template<typename _FunctorType>
class Eigen::LevenbergMarquardt< _FunctorType >
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 no_assignment_operator.
◆ diag()
template<typename _FunctorType>
- Returns
- a reference to the diagonal of the jacobian
◆ epsilon()
template<typename _FunctorType>
- Returns
- the error precision
◆ factor()
template<typename _FunctorType>
- Returns
- the step bound for the diagonal shift
◆ fnorm()
template<typename _FunctorType>
- Returns
- the norm of current vector function
◆ ftol()
template<typename _FunctorType>
- Returns
- the tolerance for the norm of the vector function
◆ fvec()
template<typename _FunctorType>
- Returns
- a reference to the current vector function
◆ gnorm()
template<typename _FunctorType>
- Returns
- the norm of the gradient of the error
◆ gtol()
template<typename _FunctorType>
- Returns
- the tolerance for the norm of the gradient of the error vector
◆ info()
template<typename _FunctorType>
Reports whether the minimization was successful.
- Returns
Success
if the minimization was succesful, NumericalIssue
if a numerical problem arises during the minimization process, for exemple 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>
- Returns
- the number of iterations performed
◆ jacobian()
template<typename _FunctorType>
- Returns
- a reference to the matrix where the current Jacobian matrix is stored
◆ lm_param()
template<typename _FunctorType>
◆ matrixR()
template<typename _FunctorType>
- Returns
- a reference to the triangular matrix R from the QR of the jacobian matrix.
- See also
- jacobian()
◆ maxfev()
template<typename _FunctorType>
- Returns
- the maximum number of function evaluation
◆ nfev()
template<typename _FunctorType>
- Returns
- the number of functions evaluation
◆ njev()
template<typename _FunctorType>
- Returns
- the number of jacobian evaluation
◆ permutation()
template<typename _FunctorType>
the permutation used in the QR factorization
◆ resetParameters()
template<typename _FunctorType>
Sets the default parameters
◆ setEpsilon()
template<typename _FunctorType>
◆ setExternalScaling()
template<typename _FunctorType>
Use an external Scaling. If set to true, pass a nonzero diagonal to diag()
◆ setFactor()
template<typename _FunctorType>
Sets the step bound for the diagonal shift
◆ setFtol()
template<typename _FunctorType>
Sets the tolerance for the norm of the vector function
◆ setGtol()
template<typename _FunctorType>
Sets the tolerance for the norm of the gradient of the error vector
◆ setMaxfev()
template<typename _FunctorType>
Sets the maximum number of function evaluation
◆ setXtol()
template<typename _FunctorType>
Sets the tolerance for the norm of the solution vector
◆ xtol()
template<typename _FunctorType>
- Returns
- the tolerance for the norm of the solution vector
The documentation for this class was generated from the following files: