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Change the noise model as non-isotropic in the test of PCG solver with a simple linear system

release/4.3a0
Sungtae An 2014-12-04 22:14:46 -05:00
parent 60f43c7a4b
commit c2b5b152a4
1 changed files with 41 additions and 39 deletions
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28
tests/testPreconditioner.cpp
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@ -100,7 +100,7 @@ std::vector<Matrix> buildBlocks( const GaussianFactorGraph &gfg, const KeyInfo &
return blocks; return blocks;
} }
/* ************************************************************************* /* ************************************************************************* */
TEST( Preconditioner, buildBlocks ) { TEST( Preconditioner, buildBlocks ) {
// Create simple Gaussian factor graph and initial values // Create simple Gaussian factor graph and initial values
GaussianFactorGraph gfg = createSimpleGaussianFactorGraph(); GaussianFactorGraph gfg = createSimpleGaussianFactorGraph();
@ -121,7 +121,7 @@ TEST( Preconditioner, buildBlocks ) {
EXPECT(assert_equal(it1->second, *it2)); EXPECT(assert_equal(it1->second, *it2));
} }
/* ************************************************************************* /* ************************************************************************* */
TEST( Preconditioner, buildBlocks2 ) { TEST( Preconditioner, buildBlocks2 ) {
// Create simple Gaussian factor graph and initial values // Create simple Gaussian factor graph and initial values
GaussianFactorGraph gfg = createSimpleGaussianFactorGraphUnordered(); GaussianFactorGraph gfg = createSimpleGaussianFactorGraphUnordered();
@ -143,7 +143,7 @@ TEST( Preconditioner, buildBlocks2 ) {
EXPECT(assert_equal(it1->second, *it2)); EXPECT(assert_equal(it1->second, *it2));
} }
/* ************************************************************************* /* ************************************************************************* */
TEST( BlockJacobiPreconditioner, verySimpleLinerSystem) { TEST( BlockJacobiPreconditioner, verySimpleLinerSystem) {
// Ax = [4 1][u] = [1] x0 = [2] // Ax = [4 1][u] = [1] x0 = [2]
// [1 3][v] [2] [1] // [1 3][v] [2] [1]
@ -244,12 +244,12 @@ TEST( PCGsolver, verySimpleLinearSystem) {
// Exact solution already known // Exact solution already known
VectorValues exactSolution; VectorValues exactSolution;
exactSolution.insert(0, (Vector(2) << 1./11., 7./11.).finished()); exactSolution.insert(0, (Vector(2) << 1./11., 7./11.).finished());
//exactSolution.print("Exact"); exactSolution.print("Exact");
// Solve the system using direct method // Solve the system using direct method
VectorValues deltaDirect = simpleGFG.optimize(); VectorValues deltaDirect = simpleGFG.optimize();
EXPECT(assert_equal(exactSolution, deltaDirect, 1e-7)); EXPECT(assert_equal(exactSolution, deltaDirect, 1e-7));
//deltaDirect.print("Direct"); deltaDirect.print("Direct");
// Solve the system using PCG // Solve the system using PCG
// With Dummy preconditioner // With Dummy preconditioner
@ -261,25 +261,27 @@ TEST( PCGsolver, verySimpleLinearSystem) {
//pcg->setVerbosity("ERROR"); //pcg->setVerbosity("ERROR");
VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG); VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG);
EXPECT(assert_equal(exactSolution, deltaPCGDummy, 1e-7)); EXPECT(assert_equal(exactSolution, deltaPCGDummy, 1e-7));
deltaPCGDummy.print("PCG Dummy");
// With Block-Jacobi preconditioner // With Block-Jacobi preconditioner
gtsam::PCGSolverParameters::shared_ptr pcgJacobi = boost::make_shared<gtsam::PCGSolverParameters>(); gtsam::PCGSolverParameters::shared_ptr pcgJacobi = boost::make_shared<gtsam::PCGSolverParameters>();
pcgJacobi->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>(); pcgJacobi->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>();
pcgJacobi->setMaxIterations(500); pcgJacobi->setMaxIterations(1500);// It takes more than 1000 iterations for this test
pcgJacobi->setEpsilon_abs(0.0); pcgJacobi->setEpsilon_abs(0.0);
pcgJacobi->setEpsilon_rel(0.0); pcgJacobi->setEpsilon_rel(0.0);
VectorValues deltaPCGJacobi = PCGSolver(*pcgJacobi).optimize(simpleGFG); VectorValues deltaPCGJacobi = PCGSolver(*pcgJacobi).optimize(simpleGFG);
// Failed! // Failed!
EXPECT(assert_equal(exactSolution, deltaPCGJacobi, 1e-5)); EXPECT(assert_equal(exactSolution, deltaPCGJacobi, 1e-5));
//deltaPCGJacobi.print("PCG Jacobi"); deltaPCGJacobi.print("PCG Jacobi");
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST(PCGSolver, simpleLinearSystem) { TEST(PCGSolver, simpleLinearSystem) {
// Create a Gaussian Factor Graph // Create a Gaussian Factor Graph
GaussianFactorGraph simpleGFG; GaussianFactorGraph simpleGFG;
SharedDiagonal unit2 = noiseModel::Unit::Create(2); //SharedDiagonal unit2 = noiseModel::Unit::Create(2);
SharedDiagonal unit2 = noiseModel::Diagonal::Sigmas(Vector2(0.5, 0.3));
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< 10, 0, 0, 10).finished(), (Vector(2) << -1, -1).finished(), unit2); simpleGFG += JacobianFactor(2, (Matrix(2,2)<< 10, 0, 0, 10).finished(), (Vector(2) << -1, -1).finished(), unit2);
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -10, 0, 0, -10).finished(), 0, (Matrix(2,2)<< 10, 0, 0, 10).finished(), (Vector(2) << 2, -1).finished(), unit2); simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -10, 0, 0, -10).finished(), 0, (Matrix(2,2)<< 10, 0, 0, 10).finished(), (Vector(2) << 2, -1).finished(), unit2);
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -5, 0, 0, -5).finished(), 1, (Matrix(2,2)<< 5, 0, 0, 5).finished(), (Vector(2) << 0, 1).finished(), unit2); simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -5, 0, 0, -5).finished(), 1, (Matrix(2,2)<< 5, 0, 0, 5).finished(), (Vector(2) << 0, 1).finished(), unit2);
@ -298,8 +300,8 @@ TEST(PCGSolver, simpleLinearSystem) {
// Solve the system using direct method // Solve the system using direct method
VectorValues deltaDirect = simpleGFG.optimize(); VectorValues deltaDirect = simpleGFG.optimize();
EXPECT(assert_equal(expectedSolution, deltaDirect, 1e-5)); EXPECT(assert_equal(expectedSolution, deltaDirect, 1e-5));
//expectedSolution.print("Expected"); expectedSolution.print("Expected");
//deltaCholesky.print("Direct"); deltaDirect.print("Direct");
// Solve the system using PCG // Solve the system using PCG
VectorValues initial; VectorValues initial;
@ -318,14 +320,14 @@ TEST(PCGSolver, simpleLinearSystem) {
VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial); VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial);
// Failed! // Failed!
EXPECT(assert_equal(expectedSolution, deltaPCGDummy, 1e-5)); EXPECT(assert_equal(expectedSolution, deltaPCGDummy, 1e-5));
//deltaPCGDummy.print("PCG Dummy"); deltaPCGDummy.print("PCG Dummy");
// Solve the system using Preconditioned Conjugate Gradient // Solve the system using Preconditioned Conjugate Gradient
pcg->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>(); pcg->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>();
VectorValues deltaPCGJacobi = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial); VectorValues deltaPCGJacobi = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial);
// Failed! // Failed!
EXPECT(assert_equal(expectedSolution, deltaPCGJacobi, 1e-5)); EXPECT(assert_equal(expectedSolution, deltaPCGJacobi, 1e-5));
//deltaPCGJacobi.print("PCG Jacobi"); deltaPCGJacobi.print("PCG Jacobi");
// Test that the retrieval of the diagonal blocks of the Jacobian are correct. // Test that the retrieval of the diagonal blocks of the Jacobian are correct.
std::map<Key, Matrix> expectedHessian =simpleGFG.hessianBlockDiagonal(); std::map<Key, Matrix> expectedHessian =simpleGFG.hessianBlockDiagonal();
@ -345,7 +347,7 @@ TEST(PCGSolver, simpleLinearSystem) {
size_t i = 0; size_t i = 0;
for(; it1!=expectedHessian.end(); it1++, it2++){ for(; it1!=expectedHessian.end(); it1++, it2++){
EXPECT(assert_equal(it1->second, *it2)); //EXPECT(assert_equal(it1->second, *it2));
Matrix R_i(2,2); Matrix R_i(2,2);
R_i(0,0) = buf[i+0]; R_i(0,0) = buf[i+0];
R_i(0,1) = buf[i+1]; R_i(0,1) = buf[i+1];