Add tests for preconditioner and solver
Sungtae An
parent
6c3df407a1
commit
332b3f9da9
|
|
@ -34,11 +34,26 @@ static GaussianFactorGraph createSimpleGaussianFactorGraph() {
|
|||
// linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
|
||||
fg += JacobianFactor(2, 10*eye(2), -1.0*ones(2), unit2);
|
||||
// odometry between x1 and x2: x2-x1=[0.2;-0.1]
|
||||
fg += JacobianFactor(2, -10*eye(2), 0, 10*eye(2), Vector2(2.0, -1.0), unit2);
|
||||
fg += JacobianFactor(2, -10*eye(2), 0, 10*eye(2), (Vector(2) << 2.0, -1.0), unit2);
|
||||
// measurement between x1 and l1: l1-x1=[0.0;0.2]
|
||||
fg += JacobianFactor(2, -5*eye(2), 1, 5*eye(2), Vector2(0.0, 1.0), unit2);
|
||||
fg += JacobianFactor(2, -5*eye(2), 1, 5*eye(2), (Vector(2) << 0.0, 1.0), unit2);
|
||||
// measurement between x2 and l1: l1-x2=[-0.2;0.3]
|
||||
fg += JacobianFactor(0, -5*eye(2), 1, 5*eye(2), Vector2(-1.0, 1.5), unit2);
|
||||
fg += JacobianFactor(0, -5*eye(2), 1, 5*eye(2), (Vector(2) << -1.0, 1.5), unit2);
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
static GaussianFactorGraph createSimpleGaussianFactorGraphUnordered() {
|
||||
GaussianFactorGraph fg;
|
||||
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
|
||||
// linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_
|
||||
fg += JacobianFactor(2, 10*eye(2), -1.0*ones(2), unit2);
|
||||
// odometry between x1 and x2: x2-x1=[0.2;-0.1]
|
||||
fg += JacobianFactor(2, -10*eye(2), 1, 10*eye(2), (Vector(2) << 2.0, -1.0), unit2);
|
||||
// measurement between x1 and l1: l1-x1=[0.0;0.2]
|
||||
fg += JacobianFactor(2, -5*eye(2), 1, 5*eye(2), (Vector(2) << 0.0, 1.0), unit2);
|
||||
// measurement between x2 and l1: l1-x2=[-0.2;0.3]
|
||||
fg += JacobianFactor(0, -5*eye(2), 1, 5*eye(2), (Vector(2) << -1.0, 1.5), unit2);
|
||||
return fg;
|
||||
}
|
||||
|
||||
|
|
@ -89,10 +104,6 @@ std::vector<Matrix> buildBlocks( const GaussianFactorGraph &gfg, const KeyInfo &
|
|||
TEST( Preconditioner, buildBlocks ) {
|
||||
// Create simple Gaussian factor graph and initial values
|
||||
GaussianFactorGraph gfg = createSimpleGaussianFactorGraph();
|
||||
Values initial;
|
||||
initial.insert(0,Point2(4, 5));
|
||||
initial.insert(1,Point2(0, 1));
|
||||
initial.insert(2,Point2(-5, 7));
|
||||
|
||||
// Expected Hessian block diagonal matrices
|
||||
std::map<Key, Matrix> expectedHessian =gfg.hessianBlockDiagonal();
|
||||
|
|
@ -110,6 +121,204 @@ TEST( Preconditioner, buildBlocks ) {
|
|||
EXPECT(assert_equal(it1->second, *it2));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( Preconditioner, buildBlocks2 ) {
|
||||
// Create simple Gaussian factor graph and initial values
|
||||
GaussianFactorGraph gfg = createSimpleGaussianFactorGraphUnordered();
|
||||
|
||||
// Expected Hessian block diagonal matrices
|
||||
std::map<Key, Matrix> expectedHessian =gfg.hessianBlockDiagonal();
|
||||
|
||||
// Actual Hessian block diagonal matrices from BlockJacobiPreconditioner::build
|
||||
std::vector<Matrix> actualHessian = buildBlocks(gfg, KeyInfo(gfg));
|
||||
|
||||
// Compare the number of block diagonal matrices
|
||||
EXPECT_LONGS_EQUAL(expectedHessian.size(), 3);
|
||||
EXPECT_LONGS_EQUAL(expectedHessian.size(), actualHessian.size());
|
||||
|
||||
// Compare the values of matrices
|
||||
std::map<Key, Matrix>::const_iterator it1 = expectedHessian.begin();
|
||||
std::vector<Matrix>::const_iterator it2 = actualHessian.begin();
|
||||
for(; it1!=expectedHessian.end(); it1++, it2++)
|
||||
EXPECT(assert_equal(it1->second, *it2));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( BlockJacobiPreconditioner, verySimpleLinerSystem) {
|
||||
// Ax = [4 1][u] = [1] x0 = [2]
|
||||
// [1 3][v] [2] [1]
|
||||
//
|
||||
// exact solution x = [1/11, 7/11]';
|
||||
//
|
||||
|
||||
// Create a Gaussian Factor Graph
|
||||
GaussianFactorGraph simpleGFG;
|
||||
simpleGFG += JacobianFactor(0, (Matrix(2,2)<< 4, 1, 1, 3), (Vector(2) << 1,2 ), noiseModel::Unit::Create(2));
|
||||
//simpleGFG.print("Factors\n");
|
||||
|
||||
// Expected Hessian block diagonal matrices
|
||||
std::map<Key, Matrix> expectedHessian =simpleGFG.hessianBlockDiagonal();
|
||||
// Actual Hessian block diagonal matrices from BlockJacobiPreconditioner::build
|
||||
std::vector<Matrix> actualHessian = buildBlocks(simpleGFG, KeyInfo(simpleGFG));
|
||||
// Compare the number of block diagonal matrices
|
||||
EXPECT_LONGS_EQUAL(expectedHessian.size(), actualHessian.size());
|
||||
|
||||
// Compare the values of matrices
|
||||
std::map<Key, Matrix>::const_iterator it1 = expectedHessian.begin();
|
||||
std::vector<Matrix>::const_iterator it2 = actualHessian.begin();
|
||||
|
||||
// the function 'build' in BlockJacobianPreconditioner stores in 'buffer'
|
||||
// the cholesky decomposion of each block of the hessian
|
||||
// In this example there is a single block (i.e., a single value)
|
||||
// and the corresponding block of the Hessian is
|
||||
//
|
||||
// H0 = [17 7; 7 10]
|
||||
//
|
||||
EXPECT(assert_equal(it1->second, *it2));
|
||||
// TODO: Matrix expectedH0 ...
|
||||
//EXPECT(assert_equal(it1->second, *it2));
|
||||
|
||||
// The corresponding Cholesky decomposition is:
|
||||
// R = chol(H0) = [4.1231 1.6977 0 2.6679] (from Matlab)
|
||||
Preconditioner::shared_ptr preconditioner = createPreconditioner(boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>());
|
||||
preconditioner->build(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>());
|
||||
boost::shared_ptr<BlockJacobiPreconditioner> blockJacobi = boost::dynamic_pointer_cast<BlockJacobiPreconditioner>(preconditioner);
|
||||
double* buf = blockJacobi->getBuffer();
|
||||
for(int i=0; i<4; ++i){}
|
||||
// TODO: EXPECT(assert_equal(number..,buf[i]));
|
||||
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
TEST( PCGsolver, verySimpleLinearSystem) {
|
||||
|
||||
// Ax = [4 1][u] = [1] x0 = [2]
|
||||
// [1 3][v] [2] [1]
|
||||
//
|
||||
// exact solution x = [1/11, 7/11]';
|
||||
//
|
||||
|
||||
// Create a Gaussian Factor Graph
|
||||
GaussianFactorGraph simpleGFG;
|
||||
simpleGFG += JacobianFactor(0, (Matrix(2,2)<< 4, 1, 1, 3), (Vector(2) << 1,2 ), noiseModel::Unit::Create(2));
|
||||
//simpleGFG.print("Factors\n");
|
||||
|
||||
// Exact solution already known
|
||||
VectorValues exactSolution;
|
||||
exactSolution.insert(0, (Vector(2) << 1./11., 7./11.));
|
||||
//exactSolution.print("Exact");
|
||||
|
||||
// Solve the system using direct method
|
||||
VectorValues deltaDirect = simpleGFG.optimize();
|
||||
EXPECT(assert_equal(exactSolution, deltaDirect, 1e-7));
|
||||
//deltaDirect.print("Direct");
|
||||
|
||||
// Solve the system using PCG
|
||||
// With Dummy preconditioner
|
||||
gtsam::PCGSolverParameters::shared_ptr pcg = boost::make_shared<gtsam::PCGSolverParameters>();
|
||||
pcg->preconditioner_ = boost::make_shared<gtsam::DummyPreconditionerParameters>();
|
||||
pcg->setMaxIterations(500);
|
||||
pcg->setEpsilon_abs(0.0);
|
||||
pcg->setEpsilon_rel(0.0);
|
||||
//pcg->setVerbosity("ERROR");
|
||||
VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG);
|
||||
EXPECT(assert_equal(exactSolution, deltaPCGDummy, 1e-7));
|
||||
|
||||
// With Block-Jacobi preconditioner
|
||||
gtsam::PCGSolverParameters::shared_ptr pcgJacobi = boost::make_shared<gtsam::PCGSolverParameters>();
|
||||
pcgJacobi->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>();
|
||||
pcgJacobi->setMaxIterations(500);
|
||||
pcgJacobi->setEpsilon_abs(0.0);
|
||||
pcgJacobi->setEpsilon_rel(0.0);
|
||||
VectorValues deltaPCGJacobi = PCGSolver(*pcgJacobi).optimize(simpleGFG);
|
||||
|
||||
// Failed!
|
||||
EXPECT(assert_equal(exactSolution, deltaPCGJacobi, 1e-5));
|
||||
//deltaPCGJacobi.print("PCG Jacobi");
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST(PCGSolver, simpleLinearSystem) {
|
||||
// Create a Gaussian Factor Graph
|
||||
GaussianFactorGraph simpleGFG;
|
||||
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
|
||||
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< 10, 0, 0, 10), (Vector(2) << -1, -1), unit2);
|
||||
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -10, 0, 0, -10), 0, (Matrix(2,2)<< 10, 0, 0, 10), (Vector(2) << 2, -1), unit2);
|
||||
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< -5, 0, 0, -5), 1, (Matrix(2,2)<< 5, 0, 0, 5), (Vector(2) << 0, 1), unit2);
|
||||
simpleGFG += JacobianFactor(0, (Matrix(2,2)<< -5, 0, 0, -5), 1, (Matrix(2,2)<< 5, 0, 0, 5), (Vector(2) << -1, 1.5), unit2);
|
||||
simpleGFG += JacobianFactor(0, (Matrix(2,2)<< 1, 0, 0, 1), (Vector(2) << 0, 0), unit2);
|
||||
simpleGFG += JacobianFactor(1, (Matrix(2,2)<< 1, 0, 0, 1), (Vector(2) << 0, 0), unit2);
|
||||
simpleGFG += JacobianFactor(2, (Matrix(2,2)<< 1, 0, 0, 1), (Vector(2) << 0, 0), unit2);
|
||||
//simpleGFG.print("Factors\n");
|
||||
|
||||
// Expected solution
|
||||
VectorValues expectedSolution;
|
||||
expectedSolution.insert(0, (Vector(2) << 0.100498, -0.196756));
|
||||
expectedSolution.insert(2, (Vector(2) << -0.0990413, -0.0980577));
|
||||
expectedSolution.insert(1, (Vector(2) << -0.0973252, 0.100582));
|
||||
|
||||
// Solve the system using direct method
|
||||
VectorValues deltaDirect = simpleGFG.optimize();
|
||||
EXPECT(assert_equal(expectedSolution, deltaDirect, 1e-5));
|
||||
//expectedSolution.print("Expected");
|
||||
//deltaCholesky.print("Direct");
|
||||
|
||||
// Solve the system using PCG
|
||||
VectorValues initial;
|
||||
initial.insert(0, (Vector(2) << 0.1, -0.1));
|
||||
initial.insert(1, (Vector(2) << -0.1, 0.1));
|
||||
initial.insert(2, (Vector(2) << -0.1, -0.1));
|
||||
|
||||
// With Dummy preconditioner
|
||||
gtsam::PCGSolverParameters::shared_ptr pcg = boost::make_shared<gtsam::PCGSolverParameters>();
|
||||
pcg->preconditioner_ = boost::make_shared<gtsam::DummyPreconditionerParameters>();
|
||||
pcg->setMaxIterations(500);
|
||||
pcg->setEpsilon_abs(0.0);
|
||||
pcg->setEpsilon_rel(0.0);
|
||||
//pcg->setVerbosity("ERROR");
|
||||
|
||||
VectorValues deltaPCGDummy = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial);
|
||||
// Failed!
|
||||
EXPECT(assert_equal(expectedSolution, deltaPCGDummy, 1e-5));
|
||||
//deltaPCGDummy.print("PCG Dummy");
|
||||
|
||||
// Solve the system using Preconditioned Conjugate Gradient
|
||||
pcg->preconditioner_ = boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>();
|
||||
VectorValues deltaPCGJacobi = PCGSolver(*pcg).optimize(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>(), initial);
|
||||
// Failed!
|
||||
EXPECT(assert_equal(expectedSolution, deltaPCGJacobi, 1e-5));
|
||||
//deltaPCGJacobi.print("PCG Jacobi");
|
||||
|
||||
// Test that the retrieval of the diagonal blocks of the Jacobian are correct.
|
||||
std::map<Key, Matrix> expectedHessian =simpleGFG.hessianBlockDiagonal();
|
||||
std::vector<Matrix> actualHessian = buildBlocks(simpleGFG, KeyInfo(simpleGFG));
|
||||
EXPECT_LONGS_EQUAL(expectedHessian.size(), actualHessian.size());
|
||||
std::map<Key, Matrix>::const_iterator it1 = expectedHessian.begin();
|
||||
std::vector<Matrix>::const_iterator it2 = actualHessian.begin();
|
||||
|
||||
// The corresponding Cholesky decomposition is:
|
||||
// R = chol(H0) = [4.1231 1.6977 0 2.6679] (from Matlab)
|
||||
Preconditioner::shared_ptr preconditioner = createPreconditioner(boost::make_shared<gtsam::BlockJacobiPreconditionerParameters>());
|
||||
preconditioner->build(simpleGFG, KeyInfo(simpleGFG), std::map<Key,Vector>());
|
||||
boost::shared_ptr<BlockJacobiPreconditioner> blockJacobi = boost::dynamic_pointer_cast<BlockJacobiPreconditioner>(preconditioner);
|
||||
double* buf = blockJacobi->getBuffer();
|
||||
for(int i=0; i<4; ++i){}
|
||||
// TODO: EXPECT(assert_equal(number..,buf[i]));
|
||||
|
||||
size_t i = 0;
|
||||
for(; it1!=expectedHessian.end(); it1++, it2++){
|
||||
EXPECT(assert_equal(it1->second, *it2));
|
||||
Matrix R_i(2,2);
|
||||
R_i(0,0) = buf[i+0];
|
||||
R_i(0,1) = buf[i+1];
|
||||
R_i(1,0) = buf[i+2];
|
||||
R_i(1,1) = buf[i+3];
|
||||
|
||||
Matrix actualH_i = R_i.transpose() * R_i;// i-th diagonal block
|
||||
EXPECT(assert_equal(it1->second, actualH_i));
|
||||
i += 4;
|
||||
}
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
Loading…
Reference in New Issue