package scipy

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package scipy
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type tag = [
  1. | `GenericBroyden
]
type t = [ `GenericBroyden | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val create : ?kw:(string * Py.Object.t) list -> unit -> t

Common interface for Jacobians or Jacobian approximations.

The optional methods come useful when implementing trust region etc., algorithms that often require evaluating transposes of the Jacobian.

Methods ------- solve Returns J^-1 * v update Updates Jacobian to point `x` (where the function has residual `Fx`)

matvec : optional Returns J * v rmatvec : optional Returns A^H * v rsolve : optional Returns A^-H * v matmat : optional Returns A * V, where V is a dense matrix with dimensions (N,K). todense : optional Form the dense Jacobian matrix. Necessary for dense trust region algorithms, and useful for testing.

Attributes ---------- shape Matrix dimensions (M, N) dtype Data type of the matrix. func : callable, optional Function the Jacobian corresponds to

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

None

val setup : x0:Py.Object.t -> f0:Py.Object.t -> func:Py.Object.t -> [> tag ] Obj.t -> Py.Object.t

None

val solve : ?tol:Py.Object.t -> v:Py.Object.t -> [> 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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