Differentiation backends

set_default_differential_backend!(backend::AbstractDiffBackend)

Set current backend for differentiation to backend.

default_differential_backend() -> AbstractDiffBackend

Get the default differentiation backend.

ExplicitEmbeddedBackend{TF<:NamedTuple} <: AbstractDiffBackend

A backend to use with the RiemannianProjectionBackend or the TangentDiffBackend, when you have explicit formulae for the gradient in the embedding available.

Constructor

ExplicitEmbeddedBackend(M::AbstractManifold; kwargs)

Construct an ExplicitEmbeddedBackend in the embedding M, where currently the following keywords may be used

• gradient for a(n allocating) gradient function gradient(M, p) defined in the embedding
• gradient! for a mutating gradient function gradient!(M, X, p).

Note that the gradient functions are defined on the embedding manifold M passed to the Backend as well

ForwardDiffBackend <: AbstractDiffBackend

Differentiation backend based on the ForwardDiff.jl package.

FiniteDiffBackend <: AbstractDiffBackend

A type to specify / use differentiation backend based on FiniteDiff.jl package.

Constructor

FiniteDiffBackend(method::Val{Symbol} = Val{:central})

FiniteDifferencesBackend(method::FiniteDifferenceMethod = central_fdm(5, 1))

Differentiation backend based on the FiniteDifferences.jl package.