OptionalcentralOptionaldampingLevenberg-Marquardt parameter, small values of the damping parameter λ result in a Gauss-Newton update and large values of λ result in a gradient descent update
Optionaldampingfactor to reduce the damping (Levenberg-Marquardt parameter) when there is not an improvement when updating parameters.
Optionaldampingfactor to increase the damping (Levenberg-Marquardt parameter) when there is an improvement when updating parameters.
OptionalerrorMinimum uncertainty allowed for each point.
OptionalgradientThe step size to approximate the jacobian matrix
Optionalimprovementthe threshold to define an improvement through an update of parameters
Array of initial parameter values
OptionaljacobianAnalytical Jacobian of the model. When provided, the Jacobian is computed
exactly instead of being approximated by finite differences, which avoids
the extra model evaluations per parameter and usually converges in fewer
iterations. centralDifference and gradientDifference are ignored when
this option is set.
OptionalmaxMaximum of allowed iterations
OptionalmaxMaximum allowed values for parameters
OptionalminMinimum allowed values for parameters
Optionaltimeoutmaximum time running before throw in seconds.
Optionalweightsweighting vector, if the length does not match with the number of data points, the vector is reconstructed with first value.
If true the jacobian matrix is approximated by central differences otherwise by forward differences