pysolvegn.build_numerical_jacobian#
- build_numerical_jacobian(residual_func, epsilon=1e-08)[source]#
Build a Jacobian function for the Gauss-Newton optimization by computing the numerical derivatives of the residual function with respect to the parameters using finite differences.
- Parameters:
residual_func (Callable) – A function that computes the residuals for a given set of parameters. The function should take a 1D array of parameters as input and return a 1D array of residuals.
epsilon (Real, optional) – A small perturbation value used for finite difference approximation of the Jacobian. Default is 1e-8.
- Returns:
jacobian_func – A function that computes the Jacobian matrix for a given set of parameters. The function should take a 1D array of parameters as input and return a 2D array representing the Jacobian matrix.
- Return type:
Callable
Version#
0.0.1: Initial version.
0.0.2: Renamed from build_jacobian to build_numerical_jacobian for clarity.