# Affine map (AffineMap)

LazySets.AffineMapType
AffineMap{N, S<:ConvexSet{N}, NM, MAT<:AbstractMatrix{NM},
VN<:AbstractVector{NM}} <: AbstractAffineMap{N, S}

Type that represents an affine transformation $M⋅X ⊕ v$ of a set $X$, that is the set

$$$Y = \{ y ∈ \mathbb{R}^n : y = Mx + v,\qquad x ∈ X \}.$$$

If $X$ is $n$-dimensional then $M$ should be an $m × n$ matrix and v ∈ \mathbb{R}^m.

Fields

• M – matrix
• X – set
• v – translation vector

The fields' getter functions are matrix, set and vector respectively.

Notes

An affine map is the composition of a linear map and a translation. This type is parametric in the coefficients of the linear map, NM, which may be different from the numeric type of the wrapped set, N. However, the numeric type of the translation vector should be NM.

The affine map preserves convexity: if X is convex, then any affine map of X is convex as well.

Examples

For the examples we create a $3×2$ matrix, a two-dimensional unit square, and a three-dimensional vector. Then we combine them in an AffineMap.

julia> A = [1 2; 1 3; 1 4]; X = BallInf([0, 0], 1); b2 = [1, 2]; b3 = [1, 2, 3];

julia> AffineMap(A, X, b3)
AffineMap{Int64, BallInf{Int64, Vector{Int64}}, Int64, Matrix{Int64}, Vector{Int64}}([1 2; 1 3; 1 4], BallInf{Int64, Vector{Int64}}([0, 0], 1), [1, 2, 3])

For convenience, A does not need to be a matrix but we also allow to use UniformScalings resp. scalars (interpreted as a scaling, i.e., a scaled identity matrix). Scaling by $1$ is ignored and simplified to a pure Translation.

julia> using LinearAlgebra

julia> am = AffineMap(2I, X, b2)
AffineMap{Int64, BallInf{Int64, Vector{Int64}}, Int64, Diagonal{Int64, Vector{Int64}}, Vector{Int64}}([2 0; 0 2], BallInf{Int64, Vector{Int64}}([0, 0], 1), [1, 2])

julia> AffineMap(2, X, b2) == am
true

julia> AffineMap(1, X, b2)
Translation{Int64, BallInf{Int64, Vector{Int64}}, Vector{Int64}}(BallInf{Int64, Vector{Int64}}([0, 0], 1), [1, 2])

Applying a linear map to an AffineMap object combines the two maps into a new AffineMap instance. Again we can make use of the conversion for convenience.

julia> B = [2 0; 0 2]; am2 = B * am
AffineMap{Int64, BallInf{Int64, Vector{Int64}}, Int64, Matrix{Int64}, Vector{Int64}}([4 0; 0 4], BallInf{Int64, Vector{Int64}}([0, 0], 1), [2, 4])

julia> 2 * am == am2
true

The application of an AffineMap to a ZeroSet or an EmptySet is simplified automatically.

julia> AffineMap(A, ZeroSet{Int}(2), b3)
Singleton{Int64, Vector{Int64}}([1, 2, 3])

julia> AffineMap(A, EmptySet{Int}(2), b3)
EmptySet{Int64}(2)
source

Inherited from AbstractAffineMap:

Inherited from ConvexSet`: