Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: arnavk23

src/ADNLPProblems/ackley.jl

@@ -1,12 +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(T) for _ = 1:n]
-  return ADNLPModels.ADNLPModel(f, x0, name = "ackley"; kwargs...)
+  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

@@ -1,12 +1,12 @@
 export griewank
 
-function griewank(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+function griewank(; n::Int = default_nvar, type::Type{T} = Float64, x0::AbstractVector{T} = T[], 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 = T[-600 + 1200 * rand(T) for _ = 1:n]
-  return ADNLPModels.ADNLPModel(f, x0, name = "griewank"; kwargs...)
+  x0_local = isempty(x0) ? zeros(T, n) : x0
+  return ADNLPModels.ADNLPModel(f, x0_local, name = "griewank"; kwargs...)
 end

src/ADNLPProblems/rastrigin.jl

@@ -5,6 +5,8 @@ function rastrigin(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...)
     n = length(x)
     return 10 * n + sum(x[i]^2 - 10 * cos(2 * T(π) * x[i]) for i = 1:n)
   end
-  x0 = T[-5.12 + 10.24 * rand(T) for _ = 1:n]
-  return ADNLPModels.ADNLPModel(f, x0, name = "rastrigin"; kwargs...)
+  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

@@ -4,6 +4,8 @@ function sphere(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) whe
   function f(x)
     return sum(x[i]^2 for i = 1:length(x))
   end
-  x0 = T[-1 + 2 * rand(T) for _ = 1:n]
-  return ADNLPModels.ADNLPModel(f, x0, name = "sphere"; kwargs...)
+  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

@@ -7,12 +7,12 @@ ackley_meta = Dict(
   :name => "ackley",
   :has_equalities_only => false,
   :has_inequalities_only => false,
-  :has_bounds => false,
+  :has_bounds => true,
   :has_fixed_variables => false,
   :objtype => :other,
   :contype => :unconstrained,
-  :best_known_lower_bound => -Inf,
-  :best_known_upper_bound => 20.0 + exp(1.0),
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
   :is_feasible => true,
   :defined_everywhere => missing,
   :origin => :unknown,

src/Meta/griewank.jl

@@ -7,12 +7,12 @@ griewank_meta = Dict(
   :name => "griewank",
   :has_equalities_only => false,
   :has_inequalities_only => false,
-  :has_bounds => false,
+  :has_bounds => true,
   :has_fixed_variables => false,
   :objtype => :other,
   :contype => :unconstrained,
-  :best_known_lower_bound => -Inf,
-  :best_known_upper_bound => 360001.0,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
   :is_feasible => true,
   :defined_everywhere => missing,
   :origin => :unknown,

src/Meta/rastrigin.jl

@@ -11,8 +11,8 @@ rastrigin_meta = Dict(
   :has_fixed_variables => false,
   :objtype => :other,
   :contype => :unconstrained,
-  :best_known_lower_bound => -Inf,
-  :best_known_upper_bound => 10000.0,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
   :is_feasible => true,
   :defined_everywhere => missing,
   :origin => :unknown,

src/Meta/sphere.jl

@@ -7,12 +7,12 @@ sphere_meta = Dict(
   :name => "sphere",
   :has_equalities_only => false,
   :has_inequalities_only => false,
-  :has_bounds => false,
+  :has_bounds => true,
   :has_fixed_variables => false,
   :objtype => :other,
   :contype => :unconstrained,
-  :best_known_lower_bound => -Inf,
-  :best_known_upper_bound => 100.0,
+  :best_known_lower_bound => 0.0,
+  :best_known_upper_bound => 0.0,
   :is_feasible => true,
   :defined_everywhere => missing,
   :origin => :unknown,

src/PureJuMP/ackley.jl

@@ -22,7 +22,8 @@ function ackley(args...; n::Int = default_nvar, kwargs...)
   nlp = Model()
 
   x0 = [-32 + 64 * rand() for i = 1:n]
-  @variable(nlp, x[i = 1:n], start = x0[i])
+  @variable(nlp, -32 <= x[i = 1:n] <= 32, start = x0[i])
+  nlp.ext[:has_bounds] = true
 
   @objective(
     nlp,

src/PureJuMP/griewank.jl

@@ -15,14 +15,18 @@
 export griewank
 
 "Griewank multimodal minimization problem"
-function griewank(args...; n::Int = default_nvar, kwargs...)
+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()
 
-  x0 = [-600 + 1200 * rand() for i = 1:n]
-  @variable(nlp, x[i = 1:n], start = x0[i])
+  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,