# 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 view(::Array{Float64,1}, :) with eltype Float64: 0.0 0.0 1.0

## Types and Functions

`Manifolds.ArrayPowerRepresentation`

— Type`ArrayPowerRepresentation`

Representation of points and tangent vectors on a power manifold using multidimensional arrays where first dimensions are equal to `representation_size`

of the wrapped manifold and the following ones are equal to the number of elements in each direction.

`Torus`

uses this representation.

`Manifolds.PowerFVectorDistribution`

— Type`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`

.

`Manifolds.PowerMetric`

— Type`PowerMetric <: Metric`

Represent the `Metric`

on an `AbstractPowerManifold`

, i.e. the inner product on the tangent space is the sum of the inner product of each elements tangent space of the power manifold.

`Manifolds.PowerPointDistribution`

— Type`PowerPointDistribution(M::AbstractPowerManifold, distribution)`

Power distribution on manifold `M`

, based on `distribution`

.

`Manifolds.flat`

— Method`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`

).

`Manifolds.sharp`

— Method`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`

).