Utility functions

sign_cadlag(x::N)::N where {N<:Real}

This function works like the sign function but is $1$ for input $0$.

Input

• x – real scalar

Output

$1$ if $x ≥ 0$, $-1$ otherwise.

Notes

This is the sign function right-continuous at zero (see càdlàg function). It can be used with vector-valued arguments via the dot operator.

Examples

julia> import LazySets.sign_cadlag

julia> sign_cadlag.([-0.6, 1.3, 0.0])
3-element Array{Float64,1}:
 -1.0
  1.0
  1.0

ispermutation(u::AbstractVector{T}, v::AbstractVector{T})::Bool where T

Check that two vectors contain the same elements up to reordering.

Input

• u – first vector
• v – second vector

Output

true iff the vectors are identical up to reordering.

Examples

julia> LazySets.ispermutation([1, 2, 2], [2, 2, 1])
true

julia> LazySets.ispermutation([1, 2, 2], [1, 1, 2])
false

@neutral(SET, NEUT)

Create functions to make a lazy set operation commutative with a given neutral element set type.

Input

• SET – lazy set operation type
• NEUT – set type for neutral element

Output

Nothing.

Notes

This macro generates four functions (possibly two more if @absorbing has been used in advance) (possibly two or four more if @declare_array_version has been used in advance).

Examples

@neutral(MinkowskiSum, N) creates at least the following functions:

• neutral(::MinkowskiSum) = N
• MinkowskiSum(X, N) = X
• MinkowskiSum(N, X) = X
• MinkowskiSum(N, N) = N

@absorbing(SET, ABS)

Create functions to make a lazy set operation commutative with a given absorbing element set type.

Input

• SET – lazy set operation type
• ABS – set type for absorbing element

Output

Nothing.

Notes

This macro generates four functions (possibly two more if @neutral has been used in advance) (possibly two or four more if @declare_array_version has been used in advance).

Examples

@absorbing(MinkowskiSum, A) creates at least the following functions:

• absorbing(::MinkowskiSum) = A
• MinkowskiSum(X, A) = A
• MinkowskiSum(A, X) = A
• MinkowskiSum(A, A) = A

@declare_array_version(SET, SETARR)

Create functions to connect a lazy set operation with its array set type.

Input

• SET – lazy set operation type
• SETARR – array set type

Output

Nothing.

Notes

This macro generates six functions (and possibly up to eight more if @neutral/@absorbing has been used in advance for the base and/or array set type).

Examples

@declare_array_version(MinkowskiSum, MinkowskiSumArray) creates at least the following functions:

• array_constructor(::MinkowskiSum) = MinkowskiSumArray
• is_array_constructor(::MinkowskiSumArray) = true
• MinkowskiSum!(X, Y)
• MinkowskiSum!(X, arr)
• MinkowskiSum!(arr, X)
• MinkowskiSum!(arr1, arr2)

@neutral_absorbing(SET, NEUT, ABS)

Create two functions to avoid method ambiguties for a lazy set operation with respect to neutral and absorbing element set types.

Input

• SET – lazy set operation type
• NEUT – set type for neutral element
• ABS – set type for absorbing element

Output

A quoted expression containing the function definitions.

Examples

@neutral_absorbing(MinkowskiSum, N, A) creates the following functions as quoted expressions:

• MinkowskiSum(N, A) = A
• MinkowskiSum(A, N) = A

@array_neutral(FUN, NEUT, SETARR)

Create two functions to avoid method ambiguities for a lazy set operation with respect to the neutral element set type and the array set type.

Input

• FUN – function name
• NEUT – set type for neutral element
• SETARR – array set type

Output

A quoted expression containing the function definitions.

Examples

@array_neutral(MinkowskiSum, N, ARR) creates the following functions as quoted expressions:

• MinkowskiSum(N, ARR) = ARR
• MinkowskiSum(ARR, N) = ARR

@array_absorbing(FUN, ABS, SETARR)

Create two functions to avoid method ambiguities for a lazy set operation with respect to the absorbing element set type and the array set type.

Input

• FUN – function name
• ABS – set type for absorbing element
• SETARR – array set type

Output

A quoted expression containing the function definitions.

Examples

@array_absorbing(MinkowskiSum, ABS, ARR) creates the following functions as quoted expressions:

• MinkowskiSum(ABS, ARR) = ABS
• MinkowskiSum(ARR, ABS) = ABS

cross_product(M::AbstractMatrix{N}) where {N<:Real}

Compute the high-dimensional cross product of $n-1$ $n$-dimensional vectors.

Input

• M – $n × n - 1$-dimensional matrix

Output

A vector.

Algorithm

The cross product is defined as follows:

where $M^{[i]}$ is defined as $M$ with the $i$-th row removed. See Althoff, Stursberg, Buss: Computing Reachable Sets of Hybrid Systems Using a Combination of Zonotopes and Polytopes. 2009.

get_radius!(sih::SymmetricIntervalHull{N},
            i::Int,
            n::Int=dim(sih))::N where {N<:Real}

Compute the radius of a symmetric interval hull of a convex set in a given dimension.

Input

• sih – symmetric interval hull of a convex set
• i – dimension in which the radius should be computed
• n – (optional, default: dim(sih)) set dimension

Output

The radius of a symmetric interval hull of a convex set in a given dimension.

Algorithm

We ask for the support vector of the underlying set for both the positive and negative unit vector in the dimension i.

an_element_helper(hp::Hyperplane{N},
                  [nonzero_entry_a]::Int)::Vector{N} where {N<:Real}

Helper function that computes an element on a hyperplane's hyperplane.

Input

• hp – hyperplane
• nonzero_entry_a – (optional, default: computes the first index) index i such that hp.a[i] is different from 0

Output

An element on a hyperplane.

Algorithm

We compute the point on the hyperplane as follows:

• We already found a nonzero entry of $a$ in dimension, say, $i$.
• We set $x[i] = b / a[i]$.
• We set $x[j] = 0$ for all $j ≠ i$.

    σ_helper(d::AbstractVector{N},
             hp::Hyperplane{N};
             error_unbounded::Bool=true,
             [halfspace]::Bool=false) where {N<:Real}

Return the support vector of a hyperplane.

Input

• d – direction
• hp – hyperplane
• error_unbounded – (optional, default: true) true if an error should be thrown whenever the set is unbounded in the given direction
• halfspace – (optional, default: false) true if the support vector should be computed for a half-space

Output

A pair (v, b) where v is a vector and b is a Boolean flag.

The flag b is false in one of the following cases:

1. The direction has norm zero.
2. The direction is the hyperplane's normal direction.
3. The direction is the opposite of the hyperplane's normal direction and

halfspace is false. In all these cases, v is any point on the hyperplane.

Otherwise, the flag b is true, the set is unbounded in the given direction, and v is any vector.

If error_unbounded is true and the set is unbounded in the given direction, this function throws an error instead of returning.

Notes

For correctness, consider the weak duality of LPs: If the primal is unbounded, then the dual is infeasible. Since there is only a single constraint, the feasible set of the dual problem is hp.a ⋅ y == d, y >= 0 (with objective function hp.b ⋅ y). It is easy to see that this problem is infeasible whenever a is not parallel to d.

binary_search_constraints(d::AbstractVector{N},
                          constraints::Vector{LinearConstraint{N}},
                          n::Int,
                          k::Int;
                          [choose_lower]::Bool=false
                         )::Int where {N<:Real}

Performs a binary search in the constraints.

Input

• d – direction
• constraints – constraints
• n – number of constraints
• k – start index
• choose_lower – (optional, default: false) flag for choosing the lower index (see the 'Output' section)

Output

In the default setting, the result is the smallest index k such that d <= constraints[k], or n+1 if no such k exists. If the choose_lower flag is set, the result is the largest index k such that constraints[k] < d, which is equivalent to being k-1 in the normal setting.

nonzero_indices(v::AbstractVector{N})::Vector{Int} where {N<:Real}

Return the indices in which a vector is non-zero.

Input

• v – vector

Output

A vector of ascending indices i such that the vector is non-zero in dimension i.

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

Check whether two vectors point in the same direction.

Input

• u – first vector
• v – second vector

Output

(true, k) iff the vectors are identical up to a positive scaling factor k, and (false, 0) otherwise.

Examples

julia> LazySets.samedir([1, 2, 3], [2, 4, 6])
(true, 0.5)

julia> LazySets.samedir([1, 2, 3], [3, 2, 1])
(false, 0)

julia> LazySets.samedir([1, 2, 3], [-1, -2, -3])
(false, 0)

_random_zero_sum_vector(rng::AbstractRNG, N::Type{<:Real}, n::Int)

Create a random vector with entries whose sum is zero.

Input

• rng – random number generator
• N – numeric type
• n – length of vector

Output

A random vector of random numbers such that all positive entries come first and all negative entries come last, and such that the total sum is zero.

Algorithm

This is a preprocessing step of the algorithm here based on P. Valtr. Probability that n random points are in convex position.

remove_duplicates_sorted!(v::AbstractVector)

Remove duplicate entries in a sorted vector.

Input

• v – sorted vector

Output

The input vector without duplicates.

reseed(rng::AbstractRNG, seed::Union{Int, Nothing})::AbstractRNG

Reset the RNG seed if the seed argument is a number.

Input

• rng – random number generator
• seed – seed for reseeding

Output

The input RNG if the seed is nothing, and a reseeded RNG otherwise.

CachedPair{N}

A mutable pair of an index and a vector.

Fields

• idx – index
• vec – vector

UnitVector{T} <: AbstractVector{T}

A lazy unit vector with arbitrary one-element.

Fields

• i – index of non-zero entry
• n – vector length
• v – non-zero entry

StrictlyIncreasingIndices

Iterator over the vectors of m strictly increasing indices from 1 to n.

Fields

• n – size of the index domain
• m – number of indices to choose (resp. length of the vectors)

Notes

The vectors are modified in-place.

The iterator ranges over $\binom{n}{m}$ (n choose m) possible vectors.

This implementation results in a lexicographic order with the last index growing first.

Examples

julia> for v in LazySets.StrictlyIncreasingIndices(4, 2)
           println(v)
       end
[1, 2]
[1, 3]
[1, 4]
[2, 3]
[2, 4]
[3, 4]