Optional
centralOptional
dampingLevenberg-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
Optional
dampingfactor to reduce the damping (Levenberg-Marquardt parameter) when there is not an improvement when updating parameters.
Optional
dampingfactor to increase the damping (Levenberg-Marquardt parameter) when there is an improvement when updating parameters.
Optional
errorMinimum uncertainty allowed for each point.
Optional
gradientThe step size to approximate the jacobian matrix
Optional
improvementthe threshold to define an improvement through an update of parameters
Array of initial parameter values
Optional
maxMaximum of allowed iterations
Optional
maxMaximum allowed values for parameters
Optional
minMinimum allowed values for parameters
Optional
timeoutmaximum time running before throw in seconds.
Optional
weightsweighting 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