nlsq.callbacks.CallbackBase

class nlsq.callbacks.CallbackBase

Bases: object

Base class for optimization callbacks.

Subclass this to create custom callbacks. Override the __call__ method to define what happens at each iteration.

Examples

>>> class CustomCallback(CallbackBase):
...     def __call__(self, iteration, cost, params, info):
...         print(f"Iter {iteration}: cost={cost:.6f}")

__call__(iteration, cost, params, info)

Called after each optimization iteration.

Parameters:

• iteration (int) – Current iteration number (0-indexed)

• cost (float) – Current cost/objective function value

• params (np.ndarray) – Current parameter values

• info (dict) – Additional information (gradient_norm, nfev, etc.)

close()

Clean up resources.

Override this method if your callback uses resources that need explicit cleanup (files, network connections, etc.).