# Power manifold

A power manifold is based on a Manifold $\mathcal M$ to build a $\mathcal M^{n_1 \times n_2 \times \cdots \times n_m}$. In the case where $m=1$ we can represent a manifold-valued vector of data of length $n_1$, for example a time series. The case where $m=2$ is useful for representing manifold-valued matrices of data of size $n_1 \times n_2$, for example certain types of images.

## Example

There are two ways to store the data: in a multidimensional array or in a nested array.

Let's look at an example for both. Let $\mathcal M$ be Sphere(2) the 2-sphere and we want to look at vectors of length 4.

For the default, the ArrayPowerRepresentation, we store the data in a multidimensional array,

using Manifolds
M = PowerManifold(Sphere(2), 4)
p = cat([1.0, 0.0, 0.0],
[1/sqrt(2.0), 1/sqrt(2.0), 0.0],
[1/sqrt(2.0), 0.0, 1/sqrt(2.0)],
[0.0, 1.0, 0.0]
,dims=2)
3×4 Array{Float64,2}:
1.0  0.707107  0.707107  0.0
0.0  0.707107  0.0       1.0
0.0  0.0       0.707107  0.0

which is a valid point i.e.

is_manifold_point(M, p)
true

This can also be used in combination with HybridArrays.jl and StaticArrays.jl, by setting

using HybridArrays, StaticArrays
q = HybridArray{Tuple{3,StaticArrays.Dynamic()},Float64,2}(p)
3×4 HybridArrays.HybridArray{Tuple{3,StaticArrays.Dynamic()},Float64,2,2,Array{Float64,2}} with indices SOneTo(3)×Base.OneTo(4):
1.0  0.707107  0.707107  0.0
0.0  0.707107  0.0       1.0
0.0  0.0       0.707107  0.0

which is still a valid point on M and PowerManifold works with these, too.

An advantage of this representation is that it is quite efficient, especially when a HybridArray (from the HybridArrays.jl package) is used to represent a point on the power manifold. A disadvantage is not being able to easily identify parts of the multidimensional array that correspond to a single point on the base manifold. Another problem is, that accessing a single point is p[:, 1] which might be unintuitive.

For the NestedPowerRepresentation we can now do

using Manifolds
M = PowerManifold(Sphere(2), NestedPowerRepresentation(), 4)
p = [ [1.0, 0.0, 0.0],
[1/sqrt(2.0), 1/sqrt(2.0), 0.0],
[1/sqrt(2.0), 0.0, 1/sqrt(2.0)],
[0.0, 1.0, 0.0],
]
4-element Array{Array{Float64,1},1}:
[1.0, 0.0, 0.0]
[0.7071067811865475, 0.7071067811865475, 0.0]
[0.7071067811865475, 0.0, 0.7071067811865475]
[0.0, 1.0, 0.0]

which is again a valid point so is_manifold_point(M, p) here also yields true. A disadvantage might be that with nested arrays one loses a little bit of performance. The data however is nicely encapsulated. Accessing the first data item is just p[1].

For accessing points on power manifolds in both representations you can use get_component and set_component! functions. They work work both point representations.

using Manifolds
M = PowerManifold(Sphere(2), NestedPowerRepresentation(), 4)
p = [ [1.0, 0.0, 0.0],
[1/sqrt(2.0), 1/sqrt(2.0), 0.0],
[1/sqrt(2.0), 0.0, 1/sqrt(2.0)],
[0.0, 1.0, 0.0],
]
set_component!(M, p, [0.0, 0.0, 1.0], 4)
get_component(M, p, 4)
3-element Array{Float64,1}:
0.0
0.0
1.0

## Types and Functions

Manifolds.PowerFVectorDistributionType
PowerFVectorDistribution([type::VectorBundleFibers], [x], distr)

Generates a random vector at a point from vector space (a fiber of a tangent bundle) of type type using the power distribution of distr.

Vector space type and point can be automatically inferred from distribution distr.

source
Manifolds.flatMethod
flat(M::AbstractPowerManifold, p, X::FVector{TangentSpaceType})

use the musical isomorphism to transform the tangent vector X from the tangent space at p on an AbstractPowerManifold M to a cotangent vector. This can be done elementwise for each entry of X (and p).

source
Manifolds.sharpMethod
sharp(M::AbstractPowerManifold, p, ξ::FVector{CotangentSpaceType})

Use the musical isomorphism to transform the cotangent vector ξ from the tangent space at p on an AbstractPowerManifold M to a tangent vector. This can be done elementwise for every entry of ξ (and p).

source