AMPGO multivariate implementation

Project.toml

@@ -7,6 +7,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"

src/ADNLPProblems/ackley.jl

@@ -0,0 +1,16 @@
+export ackley
+using Random
+
+function ackley(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  rng = get(kwargs, :rng, Random.MersenneTwister(0))
+  function f(x)
+    n = length(x)
+    sum1 = sum(x[i]^2 for i = 1:n)
+    sum2 = sum(cos(2 * T(π) * x[i]) for i = 1:n)
+    return -20 * exp(-T(0.2) * sqrt(sum1 / n)) - exp(sum2 / n) + 20 + T(ℯ)
+  end
+  x0 = T[-32 + 64 * rand(rng, T) for _ = 1:n]
+  lvar = fill(T(-32.768), n)
+  uvar = fill(T(32.768), n)
+  return ADNLPModels.ADNLPModel(f, x0; lvar = lvar, uvar = uvar, name = "ackley", kwargs...)
+end

src/ADNLPProblems/griewank.jl

@@ -0,0 +1,12 @@
+export griewank
+
+function griewank(; n::Int = default_nvar, type::Type{T} = Float64, x0::Union{Nothing, AbstractVector{T}} = nothing, kwargs...) where {T}
+  function f(x)
+    n = length(x)
+    sum_term = sum(x[i]^2 for i = 1:n) / 4000
+    prod_term = prod(cos(x[i] / sqrt(T(i))) for i = 1:n)
+    return sum_term - prod_term + 1
+  end
+  x0_local = isnothing(x0) ? zeros(T, n) : x0
+  return ADNLPModels.ADNLPModel(f, x0_local, name = "griewank"; kwargs...)
+end

src/ADNLPProblems/rastrigin.jl

@@ -0,0 +1,12 @@
+export rastrigin
+
+function rastrigin(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  function f(x)
+    n = length(x)
+    return 10 * n + sum(x[i]^2 - 10 * cos(2 * T(π) * x[i]) for i = 1:n)
+  end
+  x0 = zeros(T, n)
+  lvar = T[-5.12 for _ = 1:n]
+  uvar = T[5.12 for _ = 1:n]
+  return ADNLPModels.ADNLPModel(f, x0, lvar = lvar, uvar = uvar, name = "rastrigin"; kwargs...)
+end

src/ADNLPProblems/sphere.jl

@@ -0,0 +1,11 @@
+export sphere
+
+function sphere(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  function f(x)
+    return sum(x[i]^2 for i = 1:length(x))
+  end
+  x0 = zeros(T, n)
+  lvar = fill(T(-5.12), n)
+  uvar = fill(T(5.12), n)
+  return ADNLPModels.ADNLPModel(f, x0; lvar = lvar, uvar = uvar, name = "sphere", kwargs...)
+end

src/Meta/ackley.jl

@@ -0,0 +1,25 @@
+ackley_meta = Dict(
+  :nvar => 100,
+  :variable_nvar => true,
+  :ncon => 0,
+  :variable_ncon => false,
+  :minimize => true,
+  :name => "ackley",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :unconstrained,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_ackley_nvar(; n::Integer = default_nvar, kwargs...) = n
+get_ackley_ncon(; n::Integer = default_nvar, kwargs...) = 0
+get_ackley_nlin(; n::Integer = default_nvar, kwargs...) = 0
+get_ackley_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_ackley_nequ(; n::Integer = default_nvar, kwargs...) = 0
+get_ackley_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/griewank.jl

@@ -0,0 +1,25 @@
+griewank_meta = Dict(
+  :nvar => 100,
+  :variable_nvar => true,
+  :ncon => 0,
+  :variable_ncon => false,
+  :minimize => true,
+  :name => "griewank",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :unconstrained,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_griewank_nvar(; n::Integer = default_nvar, kwargs...) = n
+get_griewank_ncon(; n::Integer = default_nvar, kwargs...) = 0
+get_griewank_nlin(; n::Integer = default_nvar, kwargs...) = 0
+get_griewank_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_griewank_nequ(; n::Integer = default_nvar, kwargs...) = 0
+get_griewank_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/rastrigin.jl

@@ -0,0 +1,25 @@
+rastrigin_meta = Dict(
+  :nvar => 100,
+  :variable_nvar => true,
+  :ncon => 0,
+  :variable_ncon => false,
+  :minimize => true,
+  :name => "rastrigin",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :unconstrained,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_rastrigin_nvar(; n::Integer = default_nvar, kwargs...) = n
+get_rastrigin_ncon(; n::Integer = default_nvar, kwargs...) = 0
+get_rastrigin_nlin(; n::Integer = default_nvar, kwargs...) = 0
+get_rastrigin_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_rastrigin_nequ(; n::Integer = default_nvar, kwargs...) = 0
+get_rastrigin_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/sphere.jl

@@ -0,0 +1,25 @@
+sphere_meta = Dict(
+  :nvar => 100,
+  :variable_nvar => true,
+  :ncon => 0,
+  :variable_ncon => false,
+  :minimize => true,
+  :name => "sphere",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :unconstrained,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_sphere_nvar(; n::Integer = default_nvar, kwargs...) = n
+get_sphere_ncon(; n::Integer = default_nvar, kwargs...) = 0
+get_sphere_nlin(; n::Integer = default_nvar, kwargs...) = 0
+get_sphere_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_sphere_nequ(; n::Integer = default_nvar, kwargs...) = 0
+get_sphere_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/PureJuMP/ackley.jl

@@ -0,0 +1,38 @@
+# Ackley test function
+#
+# A multivariate multimodal optimization problem from the
+# AMPGO test set.
+#
+# Problem Ackley in
+# http://infinity77.net/global_optimization/test_functions_nd_A.html
+# Andrea Gavana
+#
+# f(x) = -20*exp(-0.2*sqrt(sum(x_i^2)/n)) - exp(sum(cos(2*pi*x_i))/n) + 20 + e
+#
+# Global minimum: f(0,...,0) = 0
+# Bounds: x_i in [-32, 32] for all i
+
+export ackley
+
+"Ackley multimodal minimization problem"
+function ackley(args...; n::Int = default_nvar, kwargs...)
+  n < 1 && @warn("ackley: number of variables must be ≥ 1")
+  n = max(1, n)
+
+  nlp = Model()
+
+  x0 = [-32 + 64 * rand() for i = 1:n]
+  @variable(nlp, -32 <= x[i = 1:n] <= 32, start = x0[i])
+  nlp.ext[:has_bounds] = true
+
+  @objective(
+    nlp,
+    Min,
+    -20 * exp(-0.2 * sqrt(sum(x[i]^2 for i = 1:n) / n)) -
+    exp(sum(cos(2 * π * x[i]) for i = 1:n) / n) +
+    20 +
+    exp(1)
+  )
+
+  return nlp
+end

src/PureJuMP/griewank.jl

@@ -0,0 +1,38 @@
+# Griewank test function
+#
+# A multivariate multimodal optimization problem from the
+# AMPGO test set.
+#
+# Problem Griewank in
+# http://infinity77.net/global_optimization/test_functions_nd_G.html
+# Andrea Gavana
+#
+# f(x) = sum(x_i^2)/4000 - prod(cos(x_i/sqrt(i))) + 1
+#
+# Global minimum: f(0,...,0) = 0
+# Bounds: x_i in [-600, 600] for all i
+
+export griewank
+
+"Griewank multimodal minimization problem"
+function griewank(args...; n::Int = default_nvar, x0::Union{Nothing,AbstractVector} = nothing, kwargs...)
+  n < 1 && @warn("griewank: number of variables must be ≥ 1")
+  n = max(1, n)
+
+  nlp = Model()
+
+  if x0 === nothing
+    x0 = zeros(n)
+  elseif length(x0) != n
+    throw(ArgumentError("griewank: length(x0) = $(length(x0)) must equal n = $n"))
+  end
+  @variable(nlp, -600 <= x[i = 1:n] <= 600, start = x0[i])
+
+  @objective(
+    nlp,
+    Min,
+    sum(x[i]^2 for i = 1:n) / 4000 - prod(cos(x[i] / sqrt(i)) for i = 1:n) + 1
+  )
+
+  return nlp
+end

src/PureJuMP/rastrigin.jl

@@ -0,0 +1,30 @@
+# Rastrigin test function
+#
+# A multivariate highly multimodal optimization problem from the
+# AMPGO test set.
+#
+# Problem Rastrigin in
+# http://infinity77.net/global_optimization/test_functions_nd_R.html
+# Andrea Gavana
+#
+# f(x) = 10*n + sum(x_i^2 - 10*cos(2*pi*x_i))
+#
+# Global minimum: f(0,...,0) = 0
+# Bounds: x_i in [-5.12, 5.12] for all i
+
+export rastrigin
+
+"Rastrigin multimodal minimization problem"
+function rastrigin(args...; n::Int = default_nvar, kwargs...)
+  n < 1 && @warn("rastrigin: number of variables must be ≥ 1")
+  n = max(1, n)
+
+  nlp = Model()
+
+  x0 = [0.0 for i = 1:n]
+  @variable(nlp, x[i = 1:n], lower_bound = -5.12, upper_bound = 5.12, start = x0[i])
+
+  @objective(nlp, Min, 10 * n + sum(x[i]^2 - 10 * cos(2 * π * x[i]) for i = 1:n))
+
+  return nlp
+end

src/PureJuMP/sphere.jl

@@ -0,0 +1,30 @@
+# Sphere test function
+#
+# A simple multivariate convex optimization problem from the
+# AMPGO test set.
+#
+# Problem Sphere in
+# http://infinity77.net/global_optimization/test_functions_nd_S.html
+# Andrea Gavana
+#
+# f(x) = sum(x_i^2)
+#
+# Global minimum: f(0,...,0) = 0
+# Bounds: x_i in [-1, 1] for all i
+
+export sphere
+
+"Sphere convex minimization problem"
+function sphere(args...; n::Int = default_nvar, kwargs...)
+  n < 1 && @warn("sphere: number of variables must be ≥ 1")
+  n = max(1, n)
+
+  nlp = Model()
+
+  x0 = zeros(n)
+  @variable(nlp, -1 <= x[i = 1:n] <= 1, start = x0[i])
+
+  @objective(nlp, Min, sum(x[i]^2 for i = 1:n))
+
+  return nlp
+end