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Add problems 5-13 except 9,10 (#375)
* Add Problems 5-13 and metadata --------- Co-authored-by: Tangi Migot <[email protected]>
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‎src/ADNLPProblems/auglag.jl‎

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export auglag
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function auglag(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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λ₁ = T(-0.002008)
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λ₂ = T(-0.001900)
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λ₃ = T(-0.000261)
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function f(x; n = length(x))
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s = zero(T)
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for i = 1:n
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if mod(i, 5) == 0
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prod_term = one(T)
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sum_sq = zero(T)
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for j = 1:5
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idx = i + 1 - j
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if 1 <= idx <= n
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prod_term *= x[idx]
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sum_sq += x[idx]^2
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end
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end
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s += exp(prod_term) + 10 * (sum_sq - 10 - λ₁)^2
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if i >= 4
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s += 10 * (x[i - 3] * x[i - 2] - 5 * x[i - 1] * x[i] - λ₂)^2
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end
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if i >= 4
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s += 10 * (x[i - 4]^3 + x[i - 3]^3 + 1 - λ₃)^2
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end
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end
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end
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return s
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end
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x0 = zeros(T, n)
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for i = 1:n
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m = mod(i, 5)
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if m == 1
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x0[i] = i <= 2 ? -2 : -1
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elseif m == 2
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x0[i] = i <= 2 ? 2 : -1
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elseif m == 3
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x0[i] = 2
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elseif m == 4
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x0[i] = -1
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else
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x0[i] = -1
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end
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end
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return ADNLPModels.ADNLPModel(f, x0, name = "auglag"; kwargs...)
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end

‎src/ADNLPProblems/browngen1.jl‎

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export browngen1
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function browngen1(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n = max(2, n)
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function f(x; n = length(x))
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s = zero(T)
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@inbounds for i = 2:n
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s += (x[i - 1] - 3)^2 + (x[i - 1] - x[i])^2 + exp(20 * (x[i - 1] - x[i]))
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end
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return s
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end
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x0 = [isodd(i) ? zero(T) : -one(T) for i = 1:n]
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return ADNLPModels.ADNLPModel(f, x0, name = "browngen1"; kwargs...)
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end

‎src/ADNLPProblems/browngen2.jl‎

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export browngen2
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function browngen2(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n = max(2, n)
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function f(x; n = length(x))
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s = zero(T)
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@inbounds for i = 2:n
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s += (x[i - 1]^2)^(x[i]^2 + 1) + (x[i]^2)^(x[i - 1]^2 + 1)
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end
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return s
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end
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x0 = [isodd(i) ? -one(T) : one(T) for i = 1:n]
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return ADNLPModels.ADNLPModel(f, x0, name = "browngen2"; kwargs...)
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end

‎src/ADNLPProblems/broyden7d.jl‎

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export broyden7d
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function broyden7d(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return broyden7d(Val(model); kwargs...)
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end
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function broyden7d(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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p = 7 // 3
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function f(x; n = length(x), p = p)
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x0 = zero(T)
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xn1 = zero(T)
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s = zero(T)
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s += abs((3 - 2 * x[1]) * x[1] - x0 - x[2] + 1)^p
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for i = 2:(n - 1)
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s += abs((3 - 2 * x[i]) * x[i] - x[i - 1] - x[i + 1] + 1)^p
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end
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s += abs((3 - 2 * x[n]) * x[n] - x[n - 1] - xn1 + 1)^p
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nh = div(n, 2)
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for i = 1:nh
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s += abs(x[i] + x[i + nh])^p
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end
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return s
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end
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x0 = fill(-one(T), n)
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return ADNLPModels.ADNLPModel(f, x0, name = "broyden7d"; kwargs...)
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end
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function broyden7d(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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x0 = fill(-one(T), n)
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function F!(r, x; n = length(x))
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p = 7 // 6
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nh = div(n, 2)
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@inbounds begin
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r[1] = abs((3 - 2 * x[1]) * x[1] - zero(T) - x[2] + one(T))^p
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for i = 2:(n - 1)
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r[i] = abs((3 - 2 * x[i]) * x[i] - x[i - 1] - x[i + 1] + one(T))^p
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end
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r[n] = abs((3 - 2 * x[n]) * x[n] - x[n - 1] - zero(T) + one(T))^p
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for i = 1:nh
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r[n + i] = abs(x[i] + x[i + nh])^p
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end
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end
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return r
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end
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nequ = n + div(n, 2)
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return ADNLPModels.ADNLSModel!(F!, x0, nequ, name = "broyden7d-nls"; kwargs...)
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end

‎src/ADNLPProblems/chainwoo.jl‎

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export chainwoo
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3-
function chainwoo(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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function chainwoo(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return chainwoo(Val(model); kwargs...)
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end
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function chainwoo(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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(n % 4 == 0) || @warn("chainwoo: number of variables adjusted to be a multiple of 4")
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n = 4 * max(1, div(n, 4))
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function f(x; n = length(x))
@@ -16,3 +21,26 @@ function chainwoo(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) w
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x0 = vcat([-3, -1, -3, -1], -2 * ones(T, n - 4))
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return ADNLPModels.ADNLPModel(f, x0, name = "chainwoo"; kwargs...)
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end
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function chainwoo(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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(n % 4 == 0) || @warn("chainwoo: number of variables adjusted to be a multiple of 4")
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n = 4 * max(1, div(n, 4))
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function F!(r, x; n = length(x))
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nb = div(n, 2) - 1
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r[1] = 1
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idx = 2
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@inbounds for i = 1:nb
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r[idx] = 10 * (x[2 * i] - x[2 * i - 1]^2)
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r[idx + 1] = 1 - x[2 * i - 1]
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r[idx + 2] = T(sqrt(90)) * (x[2 * i + 2] - x[2 * i + 1]^2)
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r[idx + 3] = 1 - x[2 * i + 1]
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r[idx + 4] = T(sqrt(10)) * (x[2 * i] + x[2 * i + 2] - 2)
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r[idx + 5] = (x[2 * i] - x[2 * i + 2]) / T(sqrt(10))
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idx += 6
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end
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return r
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end
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nequ = 1 + 6 * (div(n, 2) - 1)
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x0 = vcat([-3, -1, -3, -1], -2 * ones(T, n - 4))
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return ADNLPModels.ADNLSModel!(F!, x0, nequ, name = "chainwoo-nls"; kwargs...)
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end

‎src/ADNLPProblems/cragglvy2.jl‎

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export cragglvy2
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3-
function cragglvy2(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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function cragglvy2(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return cragglvy2(Val(model); kwargs...)
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end
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function cragglvy2(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n < 2 && @warn("cragglvy2: number of variables must be ≥ 2")
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n = max(2, n)
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function f(x; n = length(x))
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x0 = vcat(1, 2 * ones(T, n - 1))
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return ADNLPModels.ADNLPModel(f, x0, name = "cragglvy2"; kwargs...)
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end
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function cragglvy2(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n < 2 && @warn("cragglvy2: number of variables must be ≥ 2")
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n = max(2, n)
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function F!(r, x; n = length(x))
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for i = 1:(div(n, 2) - 1)
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r[5 * (i - 1) + 1] = (exp(x[2 * i - 1]) - x[2 * i])^2
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r[5 * (i - 1) + 2] = 10 * (x[2 * i] - x[2 * i + 1])^3
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r[5 * (i - 1) + 3] = (tan(x[2 * i + 1] - x[2 * i + 2]))^2
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r[5 * (i - 1) + 4] = x[2 * i - 1]^4
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r[5 * (i - 1) + 5] = x[2 * i + 2] - 1
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end
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return r
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end
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nequ = 5 * (div(n, 2) - 1)
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x0 = vcat(T(1), T(2) * ones(T, n - 1))
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return ADNLPModels.ADNLSModel!(F!, x0, nequ, name = "cragglvy2-nls"; kwargs...)
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end

‎src/ADNLPProblems/genbroydenb.jl‎

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export genbroydenb
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function genbroydenb(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return genbroydenb(Val(model); kwargs...)
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end
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function genbroydenb(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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p = 7 // 3
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function f(x; n = length(x))
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s = zero(T)
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for i = 1:n
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diag = (2 + 5 * x[i]^2) * x[i] + 1
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neigh = zero(T)
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@inbounds for j = max(1, i - 5):min(n, i + 1)
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if j != i
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neigh += x[j] * (1 + x[j])
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end
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end
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s += abs(diag + neigh)^p
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end
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return s
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end
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x0 = fill(-one(T), n)
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return ADNLPModels.ADNLPModel(f, x0, name = "genbroydenb", minimize = true; kwargs...)
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end
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function genbroydenb(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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p = 7 // 6
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function F!(r, x)
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m = length(x)
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@inbounds for i = 1:m
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diag = (2 + 5 * x[i]^2) * x[i] + 1
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neigh = zero(T)
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for j = max(1, i - 5):min(m, i + 1)
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if j != i
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neigh += x[j] * (1 + x[j])
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end
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end
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z = diag + neigh
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r[i] = abs(z)^(p)
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end
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return r
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end
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x0 = fill(-one(T), n)
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return ADNLPModels.ADNLSModel!(F!, x0, n, name = "genbroydenb-nls"; kwargs...)
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end

‎src/ADNLPProblems/genbroydentri.jl‎

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export genbroydentri
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function genbroydentri(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return genbroydentri(Val(model); kwargs...)
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end
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function genbroydentri(
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::Val{:nlp};
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n::Int = default_nvar,
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type::Type{T} = Float64,
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kwargs...,
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) where {T}
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p = 7 // 3
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function f(x; n = length(x))
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x0 = zero(T)
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xn1 = zero(T)
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s = abs((3 - 2 * x[1]) * x[1] - x0 - x[2] + 1)^p
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for i = 2:(n - 1)
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s += abs((3 - 2 * x[i]) * x[i] - x[i - 1] - x[i + 1] + 1)^p
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end
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s += abs((3 - 2 * x[n]) * x[n] - x[n - 1] - xn1 + 1)^p
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return s
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end
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x0 = fill(-one(T), n)
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return ADNLPModels.ADNLPModel(f, x0, name = "genbroydentri"; kwargs...)
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end
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function genbroydentri(
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::Val{:nls};
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n::Int = default_nvar,
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type::Type{T} = Float64,
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kwargs...,
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) where {T}
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p = 7 // 6
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x0 = fill(-one(T), n)
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function F!(r, x)
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x0_val = zero(T)
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xn1_val = zero(T)
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@inbounds begin
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r[1] = abs((3 - 2 * x[1]) * x[1] - x0_val - x[2] + 1)^p
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for i = 2:(n - 1)
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r[i] = abs((3 - 2 * x[i]) * x[i] - x[i - 1] - x[i + 1] + 1)^p
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end
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r[n] = abs((3 - 2 * x[n]) * x[n] - x[n - 1] - xn1_val + 1)^p
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end
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return r
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end
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return ADNLPModels.ADNLSModel!(F!, x0, n, name = "genbroydentri-nls"; kwargs...)
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end

‎src/ADNLPProblems/genrose.jl‎

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export genrose, rosenbrock
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3-
function genrose(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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function genrose(; use_nls::Bool = false, kwargs...)
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model = use_nls ? :nls : :nlp
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return genrose(Val(model); kwargs...)
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end
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function genrose(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n < 2 && @warn("genrose: number of variables must be ≥ 2")
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n = max(2, n)
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function f(x; n = length(x))
@@ -12,5 +17,22 @@ function genrose(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) wh
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return ADNLPModels.ADNLPModel(f, x0, name = "genrose"; kwargs...)
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end
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function genrose(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
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n = max(2, n)
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nequ = 2 * (n - 1) + 1
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function F!(r, x; n = length(x))
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@inbounds begin
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r[1] = 1
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for i = 1:(n - 1)
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r[i + 1] = 10 * (x[i + 1] - x[i]^2)
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r[i + n] = x[i] - 1
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end
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end
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return r
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end
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x0 = T.([i / (n + 1) for i = 1:n])
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return ADNLPModels.ADNLSModel!(F!, x0, nequ, name = "genrose-nls"; kwargs...)
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end
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rosenbrock(args...; kwargs...) =
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genrose(args..., n = 2, name = "rosenbrock"; delete!(Dict(kwargs), :n)...)

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