gleastsq - v0.3.2

gleastsq/levenberg_marquardt

Functions

levenberg_marquardt

</> 
pub fn levenberg_marquardt(
  x: List(Float),
  y: List(Float),
  func: fn(Float, List(Float)) -> Float,
  initial_params: List(Float),
  max_iterations iterations: Option(Int),
  epsilon epsilon: Option(Float),
  tolerance tolerance: Option(Float),
  damping damping: Option(Float),
  damping_increase damping_increase: Option(Float),
  damping_decrease damping_decrease: Option(Float),
) -> Result(List(Float), FitErrors)

The levenberg_marquardt function performs the Levenberg-Marquardt optimization algorithm. It is used to solve non-linear least squares problems. This function takes as input the data points, the model function, and several optional parameters to control the optimization process.

Parameters

  • x (List(Float)) A list of x-values of the data points.
  • y (List(Float)) A list of y-values of the data points.
  • func (fn(Float, List(Float)) -> Float) The model function that takes an x-value and a list of parameters, and returns the corresponding y-value.
  • initial_params (List(Float)) A list of initial guesses for the parameters of the model function.
  • iterations (Option(Int)) The maximum number of iterations to perform. Default is 100.
  • epsilon (Option(Float)) A small value to change x when calculating the derivatives for the function. Default is 0.0001.
  • tolerance (Option(Float)) The convergence tolerance. Default is 0.0001.
  • damping (Option(Float)) The initial value of the damping parameter. Default is 0.0001.
  • damping_increase (Option(Float)) The factor by which the damping parameter is increased when a step fails. Default is 10.0.
  • damping_decrease (Option(Float)): The factor by which the damping parameter is decreased when a step succeeds. Default is 0.1.
✨ Search Document