155 lines
6.2 KiB
C++
155 lines
6.2 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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#include <gtsam/linear/Errors.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/iterative-inl.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/SubgraphPreconditioner.h>
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#include <gtsam/linear/SubgraphSolver.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/inference/graph-inl.h>
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#include <gtsam/base/DSFVector.h>
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#include <boost/foreach.hpp>
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#include <boost/shared_ptr.hpp>
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#include <list>
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using namespace std;
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namespace gtsam {
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters ¶meters, const Ordering& ordering)
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: parameters_(parameters), ordering_(ordering)
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{
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initialize(gfg);
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}
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianFactorGraph::shared_ptr &jfg, const Parameters ¶meters, const Ordering& ordering)
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: parameters_(parameters), ordering_(ordering)
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{
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initialize(*jfg);
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}
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &Ab1, const GaussianFactorGraph &Ab2, const Parameters ¶meters, const Ordering& ordering)
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: parameters_(parameters), ordering_(ordering) {
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GaussianBayesNet::shared_ptr Rc1 = Ab1.eliminateSequential(ordering_, EliminateQR);
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initialize(Rc1, boost::make_shared<GaussianFactorGraph>(Ab2));
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}
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianFactorGraph::shared_ptr &Ab1,
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const GaussianFactorGraph::shared_ptr &Ab2, const Parameters ¶meters, const Ordering& ordering)
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: parameters_(parameters), ordering_(ordering) {
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GaussianBayesNet::shared_ptr Rc1 = Ab1->eliminateSequential(ordering_, EliminateQR);
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initialize(Rc1, Ab2);
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}
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianBayesNet::shared_ptr &Rc1, const GaussianFactorGraph &Ab2,
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const Parameters ¶meters, const Ordering& ordering) : parameters_(parameters), ordering_(ordering)
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{
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initialize(Rc1, boost::make_shared<GaussianFactorGraph>(Ab2));
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}
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/**************************************************************************************************/
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SubgraphSolver::SubgraphSolver(const GaussianBayesNet::shared_ptr &Rc1,
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const GaussianFactorGraph::shared_ptr &Ab2, const Parameters ¶meters, const Ordering& ordering) :
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parameters_(parameters), ordering_(ordering)
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{
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initialize(Rc1, Ab2);
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}
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/**************************************************************************************************/
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VectorValues SubgraphSolver::optimize() {
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VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues, Errors>(*pc_, pc_->zero(), parameters_);
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return pc_->x(ybar);
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}
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/**************************************************************************************************/
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VectorValues SubgraphSolver::optimize(const VectorValues &initial) {
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// the initial is ignored in this case ...
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return optimize();
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}
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/**************************************************************************************************/
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void SubgraphSolver::initialize(const GaussianFactorGraph &jfg)
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{
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GaussianFactorGraph::shared_ptr Ab1 = boost::make_shared<GaussianFactorGraph>(),
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Ab2 = boost::make_shared<GaussianFactorGraph>();
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boost::tie(Ab1, Ab2) = splitGraph(jfg) ;
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if (parameters_.verbosity())
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cout << "Split A into (A1) " << Ab1->size() << " and (A2) " << Ab2->size() << " factors" << endl;
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GaussianBayesNet::shared_ptr Rc1 = Ab1->eliminateSequential(ordering_, EliminateQR);
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VectorValues::shared_ptr xbar = boost::make_shared<VectorValues>(Rc1->optimize());
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pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
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}
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/**************************************************************************************************/
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void SubgraphSolver::initialize(const GaussianBayesNet::shared_ptr &Rc1, const GaussianFactorGraph::shared_ptr &Ab2)
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{
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VectorValues::shared_ptr xbar = boost::make_shared<VectorValues>(Rc1->optimize());
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pc_ = boost::make_shared<SubgraphPreconditioner>(Ab2, Rc1, xbar);
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}
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/**************************************************************************************************/
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boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr>
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SubgraphSolver::splitGraph(const GaussianFactorGraph &jfg) {
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const VariableIndex index(jfg);
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const size_t n = index.size();
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DSFVector D(n);
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GaussianFactorGraph::shared_ptr At(new GaussianFactorGraph());
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GaussianFactorGraph::shared_ptr Ac( new GaussianFactorGraph());
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size_t t = 0;
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BOOST_FOREACH ( const GaussianFactor::shared_ptr &gf, jfg ) {
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if ( gf->keys().size() > 2 ) {
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throw runtime_error("SubgraphSolver::splitGraph the graph is not simple, sanity check failed ");
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}
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bool augment = false ;
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/* check whether this factor should be augmented to the "tree" graph */
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if ( gf->keys().size() == 1 ) augment = true;
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else {
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const Key u = gf->keys()[0], v = gf->keys()[1],
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u_root = D.findSet(u), v_root = D.findSet(v);
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if ( u_root != v_root ) {
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t++; augment = true ;
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D.makeUnionInPlace(u_root, v_root);
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}
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}
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if ( augment ) At->push_back(gf);
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else Ac->push_back(gf);
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}
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return boost::tie(At, Ac);
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}
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/****************************************************************************/
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VectorValues SubgraphSolver::optimize (
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const GaussianFactorGraph &gfg,
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const KeyInfo &keyInfo,
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const std::map<Key, Vector> &lambda,
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const VectorValues &initial
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) { return VectorValues(); }
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} // \namespace gtsam
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