BenchmarkFunctions.ackley_1
— Methodackley_1(X)
ackley_1(X, n=3)
Compute the n-dimensional Ackley function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-32,32].
BenchmarkFunctions.ackley_2
— Methodackley_2(X)
Compute the 2-dimensional Ackley No. 2 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-32, 32] for i
= 1, 2.
BenchmarkFunctions.ackley_3
— Methodackley_3(X)
Compute the 2-dimensional Ackley No. 3 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-32, 32] for i
= 1, 2.
BenchmarkFunctions.ackley_4
— Methodackley_4(X)
ackley_4(X, n=3)
Compute the n-dimensional Ackley No. 4 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-35, 35].
BenchmarkFunctions.adjiman
— Methodadjiman(X)
Compute the 2-dimensional Adjiman function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-1, 1] for i
= 1, 2. Has specific constraints.
BenchmarkFunctions.alpine_1
— Methodalpine_1(X)
alpine_1(X, n=3)
Compute the n-dimensional Alpine No. 1 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-10, 10].
BenchmarkFunctions.alpine_2
— Methodalpine_2(X)
alpine_2(X, n=3)
Compute the n-dimensional Alpine No. 2 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [0, 10].
BenchmarkFunctions.attributes
— Methodattributes(; string)
Display either:
- a general list of attributes
- a list of attributes for a benchmark function
- a list of benchmark functions with that attribute
Examples
BenchmarkFunctions.bartels_conn
— Methodbartels_conn(X)
Compute the 2-dimensional Bartels-Conn function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-500, 500] for i
= 1, 2.
BenchmarkFunctions.beale
— Methodbeale(X)
Compute the 2-dimensional Beale function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-4.5, 4.5] for i
= 1, 2.
BenchmarkFunctions.bird
— Methodbird(X)
Compute the 2-dimensional Bird function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-2π, 2π] for i
= 1, 2.
BenchmarkFunctions.bohachevsky_1
— Methodbohachevsky_1(X)
Compute the 2-dimensional Bohachevsky No. 1 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-100, 100] for i
= 1, 2.
BenchmarkFunctions.bohachevsky_2
— Methodbohachevsky_2(X)
Compute the 2-dimensional Bohachevsky No. 2 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-100, 100] for i
= 1, 2.
BenchmarkFunctions.bohachevsky_3
— Methodbohachevsky_3(X)
Compute the 2-dimensional Bohachevsky No. 3 function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-100, 100] for i
= 1, 2.
BenchmarkFunctions.brent
— Methodbrent(X)
Compute the 2-dimensional Brent function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-10, 10] for i = 1, 2.
BenchmarkFunctions.deckkers_aarts
— Methoddeckkers_aarts(X)
Compute the 2-dimensional Deckkers-Aarts function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-20, 20] for i = 1, 2.
BenchmarkFunctions.gramacy_lee
— Methodgramacy_lee(X)
Compute the 1-dimensional Gramacy-Lee function on sample vector X
.
The function is usually evaluated on x
∈ [-0.5, 2.5].
BenchmarkFunctions.himmelblau
— Methodhimmelblau(X)
Compute the 2-dimensional Himmelblau function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-6, 6] for i
= 1, 2.
BenchmarkFunctions.mccormick
— Methodmccormick(X)
Compute the 2-dimensional McCormick function on sample vector X
.
The function is usually evaluated on x₁
∈ [-1.5, 4] and x₂
∈ [-3, 3]
BenchmarkFunctions.ndgrid
— Methodndgrid(grids...)
Create an n-dimensional grid over grids
– iterables along each dimension.
Examples
julia> ndgrid(0:1)
2-element Array{Tuple{Int64},1}:
(0,)
(1,)
jldoctest julia> ndgrid((0:1,0:1)) 4-element Array{Tuple{Int64,Int64},1}: (0, 0) (1, 0) (0, 1) (1, 1)
BenchmarkFunctions.rosenbrock
— Methodrosenbrock(X)
rosenbrock(X, n=3)
Compute the n-dimensional Rosenbrock function on sample vector X
.
The function is usually evaluated on xᵢ
∈ [-5, 10] or xᵢ
∈ [-2.048, -2.048].