package scipy

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package scipy
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Library
Module
Module type
Parameter
Class
Class type
type tag = [
  1. | `BroydenSecond
]
type t = [ `BroydenSecond | `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 -> ?reduction_method:Py.Object.t -> ?max_rank:Py.Object.t -> unit -> t

Find a root of a function, using Broyden's second Jacobian approximation.

This method is also known as 'Broyden's bad method'.

Parameters ---------- %(params_basic)s %(broyden_params)s %(params_extra)s

See Also -------- root : Interface to root finding algorithms for multivariate functions. See ``method=='broyden2'`` in particular.

Notes ----- This algorithm implements the inverse Jacobian Quasi-Newton update

.. math:: H_+ = H + (dx - H df) df^\dagger / ( df^\dagger df)

corresponding to Broyden's second method.

References ---------- .. 1 B.A. van der Rotten, PhD thesis, 'A limited memory Broyden method to solve high-dimensional systems of nonlinear equations'. Mathematisch Instituut, Universiteit Leiden, The Netherlands (2003).

https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf

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 -> Py.Object.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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