template<typename FunctorType, typename Scalar = double>
class Eigen::LevenbergMarquardt< FunctorType, Scalar >
Performs non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm.
Check wikipedia for more information. http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
Inherits internal::no_assignment_operator.
◆ diag()
template<typename FunctorType , typename Scalar = double>
- Returns
- a reference to the diagonal of the jacobian
◆ epsilon()
template<typename FunctorType , typename Scalar = double>
- Returns
- the error precision
◆ factor()
template<typename FunctorType , typename Scalar = double>
- Returns
- the step bound for the diagonal shift
◆ fnorm()
template<typename FunctorType , typename Scalar = double>
- Returns
- the norm of current vector function
◆ ftol()
template<typename FunctorType , typename Scalar = double>
- Returns
- the tolerance for the norm of the vector function
◆ fvec()
template<typename FunctorType , typename Scalar = double>
- Returns
- a reference to the current vector function
◆ gnorm()
template<typename FunctorType , typename Scalar = double>
- Returns
- the norm of the gradient of the error
◆ gtol()
template<typename FunctorType , typename Scalar = double>
- Returns
- the tolerance for the norm of the gradient of the error vector
◆ info()
template<typename FunctorType , typename Scalar = double>
Reports whether the minimization was successful.
- Returns
Success
if the minimization was successful, NumericalIssue
if a numerical problem arises during the minimization process, for example during the QR factorization NoConvergence
if the minimization did not converge after the maximum number of function evaluation allowed InvalidInput
if the input matrix is invalid
◆ iterations()
template<typename FunctorType , typename Scalar = double>
- Returns
- the number of iterations performed
◆ jacobian()
template<typename FunctorType , typename Scalar = double>
- Returns
- a reference to the matrix where the current Jacobian matrix is stored
◆ lm_param()
template<typename FunctorType , typename Scalar = double>
◆ matrixR()
template<typename FunctorType , typename Scalar = double>
- Returns
- a reference to the triangular matrix R from the QR of the jacobian matrix.
- See also
- jacobian()
◆ maxfev()
template<typename FunctorType , typename Scalar = double>
- Returns
- the maximum number of function evaluation
◆ nfev()
template<typename FunctorType , typename Scalar = double>
- Returns
- the number of functions evaluation
◆ njev()
template<typename FunctorType , typename Scalar = double>
- Returns
- the number of jacobian evaluation
◆ permutation()
template<typename FunctorType , typename Scalar = double>
the permutation used in the QR factorization
◆ resetParameters()
template<typename FunctorType , typename Scalar = double>
Sets the default parameters
◆ setEpsilon()
template<typename FunctorType , typename Scalar = double>
◆ setExternalScaling()
template<typename FunctorType , typename Scalar = double>
Use an external Scaling. If set to true, pass a nonzero diagonal to diag()
◆ setFactor()
template<typename FunctorType , typename Scalar = double>
Sets the step bound for the diagonal shift
◆ setFtol()
template<typename FunctorType , typename Scalar = double>
Sets the tolerance for the norm of the vector function
◆ setGtol()
template<typename FunctorType , typename Scalar = double>
Sets the tolerance for the norm of the gradient of the error vector
◆ setMaxfev()
template<typename FunctorType , typename Scalar = double>
Sets the maximum number of function evaluation
◆ setXtol()
template<typename FunctorType , typename Scalar = double>
Sets the tolerance for the norm of the solution vector
◆ xtol()
template<typename FunctorType , typename Scalar = double>
- Returns
- the tolerance for the norm of the solution vector
The documentation for this class was generated from the following files: