Apply suggestions from code review

Co-authored-by: Tangi Migot <[email protected]>

Author: arnavk23

src/ADNLPProblems/boundary.jl

@@ -22,7 +22,7 @@ function boundary(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, k
     x0[i] = i * h * (1 - i * h)
   end
 
-  return ADNLPModels.ADNLPModel(f, x0, name = "boundary", minimize = true; kwargs...)
+  return ADNLPModels.ADNLPModel(f, x0, name = "boundary"; kwargs...)
 end
 
 function boundary(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}

src/ADNLPProblems/variational.jl

@@ -2,10 +2,10 @@ export variational
 
 function variational(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
   h = 1 // (n + 1)
-  x0 = [begin
-    ih = i * h
-    convert(T, ih * (1 - ih))
-    end for i = 1:n]
+  x0 = Vector{T}(undef, n)
+  for i = 1:n
+    x0[i] = i * h * (1 - i * h)
+  end
   f =
     x -> begin
       xext = vcat(zero(eltype(x)), x, zero(eltype(x)))
@@ -22,5 +22,5 @@ function variational(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...
       return 2 * (term1 + n * (convert(eltype(x), h) / 2) * term2)
     end
 
-  return ADNLPModels.ADNLPModel(f, x0, name = "variational", minimize = true; kwargs...)
+  return ADNLPModels.ADNLPModel(f, x0, name = "variational"; kwargs...)
 end

src/Meta/variational.jl

@@ -14,7 +14,7 @@ variational_meta = Dict(
   :best_known_lower_bound => -Inf,
   :best_known_upper_bound => Inf,
   :is_feasible => true,
-  :defined_everywhere => missing,
+  :defined_everywhere => false,
   :origin => :academic,
 )
 

src/PureJuMP/boundary.jl

@@ -21,7 +21,7 @@ function boundary(; n::Int = default_nvar, kwargs...)
     sum(
       (
         2 * x[i] - (i == 1 ? 0 : x[i - 1]) - (i == n ? 0 : x[i + 1]) +
-        (h^2 / 2) * (x[i] + Float64(i) * h + 1)^3
+        (h^2 / 2) * (x[i] + i * h + 1)^3
       )^2 for i = 1:n
     )
   )