package scipy

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package scipy
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type tag = [
  1. | `ExcitingMixing
]
type t = [ `ExcitingMixing | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val create : ?alpha:Py.Object.t -> ?alphamax:float -> unit -> t

Find a root of a function, using a tuned diagonal Jacobian approximation.

The Jacobian matrix is diagonal and is tuned on each iteration.

.. warning::

This algorithm may be useful for specific problems, but whether it will work may depend strongly on the problem.

See Also -------- root : Interface to root finding algorithms for multivariate functions. See ``method=='excitingmixing'`` in particular.

Parameters ---------- %(params_basic)s alpha : float, optional Initial Jacobian approximation is (-1/alpha). alphamax : float, optional The entries of the diagonal Jacobian are kept in the range ``alpha, alphamax``. %(params_extra)s

val aspreconditioner : [> tag ] Obj.t -> Py.Object.t

None

val matvec : f:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val rmatvec : f:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val rsolve : ?tol:Py.Object.t -> f:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val setup : x:Py.Object.t -> f:Py.Object.t -> func:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val solve : ?tol:Py.Object.t -> f:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val todense : [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

None

val update : x:Py.Object.t -> f:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

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