BenchmarkFunctions.ackley_1Method
ackley_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_2Method
ackley_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_3Method
ackley_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_4Method
ackley_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.adjimanMethod
adjiman(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_1Method
alpine_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_2Method
alpine_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.attributesMethod
attributes(; 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_connMethod
bartels_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.bealeMethod
beale(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.birdMethod
bird(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_1Method
bohachevsky_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_2Method
bohachevsky_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_3Method
bohachevsky_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.brentMethod
brent(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_aartsMethod
deckkers_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_leeMethod
gramacy_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.himmelblauMethod
himmelblau(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.mccormickMethod
mccormick(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.ndgridMethod
ndgrid(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.rosenbrockMethod
rosenbrock(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].