=c1
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* @param xbar: the solution to R1*x=c1
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*/
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SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2, const sharedBayesNet& Rc1, const sharedValues& xbar);
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SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2,
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const sharedBayesNet& Rc1, const sharedValues& xbar);
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/** Access Ab1 */
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const sharedFG& Ab1() const { return Ab1_; }
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@ -80,12 +82,12 @@ namespace gtsam {
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/* A zero VectorValues with the structure of xbar */
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VectorValues zero() const {
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VectorValues V(VectorValues::Zero(*xbar_)) ;
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VectorValues V(VectorValues::Zero(*xbar_));
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return V ;
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}
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/**
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* Add constraint part of the error only, used in both calls above
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* Add constraint part of the error only
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* y += alpha*inv(R1')*A2'*e2
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* Takes a range indicating e2 !!!!
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*/
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@ -122,7 +122,7 @@ namespace gtsam {
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// conjugate gradient method.
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// S: linear system, V: step vector, E: errors
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template<class S, class V, class E>
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V conjugateGradients(const S& Ab, V x, const ConjugateGradientParameters ¶meters, bool steepest = false) {
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V conjugateGradients(const S& Ab, V x, const ConjugateGradientParameters ¶meters, bool steepest) {
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CGState<S, V, E> state(Ab, x, parameters, steepest);
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@ -22,7 +22,6 @@
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/linear/IterativeSolver.h>
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#include <iostream>
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using namespace std;
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@ -30,7 +29,7 @@ using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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void System::print (const string& s) const {
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void System::print(const string& s) const {
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cout << s << endl;
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gtsam::print(A_, "A");
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gtsam::print(b_, "b");
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@ -38,34 +37,42 @@ namespace gtsam {
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/* ************************************************************************* */
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Vector steepestDescent(const System& Ab, const Vector& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<System, Vector, Vector> (Ab, x, parameters, true);
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Vector steepestDescent(const System& Ab, const Vector& x,
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const ConjugateGradientParameters & parameters) {
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return conjugateGradients<System, Vector, Vector>(Ab, x, parameters, true);
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}
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Vector conjugateGradientDescent(const System& Ab, const Vector& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<System, Vector, Vector> (Ab, x, parameters);
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Vector conjugateGradientDescent(const System& Ab, const Vector& x,
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const ConjugateGradientParameters & parameters) {
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return conjugateGradients<System, Vector, Vector>(Ab, x, parameters);
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}
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/* ************************************************************************* */
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Vector steepestDescent(const Matrix& A, const Vector& b, const Vector& x, const ConjugateGradientParameters & parameters) {
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Vector steepestDescent(const Matrix& A, const Vector& b, const Vector& x,
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const ConjugateGradientParameters & parameters) {
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System Ab(A, b);
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return conjugateGradients<System, Vector, Vector> (Ab, x, parameters, true);
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return conjugateGradients<System, Vector, Vector>(Ab, x, parameters, true);
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}
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Vector conjugateGradientDescent(const Matrix& A, const Vector& b, const Vector& x, const ConjugateGradientParameters & parameters) {
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Vector conjugateGradientDescent(const Matrix& A, const Vector& b,
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const Vector& x, const ConjugateGradientParameters & parameters) {
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System Ab(A, b);
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return conjugateGradients<System, Vector, Vector> (Ab, x, parameters);
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return conjugateGradients<System, Vector, Vector>(Ab, x, parameters);
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}
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/* ************************************************************************* */
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VectorValues steepestDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors> (fg, x, parameters, true);
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VectorValues steepestDescent(const FactorGraph<JacobianFactor>& fg,
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const VectorValues& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors>(
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fg, x, parameters, true);
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}
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VectorValues conjugateGradientDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors> (fg, x, parameters);
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VectorValues conjugateGradientDescent(const FactorGraph<JacobianFactor>& fg,
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const VectorValues& x, const ConjugateGradientParameters & parameters) {
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return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors>(
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fg, x, parameters);
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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} // namespace gtsam
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@ -31,12 +31,11 @@ namespace gtsam {
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* "Vector" class E needs dot(v,v)
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* @param Ab, the "system" that needs to be solved, examples below
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* @param x is the initial estimate
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* @param epsilon determines the convergence criterion: norm(g)<epsilon*norm(g0)
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* @param maxIterations, if 0 will be set to |x|
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* @param steepest flag, if true does steepest descent, not CG
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* */
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template<class S, class V, class E>
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V conjugateGradients(const S& Ab, V x, bool verbose, double epsilon, size_t maxIterations, bool steepest = false);
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V conjugateGradients(const S& Ab, V x,
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const ConjugateGradientParameters ¶meters, bool steepest = false);
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/**
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* Helper class encapsulating the combined system |Ax-b_|^2
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@ -127,21 +126,19 @@ namespace gtsam {
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const Vector& x,
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const ConjugateGradientParameters & parameters);
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class GaussianFactorGraph;
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/**
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* Method of steepest gradients, Gaussian Factor Graph version
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* */
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*/
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VectorValues steepestDescent(
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const GaussianFactorGraph& fg,
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const FactorGraph<JacobianFactor>& fg,
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const VectorValues& x,
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const ConjugateGradientParameters & parameters);
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/**
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* Method of conjugate gradients (CG), Gaussian Factor Graph version
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* */
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*/
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VectorValues conjugateGradientDescent(
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const GaussianFactorGraph& fg,
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const FactorGraph<JacobianFactor>& fg,
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const VectorValues& x,
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const ConjugateGradientParameters & parameters);
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@ -421,7 +421,7 @@ namespace example {
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}
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/* ************************************************************************* */
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boost::tuple<GaussianFactorGraph, VectorValues> planarGraph(size_t N) {
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boost::tuple<JacobianFactorGraph, VectorValues> planarGraph(size_t N) {
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// create empty graph
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NonlinearFactorGraph nlfg;
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@ -458,7 +458,13 @@ namespace example {
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xtrue[ordering[key(x, y)]] = Point2(x,y).vector();
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// linearize around zero
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return boost::make_tuple(*nlfg.linearize(zeros, ordering), xtrue);
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boost::shared_ptr<GaussianFactorGraph> gfg = nlfg.linearize(zeros, ordering);
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JacobianFactorGraph jfg;
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BOOST_FOREACH(GaussianFactorGraph::sharedFactor factor, *gfg)
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jfg.push_back(boost::dynamic_pointer_cast<JacobianFactor>(factor));
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return boost::make_tuple(jfg, xtrue);
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}
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/* ************************************************************************* */
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@ -476,21 +482,21 @@ namespace example {
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JacobianFactorGraph T, C;
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// Add the x11 constraint to the tree
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T.push_back(original[0]);
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T.push_back(boost::dynamic_pointer_cast<JacobianFactor>(original[0]));
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// Add all horizontal constraints to the tree
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size_t i = 1;
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for (size_t x = 1; x < N; x++)
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for (size_t y = 1; y <= N; y++, i++)
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T.push_back(original[i]);
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T.push_back(boost::dynamic_pointer_cast<JacobianFactor>(original[i]));
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// Add first vertical column of constraints to T, others to C
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for (size_t x = 1; x <= N; x++)
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for (size_t y = 1; y < N; y++, i++)
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if (x == 1)
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T.push_back(original[i]);
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T.push_back(boost::dynamic_pointer_cast<JacobianFactor>(original[i]));
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else
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C.push_back(original[i]);
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C.push_back(boost::dynamic_pointer_cast<JacobianFactor>(original[i]));
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return make_pair(T, C);
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}
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@ -130,7 +130,7 @@ namespace gtsam {
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* -x11-x21-x31
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* with x11 clamped at (1,1), and others related by 2D odometry.
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*/
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boost::tuple<GaussianFactorGraph, VectorValues> planarGraph(size_t N);
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boost::tuple<JacobianFactorGraph, VectorValues> planarGraph(size_t N);
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/*
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* Create canonical ordering for planar graph that also works for tree
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@ -17,6 +17,7 @@
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#include <tests/smallExample.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/linear/iterative.h>
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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@ -35,30 +36,41 @@ using namespace gtsam;
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using namespace example;
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// define keys
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Key i3003 = 3003, i2003 = 2003, i1003 = 1003;
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Key i3002 = 3002, i2002 = 2002, i1002 = 1002;
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Key i3001 = 3001, i2001 = 2001, i1001 = 1001;
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// Create key for simulated planar graph
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Symbol key(int x, int y) {
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return symbol_shorthand::X(1000*x+y);
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}
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// TODO fix Ordering::equals, because the ordering *is* correct !
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, planarOrdering )
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//{
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// // Check canonical ordering
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// Ordering expected, ordering = planarOrdering(3);
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// expected += i3003, i2003, i1003, i3002, i2002, i1002, i3001, i2001, i1001;
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// CHECK(assert_equal(expected,ordering));
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//}
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TEST( SubgraphPreconditioner, planarOrdering ) {
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// Check canonical ordering
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Ordering expected, ordering = planarOrdering(3);
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expected +=
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key(3, 3), key(2, 3), key(1, 3),
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key(3, 2), key(2, 2), key(1, 2),
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key(3, 1), key(2, 1), key(1, 1);
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CHECK(assert_equal(expected,ordering));
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}
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/* ************************************************************************* */
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/** unnormalized error */
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double error(const JacobianFactorGraph& fg, const VectorValues& x) {
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double total_error = 0.;
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BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg)
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total_error += factor->error(x);
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return total_error;
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}
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/* ************************************************************************* */
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TEST( SubgraphPreconditioner, planarGraph )
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{
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// Check planar graph construction
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GaussianFactorGraph A;
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JacobianFactorGraph A;
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VectorValues xtrue;
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boost::tie(A, xtrue) = planarGraph(3);
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LONGS_EQUAL(13,A.size());
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LONGS_EQUAL(9,xtrue.size());
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DOUBLES_EQUAL(0,A.error(xtrue),1e-9); // check zero error for xtrue
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DOUBLES_EQUAL(0,error(A,xtrue),1e-9); // check zero error for xtrue
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// Check that xtrue is optimal
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GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(A).eliminate();
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@ -67,143 +79,143 @@ TEST( SubgraphPreconditioner, planarGraph )
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}
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, splitOffPlanarTree )
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//{
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// // Build a planar graph
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// GaussianFactorGraph A;
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// VectorValues xtrue;
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// boost::tie(A, xtrue) = planarGraph(3);
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//
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// // Get the spanning tree and constraints, and check their sizes
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// JacobianFactorGraph T, C;
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// // TODO big mess: GFG and JFG mess !!!
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// boost::tie(T, C) = splitOffPlanarTree(3, A);
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// LONGS_EQUAL(9,T.size());
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// LONGS_EQUAL(4,C.size());
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//
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// // Check that the tree can be solved to give the ground xtrue
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// GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(T).eliminate();
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// VectorValues xbar = optimize(*R1);
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// CHECK(assert_equal(xtrue,xbar));
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//}
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TEST( SubgraphPreconditioner, splitOffPlanarTree )
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{
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// Build a planar graph
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JacobianFactorGraph A;
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VectorValues xtrue;
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boost::tie(A, xtrue) = planarGraph(3);
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// Get the spanning tree and constraints, and check their sizes
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JacobianFactorGraph T, C;
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boost::tie(T, C) = splitOffPlanarTree(3, A);
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LONGS_EQUAL(9,T.size());
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LONGS_EQUAL(4,C.size());
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// Check that the tree can be solved to give the ground xtrue
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GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(T).eliminate();
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VectorValues xbar = optimize(*R1);
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CHECK(assert_equal(xtrue,xbar));
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}
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, system )
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//{
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// // Build a planar graph
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// JacobianFactorGraph Ab;
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// VectorValues xtrue;
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// size_t N = 3;
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// boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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//
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// // Get the spanning tree and corresponding ordering
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// GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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// boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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// SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
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// SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
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//
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// // Eliminate the spanning tree to build a prior
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// SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
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// VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
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//
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// // Create Subgraph-preconditioned system
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// VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
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// SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
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//
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// // Create zero config
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// VectorValues zeros = VectorValues::Zero(xbar);
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//
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// // Set up y0 as all zeros
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// VectorValues y0 = zeros;
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//
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// // y1 = perturbed y0
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// VectorValues y1 = zeros;
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// y1[i2003] = Vector_(2, 1.0, -1.0);
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//
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// // Check corresponding x values
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// VectorValues expected_x1 = xtrue, x1 = system.x(y1);
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// expected_x1[i2003] = Vector_(2, 2.01, 2.99);
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// expected_x1[i3003] = Vector_(2, 3.01, 2.99);
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// CHECK(assert_equal(xtrue, system.x(y0)));
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// CHECK(assert_equal(expected_x1,system.x(y1)));
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//
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// // Check errors
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//// DOUBLES_EQUAL(0,error(Ab,xtrue),1e-9); // TODO !
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//// DOUBLES_EQUAL(3,error(Ab,x1),1e-9); // TODO !
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// DOUBLES_EQUAL(0,error(system,y0),1e-9);
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// DOUBLES_EQUAL(3,error(system,y1),1e-9);
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//
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// // Test gradient in x
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// VectorValues expected_gx0 = zeros;
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// VectorValues expected_gx1 = zeros;
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// CHECK(assert_equal(expected_gx0,gradient(Ab,xtrue)));
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// expected_gx1[i1003] = Vector_(2, -100., 100.);
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// expected_gx1[i2002] = Vector_(2, -100., 100.);
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// expected_gx1[i2003] = Vector_(2, 200., -200.);
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// expected_gx1[i3002] = Vector_(2, -100., 100.);
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// expected_gx1[i3003] = Vector_(2, 100., -100.);
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// CHECK(assert_equal(expected_gx1,gradient(Ab,x1)));
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//
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// // Test gradient in y
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// VectorValues expected_gy0 = zeros;
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// VectorValues expected_gy1 = zeros;
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// expected_gy1[i1003] = Vector_(2, 2., -2.);
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// expected_gy1[i2002] = Vector_(2, -2., 2.);
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// expected_gy1[i2003] = Vector_(2, 3., -3.);
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// expected_gy1[i3002] = Vector_(2, -1., 1.);
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// expected_gy1[i3003] = Vector_(2, 1., -1.);
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// CHECK(assert_equal(expected_gy0,gradient(system,y0)));
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// CHECK(assert_equal(expected_gy1,gradient(system,y1)));
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//
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// // Check it numerically for good measure
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// // TODO use boost::bind(&SubgraphPreconditioner::error,&system,_1)
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// // Vector numerical_g1 = numericalGradient<VectorValues> (error, y1, 0.001);
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// // Vector expected_g1 = Vector_(18, 0., 0., 0., 0., 2., -2., 0., 0., -2., 2.,
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// // 3., -3., 0., 0., -1., 1., 1., -1.);
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// // CHECK(assert_equal(expected_g1,numerical_g1));
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//}
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TEST( SubgraphPreconditioner, system )
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{
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// Build a planar graph
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JacobianFactorGraph Ab;
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VectorValues xtrue;
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size_t N = 3;
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boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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// Get the spanning tree and corresponding ordering
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JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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SubgraphPreconditioner::sharedFG Ab1(new JacobianFactorGraph(Ab1_));
|
||||
SubgraphPreconditioner::sharedFG Ab2(new JacobianFactorGraph(Ab2_));
|
||||
|
||||
// Eliminate the spanning tree to build a prior
|
||||
SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
|
||||
VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
|
||||
|
||||
// Create Subgraph-preconditioned system
|
||||
VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
|
||||
SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
|
||||
|
||||
// Create zero config
|
||||
VectorValues zeros = VectorValues::Zero(xbar);
|
||||
|
||||
// Set up y0 as all zeros
|
||||
VectorValues y0 = zeros;
|
||||
|
||||
// y1 = perturbed y0
|
||||
VectorValues y1 = zeros;
|
||||
y1[1] = Vector_(2, 1.0, -1.0);
|
||||
|
||||
// Check corresponding x values
|
||||
VectorValues expected_x1 = xtrue, x1 = system.x(y1);
|
||||
expected_x1[1] = Vector_(2, 2.01, 2.99);
|
||||
expected_x1[0] = Vector_(2, 3.01, 2.99);
|
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CHECK(assert_equal(xtrue, system.x(y0)));
|
||||
CHECK(assert_equal(expected_x1,system.x(y1)));
|
||||
|
||||
// Check errors
|
||||
DOUBLES_EQUAL(0,error(Ab,xtrue),1e-9);
|
||||
DOUBLES_EQUAL(3,error(Ab,x1),1e-9);
|
||||
DOUBLES_EQUAL(0,error(system,y0),1e-9);
|
||||
DOUBLES_EQUAL(3,error(system,y1),1e-9);
|
||||
|
||||
// Test gradient in x
|
||||
VectorValues expected_gx0 = zeros;
|
||||
VectorValues expected_gx1 = zeros;
|
||||
CHECK(assert_equal(expected_gx0,gradient(Ab,xtrue)));
|
||||
expected_gx1[2] = Vector_(2, -100., 100.);
|
||||
expected_gx1[4] = Vector_(2, -100., 100.);
|
||||
expected_gx1[1] = Vector_(2, 200., -200.);
|
||||
expected_gx1[3] = Vector_(2, -100., 100.);
|
||||
expected_gx1[0] = Vector_(2, 100., -100.);
|
||||
CHECK(assert_equal(expected_gx1,gradient(Ab,x1)));
|
||||
|
||||
// Test gradient in y
|
||||
VectorValues expected_gy0 = zeros;
|
||||
VectorValues expected_gy1 = zeros;
|
||||
expected_gy1[2] = Vector_(2, 2., -2.);
|
||||
expected_gy1[4] = Vector_(2, -2., 2.);
|
||||
expected_gy1[1] = Vector_(2, 3., -3.);
|
||||
expected_gy1[3] = Vector_(2, -1., 1.);
|
||||
expected_gy1[0] = Vector_(2, 1., -1.);
|
||||
CHECK(assert_equal(expected_gy0,gradient(system,y0)));
|
||||
CHECK(assert_equal(expected_gy1,gradient(system,y1)));
|
||||
|
||||
// Check it numerically for good measure
|
||||
// TODO use boost::bind(&SubgraphPreconditioner::error,&system,_1)
|
||||
// Vector numerical_g1 = numericalGradient<VectorValues> (error, y1, 0.001);
|
||||
// Vector expected_g1 = Vector_(18, 0., 0., 0., 0., 2., -2., 0., 0., -2., 2.,
|
||||
// 3., -3., 0., 0., -1., 1., 1., -1.);
|
||||
// CHECK(assert_equal(expected_g1,numerical_g1));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//TEST( SubgraphPreconditioner, conjugateGradients )
|
||||
//{
|
||||
// // Build a planar graph
|
||||
// GaussianFactorGraph Ab;
|
||||
// VectorValues xtrue;
|
||||
// size_t N = 3;
|
||||
// boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
|
||||
//
|
||||
// // Get the spanning tree and corresponding ordering
|
||||
// GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
|
||||
// boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
|
||||
// SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
|
||||
// SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
|
||||
//
|
||||
// // Eliminate the spanning tree to build a prior
|
||||
// Ordering ordering = planarOrdering(N);
|
||||
// SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
|
||||
// VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
|
||||
//
|
||||
// // Create Subgraph-preconditioned system
|
||||
// VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
|
||||
// SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
|
||||
//
|
||||
// // Create zero config y0 and perturbed config y1
|
||||
// VectorValues y0 = VectorValues::Zero(xbar);
|
||||
//
|
||||
// VectorValues y1 = y0;
|
||||
// y1[i2003] = Vector_(2, 1.0, -1.0);
|
||||
// VectorValues x1 = system.x(y1);
|
||||
//
|
||||
// // Solve for the remaining constraints using PCG
|
||||
// ConjugateGradientParameters parameters;
|
||||
//// VectorValues actual = gtsam::conjugateGradients<SubgraphPreconditioner,
|
||||
//// VectorValues, Errors>(system, y1, verbose, epsilon, epsilon, maxIterations);
|
||||
//// CHECK(assert_equal(y0,actual));
|
||||
//
|
||||
// // Compare with non preconditioned version:
|
||||
// VectorValues actual2 = conjugateGradientDescent(Ab, x1, parameters);
|
||||
// CHECK(assert_equal(xtrue,actual2,1e-4));
|
||||
//}
|
||||
TEST( SubgraphPreconditioner, conjugateGradients )
|
||||
{
|
||||
// Build a planar graph
|
||||
JacobianFactorGraph Ab;
|
||||
VectorValues xtrue;
|
||||
size_t N = 3;
|
||||
boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
|
||||
|
||||
// Get the spanning tree and corresponding ordering
|
||||
JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
|
||||
boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
|
||||
SubgraphPreconditioner::sharedFG Ab1(new JacobianFactorGraph(Ab1_));
|
||||
SubgraphPreconditioner::sharedFG Ab2(new JacobianFactorGraph(Ab2_));
|
||||
|
||||
// Eliminate the spanning tree to build a prior
|
||||
Ordering ordering = planarOrdering(N);
|
||||
SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
|
||||
VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
|
||||
|
||||
// Create Subgraph-preconditioned system
|
||||
VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
|
||||
SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
|
||||
|
||||
// Create zero config y0 and perturbed config y1
|
||||
VectorValues y0 = VectorValues::Zero(xbar);
|
||||
|
||||
VectorValues y1 = y0;
|
||||
y1[1] = Vector_(2, 1.0, -1.0);
|
||||
VectorValues x1 = system.x(y1);
|
||||
|
||||
// Solve for the remaining constraints using PCG
|
||||
ConjugateGradientParameters parameters;
|
||||
VectorValues actual = conjugateGradients<SubgraphPreconditioner,
|
||||
VectorValues, Errors>(system, y1, parameters);
|
||||
CHECK(assert_equal(y0,actual));
|
||||
|
||||
// Compare with non preconditioned version:
|
||||
VectorValues actual2 = conjugateGradientDescent(Ab, x1, parameters);
|
||||
CHECK(assert_equal(xtrue,actual2,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
||||
|
|
|
|||
Loading…
Reference in New Issue