OptimizationProblems.jl/src/ADNLPProblems/dixmaan_efgh.jl at abd2a674d84a8ee199fc97fe6871aaeda95f838a · JuliaSmoothOptimizers/OptimizationProblems.jl · GitHub

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export dixmaane, dixmaanf, dixmaang, dixmaanh

function dixmaane(;
  n::Int = default_nvar,
  type::Type{T} = Float64,
  α = 1,
  β = 0,
  γ = 125 // 1000,
  δ = 125 // 1000,
  kwargs...,
) where {T}
  n_orig = n
  m = max(1, div(n, 3))
  n = 3 * m
  @adjust_nvar_warn("dixmaan", n_orig, n)
  function f(x; n = length(x), α = α, β = β, γ = γ, δ = δ)
    return 1 +
           sum(i // n * α * x[i]^2 for i = 1:n) +
           sum(β * x[i]^2 * (x[i + 1] + x[i + 1]^2)^2 for i = 1:(n - 1)) +
           sum(γ * x[i]^2 * x[i + m]^4 for i = 1:(2 * m)) +
           sum(i // n * δ * x[i] * x[i + 2 * m] for i = 1:m)
  end
  x0 = 2 * ones(T, n)
  return ADNLPModels.ADNLPModel(f, x0, name = "dixmaane"; kwargs...)
end

dixmaanf(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} = dixmaane(
  n = n,
  type = type,
  α = 1,
  β = 625 // 10000,
  γ = 625 // 10000,
  δ = 625 // 10000,
  name = "dixmaanf";
  kwargs...,
)

dixmaang(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} = dixmaane(
  n = n,
  type = type,
  α = 1,
  β = 125 // 1000,
  γ = 125 // 1000,
  δ = 125 // 1000,
  name = "dixmaang";
  kwargs...,
)

dixmaanh(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} = dixmaane(
  n = n,
  type = type,
  α = 1,
  β = 26 // 100,
  γ = 26 // 100,
  δ = 26 // 100,
  name = "dixmaanh";
  kwargs...,
)