File linearization.h

namespace sym

Typedefs

template<typename Scalar>
using SparseLinearization = Linearization<Eigen::SparseMatrix<Scalar>>
using SparseLinearizationd = SparseLinearization<double>
using SparseLinearizationf = SparseLinearization<float>
template<typename Scalar>
using DenseLinearization = Linearization<MatrixX<Scalar>>
using DenseLinearizationd = DenseLinearization<double>
using DenseLinearizationf = DenseLinearization<float>

Functions

template<typename Scalar>
sparse_matrix_structure_t GetSparseStructure(const Eigen::SparseMatrix<Scalar> &matrix)

Returns the sparse matrix structure of matrix.

template<typename Derived>
sparse_matrix_structure_t GetSparseStructure(const Eigen::EigenBase<Derived> &matrix)

Return a default initialized sparse structure because arg is dense.

template<typename MatrixType, typename Scalar, typename std::enable_if_t<kIsSparseEigenType<MatrixType>, bool> = true>
Eigen::Map<const Eigen::SparseMatrix<Scalar>> MatrixViewFromSparseStructure(const sparse_matrix_structure_t &structure, const MatrixX<Scalar> &values)

Returns the reconstructed matrix from the stored values and sparse_matrix_structure_t

Useful for reconstructing the jacobian for an iteration in the OptimizationStats

The result contains references to structure and values, so its lifetime must be shorter than those.

Parameters:

  • rows – The number of rows in the matrix (must match structure.shape for sparse matrices)

  • cols – The number of columns in the matrix (must match structure.shape for sparse matrices)

  • structure – The sparsity pattern of the matrix, as obtained from GetSparseStructure or stored in OptimizationStats::iterations. For dense matrices, this should be empty.

  • values – The coefficients of the matrix; flat for sparse matrices, 2d for dense

Template Parameters:

  • MatrixType – The type of the matrix used to decide if the result is sparse or dense. This is not otherwise used

  • Scalar – The scalar type of the result (must match the scalar type of values)

template<typename MatrixType, typename Scalar, typename std::enable_if_t<kIsEigenType<MatrixType>, bool> = true>
Eigen::Map<const MatrixX<Scalar>> MatrixViewFromSparseStructure(const sparse_matrix_structure_t &structure, const MatrixX<Scalar> &values)

Returns the reconstructed matrix from the stored values and sparse_matrix_structure_t

Useful for reconstructing the jacobian for an iteration in the OptimizationStats

The result contains references to structure and values, so its lifetime must be shorter than those.

Parameters:

  • rows – The number of rows in the matrix (must match structure.shape for sparse matrices)

  • cols – The number of columns in the matrix (must match structure.shape for sparse matrices)

  • structure – The sparsity pattern of the matrix, as obtained from GetSparseStructure or stored in OptimizationStats::iterations. For dense matrices, this should be empty.

  • values – The coefficients of the matrix; flat for sparse matrices, 2d for dense

Template Parameters:

  • MatrixType – The type of the matrix used to decide if the result is sparse or dense. This is not otherwise used

  • Scalar – The scalar type of the result (must match the scalar type of values)

template<typename Scalar>
Eigen::Map<const VectorX<Scalar>> JacobianValues(const Eigen::SparseMatrix<Scalar> &matrix)

Returns coefficients of matrix. Overloads exist for both dense and sparse matrices to make writing generic code easier.

This version returns the non-zero values of an Eigen::SparseMatrix

Note: it returns a map, so be careful about mutating or disposing of matrix before you are finished with the output.

template<typename Scalar>
const MatrixX<Scalar> &JacobianValues(const MatrixX<Scalar> &matrix)

Returns coefficients of matrix. Overloads exist for both dense and sparse matrices to make writing generic code easier.

Returns a const-ref to the argument.

template<typename MatrixType>
struct Linearization
#include <linearization.h>

Class for storing a problem linearization evaluated at a Values (i.e. a residual, jacobian, hessian, and rhs)

MatrixType is expected to be an Eigen MatrixX or SparseMatrix.

Public Types

using Scalar = typename MatrixType::Scalar
using Vector = VectorX<Scalar>
using Matrix = MatrixType

Public Functions

inline void Reset()

Set to invalid

inline bool IsInitialized() const

Returns whether the linearization is currently valid for the corresponding values. Accessing any of the members when this is false could result in unexpected behavior

inline void SetInitialized(const bool initialized = true)
inline Scalar Error() const
inline Scalar LinearDeltaError(const Vector &x_update, const Vector &damping_vector) const

Returns the change in error predicted by the Linearization at the given update

Parameters:

  • x_update – The update to the values

  • damping_vector – The vector added to the diagonal of the hessian during the linear solve

Public Members

Vector residual
Matrix hessian_lower
Matrix jacobian
Vector rhs

Private Members

bool initialized_ = {false}