Common Set Representations

Ball2{N<:AbstractFloat} <: AbstractCentrallySymmetric{N}

Type that represents a ball in the 2-norm.

Fields

• center – center of the ball as a real vector
• radius – radius of the ball as a real scalar ($≥ 0$)

Notes

Mathematically, a ball in the 2-norm is defined as the set

where $c ∈ \mathbb{R}^n$ is its center and $r ∈ \mathbb{R}_+$ its radius. Here $‖ ⋅ ‖_2$ denotes the Euclidean norm (also known as 2-norm), defined as $‖ x ‖_2 = \left( \sum\limits_{i=1}^n |x_i|^2 \right)^{1/2}$ for any $x ∈ \mathbb{R}^n$.

Examples

Create a five-dimensional ball B in the 2-norm centered at the origin with radius 0.5:

julia> B = Ball2(zeros(5), 0.5)
Ball2{Float64}([0.0, 0.0, 0.0, 0.0, 0.0], 0.5)
julia> dim(B)
5

Evaluate B's support vector in the direction $[1,2,3,4,5]$:

julia> σ([1.,2.,3.,4.,5.], B)
5-element Array{Float64,1}:
 0.06741998624632421
 0.13483997249264842
 0.20225995873897262
 0.26967994498529685
 0.3370999312316211

σ(d::AbstractVector{N}, B::Ball2{N}) where {N<:AbstractFloat}

Return the support vector of a 2-norm ball in a given direction.

Input

• d – direction
• B – ball in the 2-norm

Output

The support vector in the given direction. If the direction has norm zero, the origin is returned.

Notes

Let $c$ and $r$ be the center and radius of a ball $B$ in the 2-norm, respectively. For nonzero direction $d$ we have

This function requires computing the 2-norm of the input direction, which is performed in the given precision of the numeric datatype of both the direction and the set. Exact inputs are not supported.

∈(x::AbstractVector{N}, B::Ball2{N})::Bool where {N<:AbstractFloat}

Check whether a given point is contained in a ball in the 2-norm.

Input

• x – point/vector
• B – ball in the 2-norm

Output

true iff $x ∈ B$.

Notes

This implementation is worst-case optimized, i.e., it is optimistic and first computes (see below) the whole sum before comparing to the radius. In applications where the point is typically far away from the ball, a fail-fast implementation with interleaved comparisons could be more efficient.

Algorithm

Let $B$ be an $n$-dimensional ball in the 2-norm with radius $r$ and let $c_i$ and $x_i$ be the ball's center and the vector $x$ in dimension $i$, respectively. Then $x ∈ B$ iff $\left( ∑_{i=1}^n |c_i - x_i|^2 \right)^{1/2} ≤ r$.

Examples

julia> B = Ball2([1., 1.], sqrt(0.5))
Ball2{Float64}([1.0, 1.0], 0.7071067811865476)
julia> ∈([.5, 1.6], B)
false
julia> ∈([.5, 1.5], B)
true

center(B::Ball2{N})::Vector{N} where {N<:AbstractFloat}

Return the center of a ball in the 2-norm.

Input

• B – ball in the 2-norm

Output

The center of the ball in the 2-norm.

rand(::Type{Ball2}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Ball2{N}

Create a random ball in the 2-norm.

Input

• Ball2 – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random ball in the 2-norm.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the radius is nonnegative.

BallInf{N<:Real} <: AbstractHyperrectangle{N}

Type that represents a ball in the infinity norm.

Fields

• center – center of the ball as a real vector
• radius – radius of the ball as a real scalar ($≥ 0$)

Notes

Mathematically, a ball in the infinity norm is defined as the set

where $c ∈ \mathbb{R}^n$ is its center and $r ∈ \mathbb{R}_+$ its radius. Here $‖ ⋅ ‖_∞$ denotes the infinity norm, defined as $‖ x ‖_∞ = \max\limits_{i=1,…,n} \vert x_i \vert$ for any $x ∈ \mathbb{R}^n$.

Examples

Create the two-dimensional unit ball and compute its support function along the positive $x=y$ direction:

julia> B = BallInf(zeros(2), 1.0)
BallInf{Float64}([0.0, 0.0], 1.0)
julia> dim(B)
2
julia> ρ([1., 1.], B)
2.0

center(B::BallInf{N})::Vector{N} where {N<:Real}

Return the center of a ball in the infinity norm.

Input

• B – ball in the infinity norm

Output

The center of the ball in the infinity norm.

radius(B::BallInf, [p]::Real=Inf)::Real

Return the radius of a ball in the infinity norm.

Input

• B – ball in the infinity norm
• p – (optional, default: Inf) norm

Output

A real number representing the radius.

Notes

The radius is defined as the radius of the enclosing ball of the given $p$-norm of minimal volume with the same center.

radius_hyperrectangle(B::BallInf{N})::Vector{N} where {N<:Real}

Return the box radius of a infinity norm ball, which is the same in every dimension.

Input

• B – infinity norm ball

Output

The box radius of the ball in the infinity norm.

radius_hyperrectangle(B::BallInf{N}, i::Int)::N where {N<:Real}

Return the box radius of a infinity norm ball in a given dimension.

Input

• B – infinity norm ball

Output

The box radius of the ball in the infinity norm in the given dimension.

rand(::Type{BallInf}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::BallInf{N}

Create a random ball in the infinity norm.

Input

• BallInf – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random ball in the infinity norm.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the radius is nonnegative.

Ball1{N<:Real} <: AbstractCentrallySymmetricPolytope{N}

Type that represents a ball in the 1-norm, also known as Manhattan or Taxicab norm.

It is defined as the set

where $c ∈ \mathbb{R}^n$ is its center and $r ∈ \mathbb{R}_+$ its radius.

Fields

• center – center of the ball as a real vector
• radius – radius of the ball as a scalar ($≥ 0$)

Examples

Unit ball in the 1-norm in the plane:

julia> B = Ball1(zeros(2), 1.)
Ball1{Float64}([0.0, 0.0], 1.0)
julia> dim(B)
2

We evaluate the support vector in the East direction:

julia> σ([0.,1], B)
2-element Array{Float64,1}:
 0.0
 1.0

σ(d::AbstractVector{N}, B::Ball1{N}) where {N<:Real}

Return the support vector of a ball in the 1-norm in a given direction.

Input

• d – direction
• B – ball in the 1-norm

Output

Support vector in the given direction.

∈(x::AbstractVector{N}, B::Ball1{N})::Bool where {N<:Real}

Check whether a given point is contained in a ball in the 1-norm.

Input

• x – point/vector
• B – ball in the 1-norm

Output

true iff $x ∈ B$.

Notes

This implementation is worst-case optimized, i.e., it is optimistic and first computes (see below) the whole sum before comparing to the radius. In applications where the point is typically far away from the ball, a fail-fast implementation with interleaved comparisons could be more efficient.

Algorithm

Let $B$ be an $n$-dimensional ball in the 1-norm with radius $r$ and let $c_i$ and $x_i$ be the ball's center and the vector $x$ in dimension $i$, respectively. Then $x ∈ B$ iff $∑_{i=1}^n |c_i - x_i| ≤ r$.

Examples

julia> B = Ball1([1., 1.], 1.);

julia> ∈([.5, -.5], B)
false
julia> ∈([.5, 1.5], B)
true

vertices_list(B::Ball1{N})::Vector{Vector{N}} where {N<:Real}

Return the list of vertices of a ball in the 1-norm.

Input

• B – ball in the 1-norm

Output

A list containing the vertices of the ball in the 1-norm.

center(B::Ball1{N})::Vector{N} where {N<:Real}

Return the center of a ball in the 1-norm.

Input

• B – ball in the 1-norm

Output

The center of the ball in the 1-norm.

rand(::Type{Ball1}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Ball1{N}

Create a random ball in the 1-norm.

Input

• Ball1 – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random ball in the 1-norm.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the radius is nonnegative.

constraints_list(P::Ball1{N})::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a ball in the 1-norm.

Input

• B – ball in the 1-norm

Output

The list of constraints of the ball.

Algorithm

The constraints can be defined as $d_i^T (x-c) ≤ r$ for all $d_i$, where $d_i$ is a vector with elements $1$ or $-1$ in $n$ dimensions. To span all possible $d_i$, the function Iterators.product is used.

Ballp{N<:AbstractFloat} <: AbstractCentrallySymmetric{N}

Type that represents a ball in the p-norm, for $1 ≤ p ≤ ∞$.

It is defined as the set

where $c ∈ \mathbb{R}^n$ is its center and $r ∈ \mathbb{R}_+$ its radius. Here $‖ ⋅ ‖_p$ for $1 ≤ p ≤ ∞$ denotes the vector $p$-norm, defined as $‖ x ‖_p = \left( \sum\limits_{i=1}^n |x_i|^p \right)^{1/p}$ for any $x ∈ \mathbb{R}^n$.

Fields

• p – norm as a real scalar
• center – center of the ball as a real vector
• radius – radius of the ball as a scalar ($≥ 0$)

Notes

The special cases $p=1$, $p=2$ and $p=∞$ fall back to the specialized types Ball1, Ball2 and BallInf, respectively.

Examples

A five-dimensional ball in the $p=3/2$ norm centered at the origin of radius 0.5:

julia> B = Ballp(3/2, zeros(5), 0.5)
Ballp{Float64}(1.5, [0.0, 0.0, 0.0, 0.0, 0.0], 0.5)
julia> dim(B)
5

We evaluate the support vector in direction $[1,2,…,5]$:

julia> σ([1., 2, 3, 4, 5], B)
5-element Array{Float64,1}:
 0.013516004434607558
 0.05406401773843023
 0.12164403991146802
 0.21625607095372093
 0.33790011086518895

σ(d::AbstractVector{N}, B::Ballp{N}) where {N<:AbstractFloat}

Return the support vector of a Ballp in a given direction.

Input

• d – direction
• B – ball in the p-norm

Output

The support vector in the given direction. If the direction has norm zero, the center of the ball is returned.

Algorithm

The support vector of the unit ball in the $p$-norm along direction $d$ is:

where $\tilde{v}_i = \frac{|d_i|^q}{d_i}$ if $d_i ≠ 0$ and $tilde{v}_i = 0$ otherwise, for all $i=1,…,n$, and $q$ is the conjugate number of $p$. By the affine transformation $x = r\tilde{x} + c$, one obtains that the support vector of $\mathcal{B}_p^n(c, r)$ is

where $v_i = c_i + r\frac{|d_i|^q}{d_i}$ if $d_i ≠ 0$ and $v_i = 0$ otherwise, for all $i = 1, …, n$.

∈(x::AbstractVector{N}, B::Ballp{N})::Bool where {N<:AbstractFloat}

Check whether a given point is contained in a ball in the p-norm.

Input

• x – point/vector
• B – ball in the p-norm

Output

true iff $x ∈ B$.

Notes

This implementation is worst-case optimized, i.e., it is optimistic and first computes (see below) the whole sum before comparing to the radius. In applications where the point is typically far away from the ball, a fail-fast implementation with interleaved comparisons could be more efficient.

Algorithm

Let $B$ be an $n$-dimensional ball in the p-norm with radius $r$ and let $c_i$ and $x_i$ be the ball's center and the vector $x$ in dimension $i$, respectively. Then $x ∈ B$ iff $\left( ∑_{i=1}^n |c_i - x_i|^p \right)^{1/p} ≤ r$.

Examples

julia> B = Ballp(1.5, [1., 1.], 1.)
Ballp{Float64}(1.5, [1.0, 1.0], 1.0)
julia> ∈([.5, -.5], B)
false
julia> ∈([.5, 1.5], B)
true

center(B::Ballp{N})::Vector{N} where {N<:AbstractFloat}

Return the center of a ball in the p-norm.

Input

• B – ball in the p-norm

Output

The center of the ball in the p-norm.

rand(::Type{Ballp}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Ballp{N}

Create a random ball in the p-norm.

Input

• Ballp – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random ball in the p-norm.

Algorithm

The center and radius are normally distributed with mean 0 and standard deviation 1. Additionally, the radius is nonnegative. The p-norm is a normally distributed number ≥ 1 with mean 1 and standard deviation 1.

Ellipsoid{N<:AbstractFloat} <:  AbstractCentrallySymmetric{N}

Type that represents an ellipsoid.

It is defined as the set

where $c \in \mathbb{R}^n$ is its center and $Q \in \mathbb{R}^{n×n}$ its shape matrix, which should be a positive definite matrix. An ellipsoid can also be characterized as the image of a Euclidean ball by an invertible linear transformation. It is the higher-dimensional generalization of an ellipse.

Fields

• center – center of the ellipsoid
• shape matrix – real positive definite matrix, i.e. it is equal to its transpose and $x^\mathrm{T}Qx > 0$ for all nonzero $x$

Examples

If the center is not specified, it is assumed that the center is the origin. For instance, a 3D ellipsoid with center at the origin and the shape matrix being the identity can be created with:

julia> E = Ellipsoid(Matrix{Float64}(I, 3, 3))
Ellipsoid{Float64}([0.0, 0.0, 0.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0])

julia> dim(E)
3

julia> an_element(E)
3-element Array{Float64,1}:
 0.0
 0.0
 0.0

This ellipsoid corresponds to the unit Euclidean ball. Let's evaluate its support vector in a given direction:

julia> σ(ones(3), E)
3-element Array{Float64,1}:
 0.5773502691896258
 0.5773502691896258
 0.5773502691896258

A two-dimensional ellipsoid with given center and shape matrix:

julia> E = Ellipsoid(ones(2), Diagonal([2.0, 0.5]))
Ellipsoid{Float64}([1.0, 1.0], [2.0 0.0; 0.0 0.5])

σ(d::AbstractVector{N}, E::Ellipsoid{N}) where {N<:AbstractFloat}

Return the support vector of an ellipsoid in a given direction.

Input

• d – direction
• E – ellipsoid

Output

Support vector in the given direction.

Algorithm

Let $E$ be an ellipsoid of center $c$ and shape matrix $Q = BB^\mathrm{T}$. Its support vector along direction $d$ can be deduced from that of the unit Euclidean ball $\mathcal{B}_2$ using the algebraic relations for the support vector,

∈(x::AbstractVector{N}, E::Ellipsoid{N})::Bool where {N<:AbstractFloat}

Check whether a given point is contained in an ellipsoid.

Input

• x – point/vector
• E – ellipsoid

Output

true iff x ∈ E.

Algorithm

The point $x$ belongs to the ellipsoid of center $c$ and shape matrix $Q$ if and only if

rand(::Type{Ellipsoid}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Ellipsoid{N}

Create a random ellipsoid.

Input

• Ellipsoid – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random ellipsoid.

Algorithm

The center is a normally distributed vector with entries of mean 0 and standard deviation 1.

The idea for the shape matrix comes from here. The matrix is symmetric positive definite, but also diagonally dominant.

where $n$ = dim (defaults to 2), and $S$ is a $n \times n$ random matrix whose coefficients are uniformly distributed in the interval $[-1, 1]$.

center(E::Ellipsoid{N})::Vector{N} where {N<:AbstractFloat}

Return the center of the ellipsoid.

Input

• E – ellipsoid

Output

The center of the ellipsoid.

EmptySet{N<:Real} <: LazySet{N}

Type that represents the empty set, i.e., the set with no elements.

∅

An EmptySet instance of type Float64.

dim(∅::EmptySet)

Return the dimension of the empty set, which is -1 by convention.

Input

• ∅ – an empty set

Output

-1 by convention.

σ(d::AbstractVector{N}, ∅::EmptySet{N}) where {N<:Real}

Return the support vector of an empty set.

Input

• ∅ – an empty set

Output

An error.

∈(x::AbstractVector{N}, ∅::EmptySet{N})::Bool where {N<:Real}

Check whether a given point is contained in an empty set.

Input

• x – point/vector
• ∅ – empty set

Output

The output is always false.

Examples

julia> ∈([1.0, 0.0], ∅)
false

an_element(∅::EmptySet)

Return some element of an empty set.

Input

• ∅ – empty set

Output

An error.

rand(::Type{EmptySet}; [N]::Type{<:Real}=Float64, [dim]::Int=0,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::EmptySet{N}

Create an empty set (note that there is nothing to randomize).

Input

• EmptySet – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 0) dimension (is ignored)
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

The (only) empty set of the given numeric type.

isbounded(∅::EmptySet)::Bool

Determine whether an empty set is bounded.

Input

• ∅ – empty set

Output

true.

isempty(∅::EmptySet)::Bool

Return if the empty set is empty or not.

Input

• ∅ – empty set

Output

true.

norm(S::EmptySet, [p]::Real=Inf)

Return the norm of an empty set. It is the norm of the enclosing ball (of the given $p$-norm) of minimal volume that is centered in the origin.

Input

• S – empty set
• p – (optional, default: Inf) norm

Output

An error.

radius(S::EmptySet, [p]::Real=Inf)

Return the radius of an empty set. It is the radius of the enclosing ball (of the given $p$-norm) of minimal volume with the same center.

Input

• S – empty set
• p – (optional, default: Inf) norm

Output

An error.

diameter(S::EmptySet, [p]::Real=Inf)

Return the diameter of an empty set. It is the maximum distance between any two elements of the set, or, equivalently, the diameter of the enclosing ball (of the given $p$-norm) of minimal volume with the same center.

Input

• S – empty set
• p – (optional, default: Inf) norm

Output

An error.

HalfSpace{N<:Real} <: LazySet{N}

Type that represents a (closed) half-space of the form $a⋅x ≤ b$.

Fields

• a – normal direction
• b – constraint

Examples

The set $y ≥ 0$ in the plane:

julia> HalfSpace([0, -1.], 0.)
HalfSpace{Float64}([0.0, -1.0], 0.0)

LinearConstraint

Alias for HalfSpace

dim(hs::HalfSpace)::Int

Return the dimension of a half-space.

Input

• hs – half-space

Output

The ambient dimension of the half-space.

ρ(d::AbstractVector{N}, hs::HalfSpace{N})::N where {N<:Real}

Evaluate the support function of a half-space in a given direction.

Input

• d – direction
• hs – half-space

Output

The support function of the half-space. If the set is unbounded in the given direction, the result is Inf.

σ(d::AbstractVector{N}, hs::HalfSpace{N}) where {N<:Real}

Return the support vector of a half-space.

Input

• d – direction
• hs – half-space

Output

The support vector in the given direction, which is only defined in the following two cases:

1. The direction has norm zero.
2. The direction is the half-space's normal direction.

In both cases the result is any point on the boundary (the defining hyperplane). Otherwise this function throws an error.

∈(x::AbstractVector{N}, hs::HalfSpace{N})::Bool where {N<:Real}

Check whether a given point is contained in a half-space.

Input

• x – point/vector
• hs – half-space

Output

true iff $x ∈ hs$.

Algorithm

We just check if $x$ satisfies $a⋅x ≤ b$.

an_element(hs::HalfSpace{N})::Vector{N} where {N<:Real}

Return some element of a half-space.

Input

• hs – half-space

Output

An element on the defining hyperplane.

rand(::Type{HalfSpace}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::HalfSpace{N}

Create a random half-space.

Input

• HalfSpace – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random half-space.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the constraint a is nonzero.

isbounded(hs::HalfSpace)::Bool

Determine whether a half-space is bounded.

Input

• hs – half-space

Output

false.

isempty(hs::HalfSpace)::Bool

Return if a half-space is empty or not.

Input

• hs – half-space

Output

false.

constraints_list(hs::HalfSpace{N})::Vector{LinearConstraint{N}}
    where {N<:Real}

Return the list of constraints of a half-space.

Input

• hs – half-space

Output

A singleton list containing the half-space.

constrained_dimensions(hs::HalfSpace{N})::Vector{Int} where {N<:Real}

Return the indices in which a half-space is constrained.

Input

• hs – half-space

Output

A vector of ascending indices i such that the half-space is constrained in dimension i.

Examples

A 2D half-space with constraint $x1 ≥ 0$ is constrained in dimension 1 only.

halfspace_left(p::AbstractVector{N},
               q::AbstractVector{N})::HalfSpace{N} where {N<:Real}

Return a half-space describing the 'left' of a two-dimensional line segment through two points.

Input

• p – first point
• q – second point

Output

The half-space whose boundary goes through the two points p and q and which describes the left-hand side of the directed line segment pq.

Algorithm

The implementation is simple: the half-space $a⋅x ≤ b$ is calculated as a = [dy, -dx], where $d = (dx, dy)$ denotes the line segment pq, that is, $\vec{d} = \vec{p} - \vec{q}$, and b = dot(p, a).

Examples

The left half-space of the "east" and "west" directions in two-dimensions are the upper and lower half-spaces:

julia> import LazySets.halfspace_left

julia> halfspace_left([0.0, 0.0], [1.0, 0.0])
HalfSpace{Float64}([0.0, -1.0], 0.0)

julia> halfspace_left([0.0, 0.0], [-1.0, 0.0])
HalfSpace{Float64}([0.0, 1.0], 0.0)

We create a box from the sequence of line segments that describe its edges:

julia> H1 = halfspace_left([-1.0, -1.0], [1.0, -1.0]);

julia> H2 = halfspace_left([1.0, -1.0], [1.0, 1.0]);

julia> H3 = halfspace_left([1.0, 1.0], [-1.0, 1.0]);

julia> H4 = halfspace_left([-1.0, 1.0], [-1.0, -1.0]);

julia> H = HPolygon([H1, H2, H3, H4]);

julia> B = BallInf([0.0, 0.0], 1.0);

julia> B ⊆ H && H ⊆ B
true

halfspace_right(p::AbstractVector{N},
                q::AbstractVector{N})::HalfSpace{N} where {N<:Real}

Return a half-space describing the 'right' of a two-dimensional line segment through two points.

Input

• p – first point
• q – second point

Output

The half-space whose boundary goes through the two points p and q and which describes the right-hand side of the directed line segment pq.

Algorithm

See the documentation of halfspace_left.

Hyperplane{N<:Real} <: LazySet{N}

Type that represents a hyperplane of the form $a⋅x = b$.

Fields

• a – normal direction
• b – constraint

Examples

The plane $y = 0$:

julia> Hyperplane([0, 1.], 0.)
Hyperplane{Float64}([0.0, 1.0], 0.0)

dim(hp::Hyperplane)::Int

Return the dimension of a hyperplane.

Input

• hp – hyperplane

Output

The ambient dimension of the hyperplane.

ρ(d::AbstractVector{N}, hp::Hyperplane{N})::N where {N<:Real}

Evaluate the support function of a hyperplane in a given direction.

Input

• d – direction
• hp – hyperplane

Output

The support function of the hyperplane. If the set is unbounded in the given direction, the result is Inf.

σ(d::AbstractVector{N}, hp::Hyperplane{N}) where {N<:Real}

Return the support vector of a hyperplane.

Input

• d – direction
• hp – hyperplane

Output

The support vector in the given direction, which is only defined in the following two cases:

1. The direction has norm zero.
2. The direction is the hyperplane's normal direction or its opposite direction.

In all cases, the result is any point on the hyperplane. Otherwise this function throws an error.

∈(x::AbstractVector{N}, hp::Hyperplane{N})::Bool where {N<:Real}

Check whether a given point is contained in a hyperplane.

Input

• x – point/vector
• hp – hyperplane

Output

true iff $x ∈ hp$.

Algorithm

We just check if $x$ satisfies $a⋅x = b$.

an_element(hp::Hyperplane{N})::Vector{N} where {N<:Real}

Return some element of a hyperplane.

Input

• hp – hyperplane

Output

An element on the hyperplane.

rand(::Type{Hyperplane}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Hyperplane{N}

Create a random hyperplane.

Input

• Hyperplane – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random hyperplane.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the constraint a is nonzero.

isbounded(hp::Hyperplane)::Bool

Determine whether a hyperplane is bounded.

Input

• hp – hyperplane

Output

false.

isempty(hp::Hyperplane)::Bool

Return if a hyperplane is empty or not.

Input

• hp – hyperplane

Output

false.

constrained_dimensions(hp::Hyperplane{N})::Vector{Int} where {N<:Real}

Return the indices in which a hyperplane is constrained.

Input

• hp – hyperplane

Output

A vector of ascending indices i such that the hyperplane is constrained in dimension i.

Examples

A 2D hyperplane with constraint $x1 = 0$ is constrained in dimension 1 only.

Hyperrectangle{N<:Real} <: AbstractHyperrectangle{N}

Type that represents a hyperrectangle.

A hyperrectangle is the Cartesian product of one-dimensional intervals.

Fields

• center – center of the hyperrectangle as a real vector
• radius – radius of the ball as a real vector, i.e., half of its width along each coordinate direction

Examples

There is also a constructor from lower and upper bounds with keyword arguments high and low. The following two constructions are equivalent:

julia> c = ones(2);

julia> r = [0.1, 0.2];

julia> l = [0.9, 0.8];

julia> h = [1.1, 1.2];

julia> Hyperrectangle(c, r)
Hyperrectangle{Float64}([1.0, 1.0], [0.1, 0.2])
julia> Hyperrectangle(low=l, high=h)
Hyperrectangle{Float64}([1.0, 1.0], [0.1, 0.2])

rand(::Type{Hyperrectangle}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Hyperrectangle{N}

Create a random hyperrectangle.

Input

• Hyperrectangle – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random hyperrectangle.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the radius is nonnegative.

center(H::Hyperrectangle{N})::Vector{N} where {N<:Real}

Return the center of a hyperrectangle.

Input

• H – hyperrectangle

Output

The center of the hyperrectangle.

radius_hyperrectangle(H::Hyperrectangle{N})::Vector{N} where {N<:Real}

Return the box radius of a hyperrectangle in every dimension.

Input

• H – hyperrectangle

Output

The box radius of the hyperrectangle.

radius_hyperrectangle(H::Hyperrectangle{N}, i::Int)::N where {N<:Real}

Return the box radius of a hyperrectangle in a given dimension.

Input

• H – hyperrectangle

Output

The radius in the given dimension.

Interval{N<:Real, IN<:AbstractInterval{N}} <: AbstractHyperrectangle{N}

Type representing an interval on the real line. Mathematically, it is of the form

Fields

• dat – data container for the given interval

Notes

This type relies on the IntervalArithmetic.jl library for representation of intervals and arithmetic operations.

Examples

Unidimensional intervals are symbolic representations of a real closed interval.

We can create intervals in different ways, the simpler way is to pass a pair of numbers:

julia> x = Interval(0.0, 1.0)
Interval{Float64,IntervalArithmetic.Interval{Float64}}([0, 1])

or a 2-vector:

julia> x = Interval([0.0, 1.0])
Interval{Float64,IntervalArithmetic.Interval{Float64}}([0, 1])

Note that if the package IntervalArithmetic is loaded in the current scope, you have to prepend the LazySets to the interval type, since there is a name conflict otherwise.

julia> using IntervalArithmetic
WARNING: using IntervalArithmetic.Interval in module Main conflicts with an existing identifier.

julia> x = Interval(IntervalArithmetic.Interval(0.0, 1.0))
Interval{Float64,IntervalArithmetic.Interval{Float64}}([0, 1])

julia> dim(x)
1

julia> center(x)
1-element Array{Float64,1}:
 0.5

This type is such that the usual pairwise arithmetic operators +, -, * trigger the corresponding interval arithmetic backend method, and return a new Interval object. For the symbolic Minkowksi sum, use MinkowskiSum or ⊕.

Interval of other numeric types can be created as well, eg. a rational interval:

julia> Interval(0//1, 2//1)
Interval{Rational{Int64},AbstractInterval{Rational{Int64}}}([0//1, 2//1])

dim(x::Interval)::Int

Return the ambient dimension of an interval.

Input

• x – interval

Output

The integer 1.

σ(d::AbstractVector{N}, x::Interval{N}) where {N<:Real}

Return the support vector of an ellipsoid in a given direction.

Input

• d – direction
• x – interval

Output

Support vector in the given direction.

∈(v::AbstractVector{N}, x::Interval{N}) where {N<:Real})

Return whether a vector is contained in the interval.

Input

• v – one-dimensional vector
• x – interval

Output

true iff x contains v's first component.

∈(v::N, x::Interval{N}) where {N<:Real}

Return whether a number is contained in the interval.

Input

• v – scalar
• x – interval

Output

true iff x contains v.

an_element(x::Interval{N})::Vector{N} where {N<:Real}

Return some element of an interval.

Input

• x – interval

Output

The left border (min(x)) of the interval.

vertices_list(x::Interval{N})::Vector{Vector{N}} where {N<:Real}

Return the list of vertices of this interval.

Input

• x – interval

Output

The list of vertices of the interval represented as two one-dimensional vectors.

center(x::Interval{N})::Vector{N} where {N<:Real}

Return the interval's center.

Input

• x – interval

Output

The center, or midpoint, of $x$.

min(x::Interval{N})::N where {N<:Real}

Return the lower component of an interval.

Input

• x – interval

Output

The lower (lo) component of the interval.

max(x::Interval{N})::N where {N<:Real}

Return the higher or upper component of an interval.

Input

• x – interval

Output

The higher (hi) component of the interval.

low(x::Interval{N})::Vector{N} where {N<:Real}

Return the lower coordinate of an interval set.

Input

• x – interval

Output

A vector with the lower coordinate of the interval.

high(x::Interval{N})::Vector{N} where {N<:Real}

Return the higher coordinate of an interval set.

Input

• x – interval

Output

A vector with the higher coordinate of the interval.

radius_hyperrectangle(x::Interval{N})::Vector{N} where {N<:Real}

Return the box radius of an interval in every dimension.

Input

• x – interval

Output

The box radius of the interval (a one-dimensional vector).

radius_hyperrectangle(x::Interval{N}, i::Int)::N where {N<:Real}

Return the box radius of an interval in a given dimension.

Input

• x – interval
• i – dimension index (must be 1)

Output

The box radius in the given dimension.

+(x::Interval{N}, y::Interval{N}) where {N<:Real}

Return the sum of the intervals.

Input

• x – interval
• y – interval

Output

The sum of the intervals as a new Interval set.

-(x::Interval{N}, y::Interval{N}) where {N<:Real}

Return the difference of the intervals.

Input

• x – interval
• y – interval

Output

The difference of the intervals as a new Interval set.

    *(x::Interval{N}, y::Interval{N}) where {N<:Real}

Return the product of the intervals.

Input

• x – interval
• y – interval

Output

The product of the intervals as a new Interval set.

rand(::Type{Interval}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Interval{N}

Create a random interval.

Input

• Interval – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 1) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random interval.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1.

plot_interval(I::Interval; ...)

Plot an interval.

Input

• I – interval

Examples

julia> using Plots, LazySets;

julia> I = Interval(0.0, 1.0);

julia> plot(I);

plot_intervals(Xk::Vector{S}; ...) where {S<:Interval}

Plot an array of intervals.

Input

• Xk – linear array of intervals

Examples

julia> using Plots, LazySets;

julia> I1 = Interval([0., 1.]);

julia> I2 = Interval([0.5, 2.]);

julia> plot([I1, I2]);

Line{N<:Real, VN<:AbstractVector{N}} <: LazySet{N}

Type that represents a line in 2D of the form $a⋅x = b$ (i.e., a special case of a Hyperplane).

Fields

• a – normal direction
• b – constraint

Examples

The line $y = -x + 1$:

julia> Line([1., 1.], 1.)
Line{Float64,Array{Float64,1}}([1.0, 1.0], 1.0)

dim(L::Line)::Int

Return the ambient dimension of a line.

Input

• L – line

Output

The ambient dimension of the line, which is 2.

σ(d::AbstractVector{N}, L::Line{N}) where {N<:Real}

Return the support vector of a line in a given direction.

Input

• d – direction
• L – line

Output

The support vector in the given direction, which is defined the same way as for the more general Hyperplane.

∈(x::AbstractVector{N}, L::Line{N})::Bool where {N<:Real}

Check whether a given point is contained in a line.

Input

• x – point/vector
• L – line

Output

true iff x ∈ L.

Algorithm

The point $x$ belongs to the line if and only if $a⋅x = b$ holds.

an_element(L::Line{N})::Vector{N} where {N<:Real}

Return some element of a line.

Input

• L – line

Output

An element on the line.

Algorithm

If the $b$ value of the line is zero, the result is the origin. Otherwise the result is some $x = [x1, x2]$ such that $a·[x1, x2] = b$. We first find out in which dimension $a$ is nonzero, say, dimension 1, and then choose $x1 = 1$ and accordingly $x2 = \frac{b - a1}{a2}$.

rand(::Type{Line}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Line{N}

Create a random line.

Input

• Line – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random line.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1. Additionally, the constraint a is nonzero.

isbounded(L::Line)::Bool

Determine whether a line is bounded.

Input

• L – line

Output

false.

isempty(L::Line)::Bool

Return if a line is empty or not.

Input

• L – line

Output

false.

constrained_dimensions(L::Line{N})::Vector{Int} where {N<:Real}

Return the indices in which a line is constrained.

Input

• L – line

Output

A vector of ascending indices i such that the line is constrained in dimension i.

Examples

A line with constraint $x1 = 0$ is constrained in dimension 1 only.

LineSegment{N<:Real} <: AbstractCentrallySymmetricPolytope{N}

Type that represents a line segment in 2D between two points $p$ and $q$.

Fields

• p – first point
• q – second point

Examples

A line segment along the $x = y$ diagonal:

julia> s = LineSegment([0., 0], [1., 1.])
LineSegment{Float64}([0.0, 0.0], [1.0, 1.0])
julia> dim(s)
2

Use plot(s) to plot the extreme points of s and the line segment joining them. Membership test is computed with ∈ (in):

julia> [0., 0] ∈ s && [.25, .25] ∈ s && [1., 1] ∈ s && !([.5, .25] ∈ s)
true

We can check the intersection with another line segment, and optionally compute a witness (which is just the common point in this case):

julia> sn = LineSegment([1., 0], [0., 1.])
LineSegment{Float64}([1.0, 0.0], [0.0, 1.0])
julia> isempty(s ∩ sn)
false
julia> is_intersection_empty(s, sn, true)
(false, [0.5, 0.5])

dim(L::LineSegment)::Int

Return the ambient dimension of a line segment.

Input

• L – line segment

Output

The ambient dimension of the line segment, which is 2.

σ(d::AbstractVector{N}, L::LineSegment{N}) where {N<:Real}

Return the support vector of a line segment in a given direction.

Input

• d – direction
• L – line segment

Output

The support vector in the given direction.

Algorithm

If the angle between the vector $q - p$ and $d$ is bigger than 90° and less than 270° (measured in counter-clockwise order), the result is $p$, otherwise it is $q$. If the angle is exactly 90° or 270°, or if the direction has norm zero, this implementation returns $q$.

∈(x::AbstractVector{N}, L::LineSegment{N})::Bool where {N<:Real}

Check whether a given point is contained in a line segment.

Input

• x – point/vector
• L – line segment

Output

true iff $x ∈ L$.

Algorithm

Let $L = (p, q)$ be the line segment with extremes $p$ and $q$, and let $x$ be the given point.

1. A necessary condition for $x ∈ (p, q)$ is that the three points are aligned, thus their cross product should be zero.
2. It remains to check that $x$ belongs to the box approximation of $L$. This amounts to comparing each coordinate with those of the extremes $p$ and $q$.

Notes

The algorithm is inspired from here.

center(L::LineSegment{N})::Vector{N} where {N<:Real}

Return the center of a line segment.

Input

• L – line segment

Output

The center of the line segment.

an_element(L::LineSegment{N}) where {N<:Real}

Return some element of a line segment.

Input

• L – line segment

Output

The first vertex of the line segment.

rand(::Type{LineSegment}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::LineSegment{N}

Create a random line segment.

Input

• LineSegment – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random line segment.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1.

halfspace_left(L::LineSegment)

Return a half-space describing the 'left' of a two-dimensional line segment through two points.

Input

• L – line segment

Output

The half-space whose boundary goes through the two points p and q and which describes the left-hand side of the directed line segment pq.

halfspace_right(L::LineSegment)

Return a half-space describing the 'right' of a two-dimensional line segment through two points.

Input

• L – line segment

Output

The half-space whose boundary goes through the two points p and q and which describes the right-hand side of the directed line segment pq.

vertices_list(L::LineSegment{N}
             )::Vector{<:AbstractVector{N}} where {N<:Real}

Return the list of vertices of a line segment.

Input

• L – line segment

Output

The list of end points of the line segment.

constraints_list(L::LineSegment{N})::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a line segment in 2D.

Input

• L – line segment

Output

A vector of constraints that define the line segment.

Algorithm

$L$ is defined by 4 constraints. In this algorithm, the first two constraints are returned by $halfspace_right$ and $halfspace_left$, and the other two are obtained by considering the vector normal to the line segment that passes through each opposite vertex.

Notes

This function returns a vector of halfspaces. It does not return equality constraints.

plot_linesegment(L::LineSegment; ...)

Plot a line segment.

Input

• L – line segment

Examples

julia> using Plots, LazySets;

julia> L = LineSegment([0., 0.], [1., 1.]);

julia> plot(L);

plot_linesegments(Xk::Vector{S}; ...) where {S<:LineSegment}

Plot an array of line segments.

Input

• Xk – linear array of line segments

Examples

julia> using Plots, LazySets;

julia> L1 = LineSegment([0., 0.], [1., 1.]);

julia> L2 = LineSegment([1., 0.], [0., 1.]);

julia> plot([L1, L2]);

HPolygon{N<:Real} <: AbstractHPolygon{N}

Type that represents a convex polygon in constraint representation whose edges are sorted in counter-clockwise fashion with respect to their normal directions.

Fields

• constraints – list of linear constraints, sorted by the angle

Notes

The default constructor assumes that the given list of edges is sorted. It does not perform any sorting. Use addconstraint! to iteratively add the edges in a sorted way.

• HPolygon(constraints::Vector{LinearConstraint{<:Real}}) – default constructor
• HPolygon() – constructor with no constraints
• HPolygon(S::LazySet) – constructor from another set

σ(d::AbstractVector{N}, P::HPolygon{N};
  [linear_search]::Bool=(length(P.constraints) < BINARY_SEARCH_THRESHOLD)
 ) where {N<:Real}

Return the support vector of a polygon in a given direction.

Input

• d – direction
• P – polygon in constraint representation
• linear_search – (optional, default: see below) flag for controlling whether to perform a linear search or a binary search

Output

The support vector in the given direction. The result is always one of the vertices; in particular, if the direction has norm zero, any vertex is returned.

Algorithm

Comparison of directions is performed using polar angles; see the overload of <= for two-dimensional vectors.

For polygons with BINARY_SEARCH_THRESHOLD = 10 or more constraints we use a binary search by default.

HPolygonOpt{N<:Real} <: AbstractHPolygon{N}

Type that represents a convex polygon in constraint representation whose edges are sorted in counter-clockwise fashion with respect to their normal directions. This is a refined version of HPolygon.

Fields

• constraints – list of linear constraints
• ind – index in the list of constraints to begin the search to evaluate the support function

Notes

This structure is optimized to evaluate the support function/vector with a large sequence of directions that are close to each other. The strategy is to have an index that can be used to warm-start the search for optimal values in the support vector computation.

The default constructor assumes that the given list of edges is sorted. It does not perform any sorting. Use addconstraint! to iteratively add the edges in a sorted way.

• HPolygonOpt(constraints::Vector{LinearConstraint{<:Real}}, [ind]::Int) – default constructor with optional index
• HPolygonOpt(S::LazySet) – constructor from another set

σ(d::AbstractVector{N}, P::HPolygonOpt{N};
  [linear_search]::Bool=(length(P.constraints) < BINARY_SEARCH_THRESHOLD)
 ) where {N<:Real}

Return the support vector of an optimized polygon in a given direction.

Input

• d – direction
• P – optimized polygon in constraint representation
• linear_search – (optional, default: see below) flag for controlling whether to perform a linear search or a binary search

Output

The support vector in the given direction. The result is always one of the vertices; in particular, if the direction has norm zero, any vertex is returned.

Algorithm

Comparison of directions is performed using polar angles; see the overload of <= for two-dimensional vectors.

For polygons with BINARY_SEARCH_THRESHOLD = 10 or more constraints we use a binary search by default.

VPolygon{N<:Real} <: AbstractPolygon{N}

Type that represents a polygon by its vertices.

Fields

• vertices – the list of vertices

Notes

The constructor of VPolygon runs a convex hull algorithm on its vertices by default, to remove the possibly redundant vertices. The vertices are sorted in counter-clockwise fashion. Use the flag apply_convex_hull=false to skip the computation of the convex hull.

• VPolygon(vertices::Vector{Vector{N}}; apply_convex_hull::Bool=true, algorithm::String="monotone_chain")

σ(d::AbstractVector{N}, P::VPolygon{N}) where {N<:Real}

Return the support vector of a polygon in a given direction.

Input

• d – direction
• P – polygon in vertex representation

Output

The support vector in the given direction. If the direction has norm zero, the first vertex is returned.

Algorithm

This implementation performs a brute-force search, comparing the projection of each vector along the given direction. It runs in $O(n)$ where $n$ is the number of vertices.

Notes

For arbitrary points without structure this is the best one can do. However, a more efficient approach can be used if the vertices of the polygon have been sorted in counter-clockwise fashion. In that case a binary search algorithm can be used that runs in $O(\log n)$. See issue #40.

∈(x::AbstractVector{N}, P::VPolygon{N})::Bool where {N<:Real}

Check whether a given point is contained in a polygon in vertex representation.

Input

• x – point/vector
• P – polygon in vertex representation

Output

true iff $x ∈ P$.

Algorithm

This implementation exploits that the polygon's vertices are sorted in counter-clockwise fashion. Under this assumption we can just check if the vertex lies on the left of each edge, using the dot product.

Examples

julia> P = VPolygon([[2.0, 3.0], [3.0, 1.0], [5.0, 1.0], [4.0, 5.0]];
                    apply_convex_hull=false);

julia> ∈([4.5, 3.1], P)
false
julia> ∈([4.5, 3.0], P)
true
julia> ∈([4.4, 3.4], P)  #  point lies on the edge -> floating point error
false
julia> P = VPolygon([[2//1, 3//1], [3//1, 1//1], [5//1, 1//1], [4//1, 5//1]];
                     apply_convex_hull=false);

julia> ∈([44//10, 34//10], P)  #  with rational numbers the answer is correct
true

an_element(P::VPolygon{N})::Vector{N} where {N<:Real}

Return some element of a polygon in vertex representation.

Input

• P – polygon in vertex representation

Output

The first vertex of the polygon in vertex representation.

rand(::Type{VPolygon}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::VPolygon{N}

Create a random polygon in vertex representation.

Input

• VPolygon – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding
• num_vertices – (optional, default: -1) number of vertices of the polygon (see comment below)

Output

A random polygon in vertex representation.

Algorithm

We follow the idea here based on P. Valtr. Probability that n random points are in convex position. There is also a nice video available here.

The number of vertices can be controlled with the argument num_vertices. For a negative value we choose a random number in the range 3:10.

vertices_list(P::VPolygon{N})::Vector{Vector{N}} where {N<:Real}

Return the list of vertices of a convex polygon in vertex representation.

Input

• P – a polygon vertex representation

Output

List of vertices.

tohrep(P::VPolygon{N}, ::Type{HPOLYGON}=HPolygon
      )::HPOLYGON{N} where {N<:Real, HPOLYGON<:AbstractHPolygon}

Build a constraint representation of the given polygon.

Input

• P – polygon in vertex representation
• HPOLYGON – (optional, default: HPolygon) type of target polygon

Output

The same polygon but in constraint representation, an AbstractHPolygon.

Algorithm

The algorithms consists of adding an edge for each consecutive pair of vertices. Since the vertices are already ordered in counter-clockwise fashion (CWW), the constraints will be sorted automatically (CCW) if we start with the first edge between the first and second vertex.

tovrep(P::VPolygon{N})::VPolygon{N} where {N<:Real}

Build a vertex representation of the given polygon.

Input

• P – polygon in vertex representation

Output

The identity, i.e., the same polygon instance.

constraints_list(P::VPolygon{N})::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a polygon in V-representation.

Input

• P – polygon in V-representation

Output

The list of constraints of the polygon.

Algorithm

First the H-representation of $P$ is computed, then its list of constraints is returned.

remove_redundant_vertices(P::VPolygon{N};
                          [algorithm]::String="monotone_chain")::VPolygon{N} where {N<:Real}

Return the polygon obtained by removing the redundant vertices of the given polygon.

Input

• P – polygon in vertex representation
• algorithm – (optional, default: "monotone_chain") the algorithm used to compute the convex hull

Output

A new polygon such that its vertices are the convex hull of the given polygon.

Algorithm

A convex hull algorithm is used to compute the convex hull of the vertices of the given input polygon P; see ?convex_hull for details on the available algorithms. The vertices of the output polygon are sorted in counter-clockwise fashion.

remove_redundant_vertices!(P::VPolygon{N};
                           [algorithm]::String="monotone_chain")::VPolygon{N} where {N<:Real}

Remove the redundant vertices of the given polygon.

Input

• P – polygon in vertex representation
• algorithm – (optional, default: "monotone_chain") the algorithm used to compute the convex hull

Output

A new polygon such that its vertices are the convex hull of the given polygon.

Algorithm

A convex hull algorithm is used to compute the convex hull of the vertices of the given input polygon P; see ?convex_hull for details on the available algorithms. The vertices of the output polygon are sorted in counter-clockwise fashion.

jump2pi(x::N)::N where {N<:AbstractFloat}

Return $x + 2π$ if $x$ is negative, otherwise return $x$.

Input

• x – real scalar

Output

$x + 2π$ if $x$ is negative, $x$ otherwise.

Examples

julia> import LazySets.jump2pi

julia> jump2pi(0.0)
0.0

julia> jump2pi(-0.5)
5.783185307179586

julia> jump2pi(0.5)
0.5

<=(u::AbstractVector{N}, v::AbstractVector{N})::Bool where {N<:AbstractFloat}

Compares two 2D vectors by their direction.

Input

• u – first 2D direction
• v – second 2D direction

Output

True iff $\arg(u) [2π] ≤ \arg(v) [2π]$

Notes

The argument is measured in counter-clockwise fashion, with the 0 being the direction (1, 0).

Algorithm

The implementation uses the arctangent function with sign, atan, which for two arguments implements the atan2 function.

<=(u::AbstractVector{N}, v::AbstractVector{N})::Bool where {N<:Real}

Compares two 2D vectors by their direction.

Input

• u – first 2D direction
• v – second 2D direction

Output

True iff $\arg(u) [2π] ≤ \arg(v) [2π]$

Notes

The argument is measured in counter-clockwise fashion, with the 0 being the direction (1, 0).

Algorithm

The implementation checks the quadrant of each direction, and compares directions using the right-hand rule. In particular, it doesn't use the arctangent.

quadrant(w::AbstractVector{N})::Int where {N<:Real}

Compute the quadrant where the direction w belongs.

Input

• w – direction

Output

An integer from 0 to 3, with the following convention:

     ^
   1 | 0
  ---+-->
   2 | 3

Algorithm

The idea is to encode the following logic function: $11 ↦ 0, 01 ↦ 1, 00 ↦ 2, 10 ↦ 3$, according to the convention of above.

This function is inspired from AGPX's answer in: Sort points in clockwise order?

HPolytope{N<:Real} <: AbstractPolytope{N}

Type that represents a convex polytope in H-representation.

Fields

• constraints – vector of linear constraints

Note

Recall that a polytope is a bounded polyhedron. Boundedness is a running assumption in this type.

HPolyhedron{N<:Real} <: LazySet{N}

Type that represents a convex polyhedron in H-representation.

Fields

• constraints – vector of linear constraints

dim(P::HPoly{N})::Int where {N<:Real}

Return the dimension of a polyhedron in H-representation.

Input

• P – polyhedron in H-representation

Output

The ambient dimension of the polyhedron in H-representation. If it has no constraints, the result is $-1$.

ρ(d::AbstractVector{N}, P::HPoly{N})::N where {N<:Real}

Evaluate the support function of a polyhedron (in H-representation) in a given direction.

Input

• d – direction
• P – polyhedron in H-representation

Output

The support function of the polyhedron. If a polytope is unbounded in the given direction, we throw an error. If a polyhedron is unbounded in the given direction, the result is Inf.

Algorithm

This implementation uses GLPKSolverLP as linear programming backend.

σ(d::AbstractVector{N}, P::HPoly{N}) where {N<:Real}

Return the support vector of a polyhedron (in H-representation) in a given direction.

Input

• d – direction
• P – polyhedron in H-representation

Output

The support vector in the given direction.

Algorithm

This implementation uses GLPKSolverLP as linear programming backend.

∈(x::AbstractVector{N}, P::HPoly{N})::Bool where {N<:Real}

Check whether a given point is contained in a polyhedron in constraint representation.

Input

• x – vector with the coordinates of the point
• P – polyhedron in constraint representation

Output

true iff $x ∈ P$.

Algorithm

This implementation checks if the point lies on the outside of each hyperplane. This is equivalent to checking if the point lies in each half-space.

addconstraint!(P::HPoly{N},
               constraint::LinearConstraint{N})::Nothing where {N<:Real}

Add a linear constraint to a polyhedron in H-representation.

Input

• P – polyhedron in H-representation
• constraint – linear constraint to add

Output

Nothing.

Notes

It is left to the user to guarantee that the dimension of all linear constraints is the same.

constraints_list(P::HPoly{N})::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a polyhedron in H-representation.

Input

• P – polyhedron in H-representation

Output

The list of constraints of the polyhedron.

tosimplehrep(P::HPoly{N}) where {N}

Return the simple H-representation $Ax ≤ b$ of a polyhedron.

Input

• P – polyhedron

Output

The tuple (A, b) where A is the matrix of normal directions and b are the offsets.

tohrep(P::HPoly{N}) where {N<:Real}

Return a constraint representation of the given polyhedron in constraint representation (no-op).

Input

• P – polyhedron in constraint representation

Output

The same polyhedron instance.

isempty(P::HPoly{N}; [solver]=GLPKSolverLP())::Bool where {N<:Real}

Determine whether a polyhedron is empty.

Input

• P – polyhedron
• backend – (optional, default: default_polyhedra_backend(P, N)) the polyhedral computations backend
• solver – (optional, default: GLPKSolverLP()) LP solver backend

Output

true if and only if the constraints are inconsistent.

Algorithm

This function uses Polyhedra.isempty which evaluates the feasibility of the LP whose feasible set is determined by the set of constraints and whose objective function is zero.

Notes

This implementation uses GLPKSolverLP as linear programming backend by default.

cartesian_product(P1::HPoly{N}, P2::HPoly{N};
                  [backend]=default_polyhedra_backend(P1, N)
                 ) where {N<:Real}

Compute the Cartesian product of two polyhedra in H-representaion.

Input

• P1 – polyhedron
• P2 – another polyhedron
• backend – (optional, default: default_polyhedra_backend(P1, N)) the polyhedral computations backend

Output

The polyhedron obtained by the concrete cartesian product of P1 and P2.

Notes

For further information on the supported backends see Polyhedra's documentation.

tovrep(P::HPoly{N};
      [backend]=default_polyhedra_backend(P, N)) where {N<:Real}

Transform a polyhedron in H-representation to a polytope in V-representation.

Input

• P – polyhedron in constraint representation
• backend – (optional, default: default_polyhedra_backend(P, N)) the backend for polyhedral computations

Output

The VPolytope which is the vertex representation of the given polyhedron in constraint representation.

Notes

The conversion may not preserve the numeric type (e.g., with N == Float32) depending on the backend. For further information on the supported backends see Polyhedra's documentation.

polyhedron(P::HPoly{N};
           [backend]=default_polyhedra_backend(P, N)) where {N<:Real}

Return an HRep polyhedron from Polyhedra.jl given a polytope in H-representation.

Input

• P – polytope
• backend – (optional, default: call default_polyhedra_backend(P, N)) the polyhedral computations backend

Output

An HRep polyhedron.

Notes

For further information on the supported backends see Polyhedra's documentation.

remove_redundant_constraints(P::PT;
                             backend=GLPKSolverLP()) where {N, PT<:HPoly{N}}

Given a polyhedron in H-representation, return a new polyhedron with no reundant constraints.

Input

• P – polyhedron
• backend – (optional, default: GLPKSolverLP) the numeric LP solver backend

Output

A new polyhedron obtained by removing the redundant constraints in P.

Algorithm

See remove_redundant_constraints!.

remove_redundant_constraints!(P::PT;
                              backend=GLPKSolverLP()) where {N, PT<:HPoly{N}}

Remove the redundant constraints in a polyhedron in H-representation; the polyhedron is updated inplace.

Input

• P – polyhedron
• backend – (optional, default: GLPKSolverLP) the numeric LP solver backend

Output

The polyhedron obtained by removing the redundant constraints in P.

Algorithm

If the polyhedron P has m constraints and its dimension is n, this function checks one by one if each of the m constraints is implied by the remaining ones. To check if the k-th constraint is redundant, an LP is formulated.

For details, see Fukuda's Polyhedra FAQ.

rand(::Type{HPolytope}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::HPolytope{N}

Create a random polytope in constraint representation.

Input

• HPolytope – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding
• num_vertices – (optional, default: -1) upper bound on the number of vertices of the polytope (see comment below)

Output

A random polytope in constraint representation.

Algorithm

We create a random polytope in vertex representation and convert it to constraint representation (hence the argument num_vertices). See rand(::Type{VPolytope}).

vertices_list(P::HPolytope{N};
              [backend]=default_polyhedra_backend(P, N),
              [prunefunc]=removevredundancy!)::Vector{Vector{N}} where
              {N<:Real}

Return the list of vertices of a polytope in constraint representation.

Input

• P – polytope in constraint representation
• backend – (optional, default: default_polyhedra_backend(P, N)) the polyhedral computations backend
• prunefunc – (optional, default: removevredundancy!) function to post-process the output of vreps

Output

List of vertices.

Notes

For further information on the supported backends see Polyhedra's documentation.

rand(::Type{HPolyhedron}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::HPolyhedron{N}

Create a polyhedron.

Input

• HPolyhedron – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension (is ignored)
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A polyhedron.

Algorithm

We first create a random polytope and then randomly remove some of the constraints.

isbounded(P::HPolyhedron)::Bool

Determine whether a polyhedron in constraint representation is bounded.

Input

• P – polyhedron in constraint representation

Output

true iff the polyhedron is bounded.

Algorithm

We first check if the polyhedron has more than max(dim(P), 1) constraints, which is a necessary condition for boundedness. If so, we check boundedness via isbounded_unit_dimensions.

vertices_list(P::HPolyhedron{N}) where {N<:Real}

Return the list of vertices of a polyhedron in constraint representation.

Input

• P – polyhedron in constraint representation

Output

This function returns an error because the polyhedron is possibly unbounded. If P is known to be bounded, try converting to HPolytope first:

julia> P = HPolyhedron([HalfSpace([1.0, 0.0], 1.0),
                        HalfSpace([0.0, 1.0], 1.0),
                        HalfSpace([-1.0, 0.0], 1.0),
                        HalfSpace([0.0, -1.0], 1.0)]);

julia> P_as_polytope = convert(HPolytope, P);

singleton_list(P::HPolyhedron{N})::Vector{Singleton{N}} where {N<:Real}

Return the vertices of a polyhedron in H-representation as a list of singletons.

Input

• P – polytope in constraint representation

Output

This function returns an error because the polyhedron is possibly unbounded. If P is known to be bounded, try converting to HPolytope first.

constrained_dimensions(P::HPolyhedron{N})::Vector{Int} where {N<:Real}

Return the indices in which a polyhedron in constraint representation is constrained.

Input

• P – polyhedron in constraint representation

Output

A vector of ascending indices i such that the polyhedron is constrained in dimension i.

Examples

A 2D polyhedron with constraint $x1 ≥ 0$ is constrained in dimension 1 only.

VPolytope{N<:Real} <: AbstractPolytope{N}

Type that represents a convex polytope in V-representation.

Fields

• vertices – the list of vertices

dim(P::VPolytope)::Int

Return the dimension of a polytope in V-representation.

Input

• P – polytope in V-representation

Output

The ambient dimension of the polytope in V-representation. If it is empty, the result is $-1$.

Examples

julia> v = VPolytope();

julia> dim(v) > 0
false

julia> v = VPolytope([ones(3)])
VPolytope{Float64}(Array{Float64,1}[[1.0, 1.0, 1.0]])

julia> dim(v) == 3
true

σ(d::AbstractVector{N}, P::VPolytope{N}; algorithm="hrep") where {N<:Real}

Return the support vector of a polyhedron (in V-representation) in a given direction.

Input

• d – direction
• P – polyhedron in V-representation
• algorithm – (optional, default: 'hrep') method to compute the support vector

Output

The support vector in the given direction.

rand(::Type{VPolytope}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::VPolytope{N}

Create a random polytope in vertex representation.

Input

• VPolytope – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding
• num_vertices – (optional, default: -1) upper bound on the number of vertices of the polytope (see comment below)

Output

A random polytope in vertex representation.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1.

The number of vertices can be controlled with the argument num_vertices. For a negative value we choose a random number in the range dim:5*dim (except if dim == 1, in which case we choose in the range 1:2). Note that we do not guarantee that the vertices are not redundant.

vertices_list(P::VPolytope{N})::Vector{Vector{N}} where {N<:Real}

Return the list of vertices of a polytope in V-representation.

Input

• P – polytope in vertex representation

Output

List of vertices.

remove_redundant_vertices(P::VPolytope{N};
                          [backend]=default_polyhedra_backend(P, N))::VPolytope{N} where {N<:Real}

Return the polytope obtained by removing the redundant vertices of the given polytope.

Input

• P – polytope in vertex representation
• backend – (optional, default: default_polyhedra_backend(P1, N)) the polyhedral computations backend, see Polyhedra's documentation for further information

Output

A new polytope such that its vertices are the convex hull of the given polytope.

constraints_list(P::VPolytope{N})::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a polytope in V-representation.

Input

• P – polytope in V-representation

Output

The list of constraints of the polytope.

Algorithm

First the H-representation of $P$ is computed, then its list of constraints is returned.

tohrep(P::VPolytope{N};
       [backend]=default_polyhedra_backend(P, N)) where {N<:Real}

Transform a polytope in V-representation to a polytope in H-representation.

Input

• P – polytope in vertex representation
• backend – (optional, default: default_polyhedra_backend(P, N)) the backend for polyhedral computations

Output

The HPolytope which is the constraint representation of the given polytope in vertex representation.

Notes

The conversion may not preserve the numeric type (e.g., with N == Float32) depending on the backend. For further information on the supported backends see Polyhedra's documentation.

tovrep(P::VPolytope)

Return a vertex representation of the given polytope in vertex representation (no-op).

Input

• P – polytope in vertex representation

Output

The same polytope instance.

cartesian_product(P1::VPolytope{N}, P2::VPolytope{N};
                  [backend]=default_polyhedra_backend(P1, N)) where {N}

Compute the Cartesian product of two polytopes in V-representation.

Input

• P1 – polytope
• P2 – another polytope
• backend – (optional, default: default_polyhedra_backend(P1, N)) the polyhedral computations backend, see Polyhedra's documentation for further information

Output

The VPolytope obtained by the concrete Cartesian product of P1 and P2.

polyhedron(P::VPolytope{N};
           [backend]=default_polyhedra_backend(P, N)) where {N<:Real}

Return an VRep polyhedron from Polyhedra.jl given a polytope in V-representation.

Input

• P – polytope
• backend – (optional, default: default_polyhedra_backend(P, N)) the polyhedral computations backend, see Polyhedra's documentation for further information

Output

A VRep polyhedron.

Singleton{N<:Real, VN<:AbstractVector{N}} <: AbstractSingleton{N}

Type that represents a singleton, that is, a set with a unique element.

Fields

• element – the only element of the set

rand(::Type{Singleton}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Singleton{N}

Create a random singleton.

Input

• Singleton – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

A random singleton.

Algorithm

The element is a normally distributed vector with entries of mean 0 and standard deviation 1.

element(S::Singleton{N}) where {N<:Real}

Return the element of a singleton.

Input

• S – singleton

Output

The element of the singleton.

element(S::Singleton{N}, i::Int)::N where {N<:Real}

Return the i-th entry of the element of a singleton.

Input

• S – singleton
• i – dimension

Output

The i-th entry of the element of the singleton.

ZeroSet{N<:Real} <: AbstractSingleton{N}

Type that represents the zero set, i.e., the set that only contains the origin.

Fields

• dim – the ambient dimension of this zero set

dim(Z::ZeroSet)::Int

Return the ambient dimension of this zero set.

Input

• Z – a zero set, i.e., a set that only contains the origin

Output

The ambient dimension of the zero set.

σ(d::AbstractVector{N}, Z::ZeroSet{N}) where {N<:Real}

Return the support vector of a zero set.

Input

• Z – a zero set, i.e., a set that only contains the origin

Output

The returned value is the origin since it is the only point that belongs to this set.

∈(x::AbstractVector{N}, Z::ZeroSet{N})::Bool where {N<:Real}

Check whether a given point is contained in a zero set.

Input

• x – point/vector
• Z – zero set

Output

true iff $x ∈ Z$.

Examples

julia> Z = ZeroSet(2);

julia> ∈([1.0, 0.0], Z)
false
julia> ∈([0.0, 0.0], Z)
true

rand(::Type{ZeroSet}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::ZeroSet{N}

Create a zero set (note that there is nothing to randomize).

Input

• ZeroSet – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding

Output

The (only) zero set of the given numeric type and dimension.

element(S::ZeroSet{N})::Vector{N} where {N<:Real}

Return the element of a zero set.

Input

• S – zero set

Output

The element of the zero set, i.e., a zero vector.

element(S::ZeroSet{N}, ::Int)::N where {N<:Real}

Return the i-th entry of the element of a zero set.

Input

• S – zero set
• i – dimension

Output

The i-th entry of the element of the zero set, i.e., 0.

linear_map(M::AbstractMatrix{N}, Z::ZeroSet{N}) where {N<:Real}

Concrete linear map of a zero set.

Input

• M – matrix
• Z – zero set

Output

The zero set whose dimension matches the output dimension of the given matrix.

Zonotope{N<:Real} <: AbstractCentrallySymmetricPolytope{N}

Type that represents a zonotope.

Fields

• center – center of the zonotope
• generators – matrix; each column is a generator of the zonotope

Notes

Mathematically, a zonotope is defined as the set

where $c \in \mathbb{R}^n$ is its center and $\{g_i\}_{i=1}^p$, $g_i \in \mathbb{R}^n$, is the set of generators. This characterization defines a zonotope as the finite Minkowski sum of line segments. Zonotopes can be equivalently described as the image of a unit infinity-norm ball in $\mathbb{R}^n$ by an affine transformation.

• Zonotope(center::AbstractVector{N}, generators::AbstractMatrix{N}) where {N<:Real}

• Zonotope(center::AbstractVector{N}, generators_list::AbstractVector{VN} ) where {N<:Real, VN<:AbstractVector{N}}

Examples

A two-dimensional zonotope with given center and set of generators:

julia> Z = Zonotope([1.0, 0.0], [0.1 0.0; 0.0 0.1])
Zonotope{Float64}([1.0, 0.0], [0.1 0.0; 0.0 0.1])
julia> dim(Z)
2

Compute its vertices:

julia> vertices_list(Z)
4-element Array{Array{Float64,1},1}:
 [1.1, 0.1]
 [0.9, 0.1]
 [1.1, -0.1]
 [0.9, -0.1]

Evaluate the support vector in a given direction:

julia> σ([1., 1.], Z)
2-element Array{Float64,1}:
 1.1
 0.1

Alternative constructor: A zonotope in two dimensions with three generators:

julia> Z = Zonotope(ones(2), [[1., 0.], [0., 1.], [1., 1.]])
Zonotope{Float64}([1.0, 1.0], [1.0 0.0 1.0; 0.0 1.0 1.0])
julia> Z.generators
2×3 Array{Float64,2}:
 1.0  0.0  1.0
 0.0  1.0  1.0

σ(d::AbstractVector{N}, Z::Zonotope{N}) where {N<:Real}

Return the support vector of a zonotope in a given direction.

Input

• d – direction
• Z – zonotope

Output

Support vector in the given direction. If the direction has norm zero, the vertex with $ξ_i = 1 \ \ ∀ i = 1,…, p$ is returned.

∈(x::AbstractVector{N}, Z::Zonotope{N};
  solver=GLPKSolverLP(method=:Simplex))::Bool where {N<:Real}

Check whether a given point is contained in a zonotope.

Input

• x – point/vector
• Z – zonotope
• solver – (optional, default: GLPKSolverLP(method=:Simplex)) the backend used to solve the linear program

Output

true iff $x ∈ Z$.

Examples

julia> Z = Zonotope([1.0, 0.0], [0.1 0.0; 0.0 0.1]);

julia> ∈([1.0, 0.2], Z)
false
julia> ∈([1.0, 0.1], Z)
true

Algorithm

The membership problem is computed by stating and solving the following linear program with the simplex method. Let $p$ and $n$ be the number of generators and ambient dimension, respectively. We consider the minimization of $x_0$ in the $p+1$-dimensional space of elements $(x_0, ξ_1, …, ξ_p)$ constrained to $0 ≤ x_0 ≤ ∞$, $ξ_i ∈ [-1, 1]$ for all $i = 1, …, p$, and such that $x-c = Gξ$ holds. If a feasible solution exists, the optimal value $x_0 = 0$ is achieved.

Notes

This function is parametric in the number type N. For exact arithmetic use an appropriate backend, e.g. solver=GLPKSolverLP(method=:Exact).

rand(::Type{Zonotope}; [N]::Type{<:Real}=Float64, [dim]::Int=2,
     [rng]::AbstractRNG=GLOBAL_RNG, [seed]::Union{Int, Nothing}=nothing
    )::Zonotope{N}

Create a random zonotope.

Input

• Zonotope – type for dispatch
• N – (optional, default: Float64) numeric type
• dim – (optional, default: 2) dimension
• rng – (optional, default: GLOBAL_RNG) random number generator
• seed – (optional, default: nothing) seed for reseeding
• num_generators – (optional, default: -1) number of generators of the zonotope (see comment below)

Output

A random zonotope.

Algorithm

All numbers are normally distributed with mean 0 and standard deviation 1.

The number of generators can be controlled with the argument num_generators. For a negative value we choose a random number in the range dim:2*dim (except if dim == 1, in which case we only create a single generator).

vertices_list(Z::Zonotope{N})::Vector{Vector{N}} where {N<:Real}

Return the vertices of a zonotope.

Input

• Z – zonotope

Output

List of vertices as a vector of vectors.

Algorithm

If the zonotope has $p$ generators, each of the $2^p$ vertices is computed by taking the sum of the center and a linear combination of generators, where the combination factors are $ξ_i ∈ \{-1, 1\}$.

Notes

For high dimensions, it would be preferable to develop a vertex_iterator approach.

constraints_list(P::Zonotope{N}
                )::Vector{LinearConstraint{N}} where {N<:Real}

Return the list of constraints defining a zonotope.

Input

• Z – zonotope

Output

The list of constraints of the zonotope.

Algorithm

This is the (inefficient) fallback implementation for rational numbers. It first computes the vertices and then converts the corresponding polytope to constraint representation.

constraints_list(P::Zonotope{N}
                )::Vector{LinearConstraint{N}} where {N<:AbstractFloat}

Return the list of constraints defining a zonotope.

Input

• Z – zonotope

Output

The list of constraints of the zonotope.

Notes

The algorithm assumes that no generator is redundant. The result has $2 \binom{p}{n-1}$ (with $p$ being the number of generators and $n$ being the ambient dimension) constraints, which is optimal under this assumption.

Algorithm

We follow the algorithm presented in Althoff, Stursberg, Buss: Computing Reachable Sets of Hybrid Systems Using a Combination of Zonotopes and Polytopes. 2009.

The one-dimensional case is not covered by that algorithm; we manually handle this case, assuming that there is only one generator.

center(Z::Zonotope{N})::Vector{N} where {N<:Real}

Return the center of a zonotope.

Input

• Z – zonotope

Output

The center of the zonotope.

order(Z::Zonotope)::Rational

Return the order of a zonotope.

Input

• Z – zonotope

Output

A rational number representing the order of the zonotope.

Notes

The order of a zonotope is defined as the quotient of its number of generators and its dimension.

minkowski_sum(Z1::Zonotope{N}, Z2::Zonotope{N}) where {N<:Real}

Concrete Minkowski sum of a pair of zonotopes.

Input

• Z1 – one zonotope
• Z2 – another zonotope

Output

The zonotope obtained by summing the centers and concatenating the generators of $Z_1$ and $Z_2$.

linear_map(M::AbstractMatrix{N}, Z::Zonotope{N}) where {N<:Real}

Concrete linear map of a zonotope.

Input

• M – matrix
• Z – zonotope

Output

The zonotope obtained by applying the linear map to the center and generators of $Z$.

scale(α::Real, Z::Zonotope)

Concrete scaling of a zonotope.

Input

• α – scalar
• Z – zonotope

Output

The zonotope obtained by applying the numerical scale to the center and generators of $Z$.

ngens(Z::Zonotope)::Int

Return the number of generators of a zonotope.

Input

• Z – zonotope

Output

Integer representing the number of generators.

reduce_order(Z::Zonotope, r)::Zonotope

Reduce the order of a zonotope by overapproximating with a zonotope with less generators.

Input

• Z – zonotope
• r – desired order

Output

A new zonotope with less generators, if possible.

Algorithm

This function implements the algorithm described in A. Girard's Reachability of Uncertain Linear Systems Using Zonotopes, HSCC. Vol. 5. 2005.