Common Set Operations

MinkowskiSum{T1<:LazySet, T2<:LazySet} <: LazySet

Type that represents the Minkowski sum of two convex sets.

Fields

• X – first convex set

• Y – second convex set

dim(ms::MinkowskiSum)::Int

Return the dimension of a Minkowski sum.

Input

• ms – Minkowski sum

Output

The ambient dimension of the Minkowski sum.

σ(d::AbstractVector{<:Real}, ms::MinkowskiSum)::AbstractVector{<:Real}

Return the support vector of a Minkowski sum.

Input

• d – direction

• ms – Minkowski sum

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the summand sets.

X + Y

Convenience constructor for Minkowski sum.

Input

• X – a convex set

• Y – another convex set

Output

The symbolic Minkowski sum of $X$ and $Y$.

⊕(X::LazySet, Y::LazySet)

Unicode alias constructor oplus for the Minkowski sum operator +(X, Y).

MinkowskiSumArray{T<:LazySet} <: LazySet

Type that represents the Minkowski sum of a finite number of convex sets.

Fields

• sfarray – array of convex sets

Notes

This type assumes that the dimensions of all elements match.

• MinkowskiSumArray(sfarray::Vector{<:LazySet}) – default constructor

• MinkowskiSumArray() – constructor for an empty sum

• MinkowskiSumArray(n::Int) – constructor for an empty sum with size hint

dim(msa::MinkowskiSumArray)::Int

Return the dimension of a Minkowski sum of a finite number of sets.

Input

• msa – Minkowski sum array

Output

The ambient dimension of the Minkowski sum of a finite number of sets.

σ(d::AbstractVector{<:Real}, msa::MinkowskiSumArray)::Vector{<:Real}

Return the support vector of a Minkowski sum of a finite number of sets in a given direction.

Input

• d – direction

• msa – Minkowski sum array

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the summand sets.

+(msa::MinkowskiSumArray, S::LazySet)::MinkowskiSumArray

Add a convex set to a Minkowski sum of a finite number of convex sets from the right.

Input

• msa – Minkowski sum array (is modified)

• S – convex set

Output

The modified Minkowski sum of a finite number of convex sets.

+(S::LazySet, msa::MinkowskiSumArray)::MinkowskiSumArray

Add a convex set to a Minkowski sum of a finite number of convex sets from the left.

Input

• S – convex set

• msa – Minkowski sum array (is modified)

Output

The modified Minkowski sum of a finite number of convex sets.

+(msa1::MinkowskiSumArray, msa2::MinkowskiSumArray)::MinkowskiSumArray

Add the elements of a finite Minkowski sum of convex sets to another finite Minkowski sum.

Input

• msa1 – first Minkowski sum array (is modified)

• msa2 – second Minkowski sum array

Output

The modified first Minkowski sum of a finite number of convex sets.

+(msa::MinkowskiSumArray, Z::ZeroSet)::MinkowskiSumArray

Returns the original array because addition with an empty set is a no-op.

Input

• msa – Minkowski sum array

• Z – a Zero set

CartesianProduct{S1<:LazySet,S2<:LazySet} <: LazySet

Type that represents a Cartesian product of two convex sets.

Fields

• X – first convex set

• Y – second convex set

Notes

The Cartesian product of three elements is obtained recursively. See also CartesianProductArray for an implementation of a Cartesian product of many sets without recursion, instead using an array.

• CartesianProduct{S1<:LazySet,S2<:LazySet} – default constructor

• CartesianProduct(Xarr::Vector{S}) where {S<:LazySet} – constructor from an array of convex sets

dim(cp::CartesianProduct)::Int

Return the dimension of a Cartesian product.

Input

• cp – Cartesian product

Output

The ambient dimension of the Cartesian product.

σ(d::AbstractVector{<:Real}, cp::CartesianProduct)::AbstractVector{<:Real}

Return the support vector of a Cartesian product.

Input

• d – direction

• cp – Cartesian product

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the product sets.

    *(X::LazySet, Y::LazySet)::CartesianProduct

Return the Cartesian product of two convex sets.

Input

• X – convex set

• Y – convex set

Output

The Cartesian product of the two convex sets.

∈(x::AbstractVector{<:Real}, cp::CartesianProduct)::Bool

Check whether a given point is contained in a Cartesian product set.

Input

• x – point/vector

• cp – Cartesian product

Output

true iff $x ∈ cp$.

CartesianProductArray{S<:LazySet} <: LazySet

Type that represents the Cartesian product of a finite number of convex sets.

Fields

• sfarray – array of sets

Notes

• CartesianProductArray(sfarray::Vector{<:LazySet}) – default constructor

• CartesianProductArray() – constructor for an empty Cartesian product

• CartesianProductArray(n::Int) – constructor for an empty Cartesian product with size hint

dim(cpa::CartesianProductArray)::Int

Return the dimension of a Cartesian product of a finite number of convex sets.

Input

• cpa – Cartesian product array

Output

The ambient dimension of the Cartesian product of a finite number of convex sets.

σ(d::AbstractVector{<:Real}, cpa::CartesianProductArray)::AbstractVector{<:Real}

Support vector of a Cartesian product.

Input

• d – direction

• cpa – Cartesian product array

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the product sets.

    *(cpa::CartesianProductArray, S::LazySet)::CartesianProductArray

Multiply a convex set to a Cartesian product of a finite number of convex sets from the right.

Input

• cpa – Cartesian product array (is modified)

• S – convex set

Output

The modified Cartesian product of a finite number of convex sets.

    *(S::LazySet, cpa::CartesianProductArray)::CartesianProductArray

Multiply a convex set to a Cartesian product of a finite number of convex sets from the left.

Input

• S – convex set

• cpa – Cartesian product array (is modified)

Output

The modified Cartesian product of a finite number of convex sets.

    *(cpa1::CartesianProductArray, cpa2::CartesianProductArray)::CartesianProductArray

Multiply a finite Cartesian product of convex sets to another finite Cartesian product.

Input

• cpa1 – first Cartesian product array (is modified)

• cpa2 – second Cartesian product array

Output

The modified first Cartesian product.

∈(x::AbstractVector{<:Real}, cpa::CartesianProductArray)::Bool

Check whether a given point is contained in a Cartesian product of a finite number of sets.

Input

• x – point/vector

• cpa – Cartesian product array

Output

true iff $x ∈ \text{cpa}$.

LinearMap{S<:LazySet, N<:Real} <: LazySet

Type that represents a linear transformation $M⋅S$ of a convex set $S$.

Fields

• M – matrix/linear map

• sf – convex set

dim(lm::LinearMap)::Int

Return the dimension of a linear map.

Input

• lm – linear map

Output

The ambient dimension of the linear map.

σ(d::AbstractVector{<:Real}, lm::LinearMap)::AbstractVector{<:Real}

Return the support vector of the linear map.

Input

• d – direction

• lm – linear map

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the wrapped set.

Notes

If $L = M⋅S$, where $M$ is a matrix and $S$ is a convex set, it follows that $σ(d, L) = M⋅σ(M^T d, S)$ for any direction $d$.

    *(M::AbstractMatrix{<:Real}, S::LazySet)

Return the linear map of a convex set.

Input

• M – matrix/linear map

• S – convex set

Output

If the matrix is null, a ZeroSet is returned; otherwise a lazy linear map.

    *(a::Real, S::LazySet)::LinearMap

Return a linear map of a convex set by a scalar value.

Input

• a – real scalar

• S – convex set

Output

The linear map of the convex set.

∈(x::AbstractVector{N}, lm::LinearMap{<:LazySet, N})::Bool where {N<:Real}

Check whether a given point is contained in a linear map of a convex set.

Input

• x – point/vector

• lm – linear map of a convex set

Output

true iff $x ∈ lm$.

Algorithm

Note that $x ∈ M⋅S$ iff $M^{-1}⋅x ∈ S$. This implementation does not explicitly invert the matrix, which is why it also works for non-square matrices.

Examples

julia> lm = LinearMap([2.0 0.0; 0.0 1.0], BallInf([1., 1.], 1.));

julia> ∈([5.0, 1.0], lm)
false
julia> ∈([3.0, 1.0], lm)
true

An example with non-square matrix:

julia> B = BallInf(zeros(4), 1.);

julia> M = [1. 0 0 0; 0 1 0 0]/2;

julia> ∈([0.5, 0.5], M*B)
true

ExponentialMap{S<:LazySet} <: LazySet

Type that represents the action of an exponential map on a convex set.

Fields

• spmexp – sparse matrix exponential

• X – convex set

dim(em::ExponentialMap)::Int

Return the dimension of an exponential map.

Input

• em – an ExponentialMap

Output

The ambient dimension of the exponential map.

σ(d::AbstractVector{Float64}, em::ExponentialMap)::AbstractVector{Float64}

Return the support vector of the exponential map.

Input

• d – direction

• em – exponential map

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the wrapped set.

Notes

If $E = \exp(M)⋅S$, where $M$ is a matrix and $S$ is a convex set, it follows that $σ(d, E) = \exp(M)⋅σ(\exp(M)^T d, S)$ for any direction $d$.

∈(x::AbstractVector{<:Real}, em::ExponentialMap{<:LazySet})::Bool

Check whether a given point is contained in an exponential map of a convex set.

Input

• x – point/vector

• em – linear map of a convex set

Output

true iff $x ∈ em$.

Algorithm

This implementation exploits that $x ∈ \exp(M)⋅S$ iff $\exp(-M)⋅x ∈ S$. This follows from $\exp(-M)⋅\exp(M) = I$ for any $M$.

Examples

julia> em = ExponentialMap(SparseMatrixExp(SparseMatrixCSC([2.0 0.0; 0.0 1.0])),
                           BallInf([1., 1.], 1.));

julia> ∈([5.0, 1.0], em)
false
julia> ∈([1.0, 1.0], em)
true

ExponentialProjectionMap{S<:LazySet} <: LazySet

Type that represents the application of a projection of a sparse matrix exponential to a convex set.

Fields

• spmexp – projection of a sparse matrix exponential

• X – convex set

dim(eprojmap::ExponentialProjectionMap)::Int

Return the dimension of a projection of an exponential map.

Input

• eprojmap – projection of an exponential map

Output

The ambient dimension of the projection of an exponential map.

σ(d::AbstractVector{Float64}, eprojmap::ExponentialProjectionMap)::AbstractVector{Float64}

Return the support vector of a projection of an exponential map.

Input

• d – direction

• eprojmap – projection of an exponential map

Output

The support vector in the given direction. If the direction has norm zero, the result depends on the wrapped set.

Notes

If $S = (L⋅M⋅R)⋅X$, where $L$ and $R$ are matrices, $M$ is a matrix exponential, and $X$ is a set, it follows that $σ(d, S) = L⋅M⋅R⋅σ(R^T⋅M^T⋅L^T⋅d, X)$ for any direction $d$.

SparseMatrixExp{N<:Real}

Type that represents the matrix exponential, $\exp(M)$, of a sparse matrix.

Fields

• M – sparse matrix

Notes

This type is provided for use with very large and very sparse matrices. The evaluation of the exponential matrix action over vectors relies on the Expokit package.

    *(spmexp::SparseMatrixExp, X::LazySet)::ExponentialMap

Return the exponential map of a convex set from a sparse matrix exponential.

Input

• spmexp – sparse matrix exponential

• X – convex set

Output

The exponential map of the convex set.

ProjectionSparseMatrixExp{N<:Real}

Type that represents the projection of a sparse matrix exponential, i.e., $L⋅\exp(M)⋅R$ for a given sparse matrix $M$.

Fields

• L – left multiplication matrix

• E – sparse matrix exponential

• R – right multiplication matrix

    *(projspmexp::ProjectionSparseMatrixExp, X::LazySet)::ExponentialProjectionMap

Return the application of a projection of a sparse matrix exponential to a convex set.

Input

• projspmexp – projection of a sparse matrix exponential

• X – convex set

Output

The application of the projection of a sparse matrix exponential to the convex set.

ConvexHull{S1<:LazySet, S2<:LazySet} <: LazySet

Type that represents the convex hull of the union of two convex sets.

Fields

• X – convex set

• Y – convex set

CH

Alias for ConvexHull.

dim(ch::ConvexHull)::Int

Return the dimension of a convex hull of two convex sets.

Input

• ch – convex hull of two convex sets

Output

The ambient dimension of the convex hull of two convex sets.

σ(d::AbstractVector{<:Real}, ch::ConvexHull)::AbstractVector{<:Real}

Return the support vector of a convex hull of two convex sets in a given direction.

Input

• d – direction

• ch – convex hull of two convex sets

convex_hull(points::Vector{S}; [algorithm]::String="monotone_chain"
           )::Vector{S} where {S<:AbstractVector{N}} where {N<:Real}

Compute the convex hull of points in the plane.

Input

• points – list of 2D vectors

• algorithm – (optional, default: "monotone_chain") the convex hull algorithm, valid options are:

  • "monotone_chain"

  • "monotone_chain_sorted"

Output

The convex hull as a list of 2D vectors with the coordinates of the points.

Examples

Compute the convex hull of a random set of points:

julia> points = [randn(2) for i in 1:30]; # 30 random points in 2D

julia> hull = convex_hull(points);

julia> typeof(hull)
Array{Array{Float64,1},1}

Plot both the random points and the computed convex hull polygon:

julia> using Plots;

julia> plot([Tuple(pi) for pi in points], seriestype=:scatter);

julia> plot!(VPolygon(hull), alpha=0.2);

convex_hull!(points::Vector{S}; [algorithm]::String="monotone_chain"
            )::Vector{S} where {S<:AbstractVector{N}} where {N<:Real}

Compute the convex hull of points in the plane, in-place.

Input

• points – list of 2D vectors (is modified)

• algorithm – (optional, default: "monotone_chain") the convex hull algorithm; valid options are:

  • "monotone_chain"

  • "monotone_chain_sorted"

Output

The convex hull as a list of 2D vectors with the coordinates of the points.

Notes

See the non-modifying version convex_hull for more details.

right_turn(O::AbstractVector{N}, A::AbstractVector{N}, B::AbstractVector{N}
          )::N where {N<:Real}

Determine if the acute angle defined by the three points O, A, B in the plane is a right turn (counter-clockwise) with respect to the center O.

Input

• O – 2D center point

• A – 2D one point

• B – 2D another point

Output

Scalar representing the rotation.

Algorithm

The cross product is used to determine the sense of rotation. If the result is 0, the points are collinear; if it is positive, the three points constitute a positive angle of rotation around O from A to B; otherwise they constitute a negative angle.

monotone_chain!(points::Vector{S}; sort::Bool=true
               )::Vector{S} where {S<:AbstractVector{N}} where {N<:Real}

Compute the convex hull of points in the plane using Andrew's monotone chain method.

Input

• points – list of 2D vectors; is sorted in-place inside this function

• sort – (optional, default: true) flag for sorting the vertices lexicographically; sortedness is required for correctness

Output

List of vectors containing the 2D coordinates of the corner points of the convex hull.

Notes

For large sets of points, it is convenient to use static vectors to get maximum performance. For information on how to convert usual vectors into static vectors, see the type SVector provided by the StaticArrays package.

Algorithm

This function implements Andrew's monotone chain convex hull algorithm to construct the convex hull of a set of $n$ points in the plane in $O(n \log n)$ time. For further details see Monotone chain