# How to implement your own manifold

This tutorial demonstrates how to easily set your own manifold up within Manifolds.jl.

## Introduction

If you looked around a little and saw the interface, the amount of functions and possibilities, it might seem that a manifold might take some time to implement. This tutorial demonstrates that you can get your first own manifold quite fast and you only have to implement the functions you actually need. For this tutorial it would be helpful if you take a look at our notation. This tutorial assumes that you heard of the exponential map, tangent vectors and the dimension of a manifold. If not, please read for example [do Carmo, 1992], Chapter 3, first.

In general you need just a datatype (struct) that inherits from AbstractManifold to define a manifold. No function is per se required to be implemented. However, it is a good idea to provide functions that might be useful to others, for example check_point and check_vector, as we do in this tutorial.

We start with two technical preliminaries. If you want to start directly, you can skip this paragraph and revisit it for two of the implementation details.

After that, we will

## Technical preliminaries

There are only two small technical things we need to explain at this point. First of all our AbstractManifold{𝔽} has a parameter 𝔽. This parameter indicates the number_system the manifold is based on, for example ℝ for real manifolds. It is important primarily for defining bases of tangent spaces. See SymmetricMatrices as an example of defining both a real-valued and a complex-valued symmetric manifolds using one type.

Second, a main design decision of Manifolds.jl is that most functions are implemented as mutating functions, i.e. as in-place-computations. There usually exists a non-mutating version that falls back to allocating memory and calling the mutating one. This means you only have to implement the mutating version, unless there is a good reason to provide a special case for the non-mutating one, i.e. because in that case you know a far better performing implementation.

Let's look at an example. The exponential map $\exp_p\colon T_p\mathcal M \to \mathcal M$ that maps a tangent vector $X\in T_p\mathcal M$ from the tangent space at $p\in \mathcal M$ to the manifold. The function exp has to know the manifold M, the point p and the tangent vector X as input, so to compute the resulting point q you need to call

q = exp(M, p, X)

If you already have allocated memory for the variable that should store the result, it is better to perform the computations directly in that memory and avoid reallocations. For example

q = similar(p)
exp!(M, q, p, X)

calls exp!, which modifies its input q and returns the resulting point in there. Actually these two lines are (almost) the default implementation for exp. allocate_result that is actually used there just calls similar for simple Arrays. Note that for a unified interface, the manifold M is always the first parameter, and the variable the result will be stored to in the mutating variants is always the second parameter.

Long story short: if possible, implement the mutating version exp!, you get the exp for free. Many functions that build upon basic functions employ the mutating variant, too, to avoid reallocations.

## Startup

As a start, let's load ManifoldsBase.jl and import the functions we consider throughout this tutorial. For implementing a manifold, loading the interface should suffice for quite some time.

using ManifoldsBase, LinearAlgebra, Test
import ManifoldsBase: check_point, check_vector, manifold_dimension, exp!

## Goal

As an example, let's implement the sphere, but with a radius $r$. Since this radius is a property inherent to the manifold, it will become a field of the manifold. The second information, we want to store is the dimension of the sphere, for example whether it's the 1-sphere, i.e. the circle, represented by vectors $p\in\mathbb R^2$ or the 2-sphere in $\mathbb R^3$. Since the latter might be something we want to dispatch on, we model it as a parameter of the type.

In general the struct of a manifold should provide information about the manifold, which are inherent to the manifold or has to be available without a specific point or tangent vector present. This is – most prominently – a way to determine the manifold dimension.

For our example we define

"""
MySphere{N} <: AbstractManifold{ℝ}

Define an n-sphere of radius r. Construct by MySphere(radius,n)
"""
struct MySphere{N} <: AbstractManifold{ManifoldsBase.ℝ} where {N}
end
Base.show(io::IO, M::MySphere{n}) where {n} = print(io, "MySphere($(M.radius),$n)")

Here, the last line just provides a nicer print of a variable of that type Now we can already initialize our manifold that we will use later, the $2$-sphere of radius $1.5$.

S = MySphere(1.5, 2)
MySphere(1.5,2)

## Checking points and tangents

If we have now a point, represented as an array, we would first like to check, that it is a valid point on the manifold. For this one can use the easy interface is_point. This internally uses check_point. This is what we want to implement. We have to return the error if p is not on M and nothing otherwise.

We have to check two things: that a point p is a vector with N+1 entries and its norm is the desired radius. To spare a few lines, we can use short-circuit evaluation instead of if statements. If something has to only hold up to precision, we can pass that down, too using the kwargs....

function check_point(M::MySphere{N}, p; kwargs...) where {N}
(size(p)) == (N+1,) || return DomainError(size(p),"The size of $p is not$((N+1,)).")
return DomainError(norm(p), "The norm of $p is not$(M.radius).")
end
return nothing
end

Similarly, we can verify, whether a tangent vector X is valid. It has to fulfill the same size requirements and it has to be orthogonal to p. We can again use the kwargs, but also provide a way to check p, too.

function check_vector(M::MySphere, p, X; kwargs...)
size(X) != size(p) && return DomainError(size(X), "The size of $X is not$(size(p)).")
if !isapprox(dot(p,X), 0.0; kwargs...)
return DomainError(dot(p,X), "The tangent $X is not orthogonal to$p.")
end
return nothing
end

to test points we can now use

is_point(S, [1.0,0.0,0.0]) # norm 1, so not on S, returns false
@test_throws DomainError is_point(S, [1.5,0.0], true) # only on R^2, throws an error.
p = [1.5,0.0,0.0]
X = [0.0,1.0,0.0]
# The following two tests return true
[ is_point(S, p); is_vector(S,p,X) ]
2-element Vector{Bool}:
1
1

## Functions on the manifold

For the manifold_dimension we have to just return the N parameter

manifold_dimension(::MySphere{N}) where {N} = N
manifold_dimension(S)
2

Note that we can even omit the variable name in the first line since we do not have to access any field or use the variable otherwise.

To implement the exponential map, we have to implement the formula for great arcs, given a start point p and a direction X on the $n$-sphere of radius $r$ the formula reads

$$$\exp_p X = \cos(\frac{1}{r}\lVert X \rVert)p + \sin(\frac{1}{r}\lVert X \rVert)\frac{r}{\lVert X \rVert}X.$$$

Note that with this choice we for example implicitly assume a certain metric. This is completely fine. We only have to think about specifying a metric explicitly, when we have (at least) two different metrics on the same manifold.

An implementation of the mutation version, see the technical note, reads

function exp!(M::MySphere{N}, q, p, X) where {N}
nX = norm(X)
if nX == 0
q .= p
else
end
return q
end

A first easy check can be done taking p from above and any vector X of length 1.5π from its tangent space. The resulting point is opposite of p, i.e. -p

q = exp(S,p, [0.0,1.5π,0.0])
[isapprox(p,-q); is_point(S,q)]
2-element Vector{Bool}:
1
1

## Conclusion

You can now just continue implementing further functions from the interface, but with just exp! you for example already have

For the shortest_geodesic the implementation of a logarithm log, again better a log! is necessary.

Sometimes a default implementation is provided; for example if you implemented inner, the norm is defined. You should overwrite it, if you can provide a more efficient version. For a start the default should suffice. With log! and inner you get the distance, and so.

In summary with just these few functions you can already explore the first things on your own manifold. Whenever a function from Manifolds.jl requires another function to be specifically implemented, you get a reasonable error message.

## Literature

• [doCarmo, 1992] M. P. do Carmo, Riemannian Geometry, Birkhäuser Boston, 1992, ISBN: 0-8176-3490-8.