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| JacobianFactorSVD () |
| Default constructor. More...
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| JacobianFactorSVD (const KeyVector &keys, const SharedDiagonal &model=SharedDiagonal()) |
| Empty constructor with keys. More...
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| JacobianFactorSVD (const KeyVector &keys, const std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > &Fblocks, const Matrix &Enull, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
| Construct a new JacobianFactorSVD object, createing a reduced-rank Jacobian factor on the CameraSet. More...
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VectorValues | gradientAtZero () const override |
| Expose base class gradientAtZero. More...
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void | gradientAtZero (double *d) const override |
| Raw memory access version of gradientAtZero. More...
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VectorValues | hessianDiagonal () const |
| Using the base method. More...
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void | hessianDiagonal (double *d) const override |
| Raw memory access version of hessianDiagonal. More...
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virtual void | hessianDiagonal (double *d) const=0 |
| Using the base method. More...
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void | multiplyHessianAdd (double alpha, const double *x, double *y) const |
| double* Hessian-vector multiply, i.e. y += A'*(A*x) RAW memory access! Assumes keys start at 0 and go to M-1, and x and and y are laid out that way More...
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void | multiplyHessianAdd (double alpha, const double *x, double *y, const std::vector< size_t > &accumulatedDims) const |
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void | multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const override |
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void | multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const override |
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Vector | operator* (const double *x) const |
| double* Matrix-vector multiply, i.e. y = A*x RAW memory access! Assumes keys start at 0 and go to M-1, and x is laid out that way More...
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| RegularJacobianFactor () |
| Default constructor. More...
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template<typename KEYS > |
| RegularJacobianFactor (const KEYS &keys, const VerticalBlockMatrix &augmentedMatrix, const SharedDiagonal &sigmas=SharedDiagonal()) |
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template<typename TERMS > |
| RegularJacobianFactor (const TERMS &terms, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
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void | transposeMultiplyAdd (double alpha, const Vector &e, double *x) const |
| double* Transpose Matrix-vector multiply, i.e. x += A'*e RAW memory access! Assumes keys start at 0 and go to M-1, and y is laid out that way More...
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Matrix | augmentedInformation () const override |
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Matrix | augmentedJacobian () const override |
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Matrix | augmentedJacobianUnweighted () const |
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GaussianFactor::shared_ptr | clone () const override |
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size_t | cols () const |
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std::pair< std::shared_ptr< GaussianConditional >, shared_ptr > | eliminate (const Ordering &keys) |
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bool | equals (const GaussianFactor &lf, double tol=1e-9) const override |
| assert equality up to a tolerance More...
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double | error (const HybridValues &c) const override |
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virtual double | error (const VectorValues &c) const |
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double | error (const VectorValues &c) const override |
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Vector | error_vector (const VectorValues &c) const |
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SharedDiagonal & | get_model () |
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const SharedDiagonal & | get_model () const |
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ABlock | getA () |
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constABlock | getA () const |
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ABlock | getA (const Key &key) |
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constABlock | getA (const_iterator variable) const |
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ABlock | getA (iterator variable) |
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BVector | getb () |
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const constBVector | getb () const |
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DenseIndex | getDim (const_iterator variable) const override |
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Vector | gradient (Key key, const VectorValues &x) const override |
| Compute the gradient wrt a key at any values. More...
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std::map< Key, Matrix > | hessianBlockDiagonal () const override |
| Return the block diagonal of the Hessian for this factor. More...
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void | hessianDiagonalAdd (VectorValues &d) const override |
| Add the current diagonal to a VectorValues instance. More...
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Matrix | information () const override |
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bool | isConstrained () const |
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std::pair< Matrix, Vector > | jacobian () const override |
| Returns (dense) A,b pair associated with factor, bakes in the weights. More...
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| JacobianFactor () |
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| JacobianFactor (const GaussianFactor &gf) |
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| JacobianFactor (const GaussianFactorGraph &graph) |
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| JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering) |
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| JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering, const VariableSlots &p_variableSlots) |
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| JacobianFactor (const GaussianFactorGraph &graph, const VariableSlots &p_variableSlots) |
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| JacobianFactor (const HessianFactor &hf) |
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| JacobianFactor (const JacobianFactor &jf) |
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template<typename KEYS > |
| JacobianFactor (const KEYS &keys, const VerticalBlockMatrix &augmentedMatrix, const SharedDiagonal &sigmas=SharedDiagonal()) |
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template<typename TERMS > |
| JacobianFactor (const TERMS &terms, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
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| JacobianFactor (const Vector &b_in) |
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| JacobianFactor (Key i1, const Matrix &A1, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
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| JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
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| JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, Key i3, const Matrix &A3, const Vector &b, const SharedDiagonal &model=SharedDiagonal()) |
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std::pair< Matrix, Vector > | jacobianUnweighted () const |
| Returns (dense) A,b pair associated with factor, does not bake in weights. More...
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VerticalBlockMatrix & | matrixObject () |
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const VerticalBlockMatrix & | matrixObject () const |
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void | multiplyHessianAdd (double alpha, const double *x, double *y, const std::vector< size_t > &accumulatedDims) const |
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GaussianFactor::shared_ptr | negate () const override |
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Vector | operator* (const VectorValues &x) const |
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void | print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override |
| print with optional string More...
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size_t | rows () const |
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void | setModel (bool anyConstrained, const Vector &sigmas) |
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std::shared_ptr< GaussianConditional > | splitConditional (size_t nrFrontals) |
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void | transposeMultiplyAdd (double alpha, const Vector &e, VectorValues &x) const |
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Vector | unweighted_error (const VectorValues &c) const |
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void | updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const override |
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JacobianFactor | whiten () const |
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| ~JacobianFactor () override |
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| GaussianFactor () |
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template<typename CONTAINER > |
| GaussianFactor (const CONTAINER &keys) |
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double | error (const HybridValues &c) const override |
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VectorValues | hessianDiagonal () const |
| Return the diagonal of the Hessian for this factor. More...
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virtual | ~Factor ()=default |
| Default destructor. More...
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bool | empty () const |
| Whether the factor is empty (involves zero variables). More...
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Key | front () const |
| First key. More...
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Key | back () const |
| Last key. More...
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const_iterator | find (Key key) const |
| find More...
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const KeyVector & | keys () const |
| Access the factor's involved variable keys. More...
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const_iterator | begin () const |
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const_iterator | end () const |
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size_t | size () const |
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virtual void | printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const |
| print only keys More...
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bool | equals (const This &other, double tol=1e-9) const |
| check equality More...
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KeyVector & | keys () |
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iterator | begin () |
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iterator | end () |
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template<size_t D, size_t ZDim>
class gtsam::JacobianFactorSVD< D, ZDim >
JacobianFactor for Schur complement that uses the "Nullspace Trick" by Mourikis et al.
This trick is equivalent to the Schur complement, but can be faster. In essence, the linear factor |E*dp + F*dX - b|, where p is point and X are poses, is multiplied by Enull, a matrix that spans the left nullspace of E, i.e., The mx3 matrix is analyzed with SVD as E = [Erange Enull]*S*V (mxm * mx3 * 3x3) where Enull is an m x (m-3) matrix Then Enull'*E*dp = 0, and |Enull'*E*dp + Enull'*F*dX - Enull'*b| == |Enull'*F*dX - Enull'*b| Normally F is m x 6*numKeys, and Enull'*F yields an (m-3) x 6*numKeys matrix.
The code below assumes that F is block diagonal and is given as a vector of ZDim*D blocks. Example: m = 4 (2 measurements), Enull = 4*1, F = 4*12 (for D=6) Then Enull'*F = 1*4 * 4*12 = 1*12, but each 1*6 piece can be computed as a 1x2 * 2x6 multiplication.
Definition at line 29 of file JacobianFactorSVD.h.