ScipyMinimize

class pmrf.optimize.ScipyMinimize(method: str | None = None, tol: float | None = None, options: dict = <factory>, show_progress: bool = True)

Bases: AbstractBoundedMinimizer

A wrapper around SciPy’s scipy.optimize.minimize().

Acts as an adapter layer between JAX’s PyTrees and SciPy’s required flat 1D NumPy arrays. Handles automatic differentiation implicitly via JAXopt.

Parameters:

  • method (str, optional) – Type of solver, pass None to use SciPy defaults.

  • tol (float, optional) – Tolerance for termination.

  • options (dict, optional) – A dictionary of solver options.

  • show_progress (bool, default=True) – Whether to show a progress bar during optimization.

run(fn: Callable[[PyTree, Any], Any], y0: PyTree, args: Any = None, bounds: tuple[PyTree, PyTree] | None = None, max_iter: int = 1024, **kwargs) → tuple[MinimizeResult, PyTree]

Execute the minimization algorithm.

Parameters:

  • fn (callable) – The objective function to minimize.

  • y0 (PyTree) – The initial parameter guess.

  • args (Any) – Args to pass to fn.

  • bounds (PyTree) – Bounds for y0, if any.

  • max_iter (int = 1024) – The maximum number of iterations to take.

  • **kwargs – Runtime arguments forward to the solver backend.

Returns:

A tuple of (pmrf.optimize.MinimizeResult, metrics)`.

Return type:

tuple

method: str | None = None
options: dict
show_progress: bool = True
tol: float | None = None