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28 using namespace gtsam;
64 Vector expectedX = (
Vector(6) << -0.1, 0.1, -0.1, -0.1, 0.1, -0.2).finished();
82 TEST( Iterative, conjugateGradientDescent_hard_constraint )
110 TEST( Iterative, conjugateGradientDescent_soft_constraint )
120 std::shared_ptr<GaussianFactorGraph> fg =
graph.
linearize(config);
static int runAllTests(TestResult &result)
Vector steepestDescent(const System &Ab, const Vector &x, const ConjugateGradientParameters ¶meters)
Array< double, 1, 3 > e(1./3., 0.5, 2.)
static const Eigen::MatrixBase< Vector3 >::ConstantReturnType Z_3x1
std::pair< Matrix, Vector > jacobian(const Ordering &ordering) const
TEST(Iterative, steepestDescent)
std::shared_ptr< GaussianFactorGraph > linearize(const Values &linearizationPoint) const
Linearize a nonlinear factor graph.
Matrix< SCALARA, Dynamic, Dynamic, opt_A > A
Vector conjugateGradientDescent(const System &Ab, const Vector &x, const ConjugateGradientParameters ¶meters)
GaussianFactorGraph createGaussianFactorGraph()
static ConjugateGradientParameters parameters
EIGEN_DONT_INLINE Scalar zero()
void addPrior(Key key, const T &prior, const SharedNoiseModel &model=nullptr)
Iterative methods, implementation.
double epsilon_rel
threshold for relative error decrease
VectorValues zeroVectors() const
static const Pose3 pose1(Rot3(), Point3(0, 1, 0.5))
size_t maxIterations
maximum number of cg iterations
void insert(Key j, const Value &val)
bool assert_equal(const Matrix &expected, const Matrix &actual, double tol)
VectorValues optimize(const Eliminate &function=EliminationTraitsType::DefaultEliminate) const
NonlinearFactorGraph graph
Create small example with two poses and one landmark.
Eigen::Matrix< double, Eigen::Dynamic, 1 > Vector
double epsilon_abs
threshold for absolute error decrease
IsDerived< DERIVEDFACTOR > emplace_shared(Args &&... args)
Emplace a shared pointer to factor of given type.
gtsam
Author(s):
autogenerated on Fri Nov 1 2024 03:40:26