nifty8.re.optimize module

class OptimizeResults(x: Any, success: bool | Array, status: int | Array, fun: Any, jac: Any, hess: Array | None = None, hess_inv: Array | None = None, nfev: None | int | Array = None, njev: None | int | Array = None, nhev: None | int | Array = None, nit: None | int | Array = None, trust_radius: None | float | Array = None, jac_magnitude: None | float | Array = None, good_approximation: None | bool | Array = None)

Bases: NamedTuple

Object holding optimization results inspired by JAX and scipy.

Variables:

• x (ndarray) – The solution of the optimization.

• success (bool) – Whether or not the optimizer exited successfully.

• status (int) – Termination status of the optimizer. Its value depends on the underlying solver. NOTE, in contrast to scipy there is no message for details since strings are not statically memory bound.

• hess (fun, jac,) – Values of objective function, its Jacobian and its Hessian (if available). The Hessians may be approximations, see the documentation of the function in question.

• hess_inv (object) – Inverse of the objective function’s Hessian; may be an approximation. Not available for all solvers.

• nhev (nfev, njev,) – Number of evaluations of the objective functions and of its Jacobian and Hessian.

• nit (int) – Number of iterations performed by the optimizer.

fun: Any

Alias for field number 3

good_approximation: None | bool | Array

Alias for field number 13

hess: Array | None

Alias for field number 5

hess_inv: Array | None

Alias for field number 6

jac: Any

Alias for field number 4

jac_magnitude: None | float | Array

Alias for field number 12

nfev: None | int | Array

Alias for field number 7

nhev: None | int | Array

Alias for field number 9

nit: None | int | Array

Alias for field number 10

njev: None | int | Array

Alias for field number 8

status: int | Array

Alias for field number 2

success: bool | Array

Alias for field number 1

trust_radius: None | float | Array

Alias for field number 11

x: Any

Alias for field number 0

minimize(fun: Callable[[...], float] | None, x0, args: Tuple = (), *, method: str, tol: float | None = None, options: Mapping[str, Any] | None = None) → OptimizeResults

Minimize fun.

newton_cg(fun=None, x0=None, *args, **kwargs)

Minimize a scalar-valued function using the Newton-CG algorithm.

trust_ncg(fun=None, x0=None, *args, **kwargs)