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release/4.3a0
Frank Dellaert 2012-09-04 22:20:40 +00:00
parent a553d2f845
commit c55f4be0d1
1 changed files with 10 additions and 2 deletions
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12
tests/testSubgraphSolver.cpp
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@ -55,9 +55,11 @@ TEST( SubgraphSolver, constructor1 )
size_t N = 3; size_t N = 3;
boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
// The first constructor just takes a factor graph (and parameters)
// and it will split the graph into A1 and A2, where A1 is a spanning tree
SubgraphSolverParameters parameters; SubgraphSolverParameters parameters;
SubgraphSolver solver(Ab, parameters); SubgraphSolver solver(Ab, parameters);
VectorValues optimized = solver.optimize(); VectorValues optimized = solver.optimize(); // does PCG optimization
DOUBLES_EQUAL(0.0, error(Ab, optimized), 1e-5); DOUBLES_EQUAL(0.0, error(Ab, optimized), 1e-5);
} }
@ -74,6 +76,8 @@ TEST( SubgraphSolver, constructor2 )
JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2 JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab); boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
// The second constructor takes two factor graphs,
// so the caller can specify the preconditioner (Ab1) and the constraints that are left out (Ab2)
SubgraphSolverParameters parameters; SubgraphSolverParameters parameters;
SubgraphSolver solver(Ab1_, Ab2_, parameters); SubgraphSolver solver(Ab1_, Ab2_, parameters);
VectorValues optimized = solver.optimize(); VectorValues optimized = solver.optimize();
@ -93,8 +97,12 @@ TEST( SubgraphSolver, constructor3 )
JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2 JacobianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab); boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
GaussianBayesNet::shared_ptr Rc1 = EliminationTree<GaussianFactor>::Create(Ab1_)->eliminate(&EliminateQR); // The caller solves |A1*x-b1|^2 == |R1*x-c1|^2 via QR factorization, where R1 is square UT
GaussianBayesNet::shared_ptr Rc1 = //
EliminationTree<GaussianFactor>::Create(Ab1_)->eliminate(&EliminateQR);
// The third constructor allows the caller to pass an already solved preconditioner Rc1_
// as a Bayes net, in addition to the "loop closing constraints" Ab2, as before
SubgraphSolverParameters parameters; SubgraphSolverParameters parameters;
SubgraphSolver solver(Rc1, Ab2_, parameters); SubgraphSolver solver(Rc1, Ab2_, parameters);
VectorValues optimized = solver.optimize(); VectorValues optimized = solver.optimize();