Add warnings when dimension is modified

src/ADNLPProblems/ADNLPProblems.jl

@@ -26,6 +26,27 @@ end
   @require ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a" begin
     using JLD2, LinearAlgebra, SparseArrays, SpecialFunctions
 
+    """
+        @adjust_nvar_warn(problem_name, n_orig, n)
+
+    Issue a warning if the number of variables was adjusted, showing both original and adjusted values.
+    This macro provides consistent warning messages across all problems with dimension adjustments.
+
+    # Example
+    ```julia
+    n_orig = n
+    n = 4 * max(1, div(n, 4))
+    @adjust_nvar_warn("woods", n_orig, n)
+    ```
+    """
+    macro adjust_nvar_warn(problem_name, n_orig, n)
+      return quote
+        ($(esc(n)) == $(esc(n_orig))) ||
+          @warn(string($(esc(problem_name)), ": number of variables adjusted from ",
+                      $(esc(n_orig)), " to ", $(esc(n))))
+      end
+    end
+
     path = dirname(@__FILE__)
     files = filter(x -> x[(end - 2):end] == ".jl", readdir(path))
     for file in files

src/ADNLPProblems/NZF1.jl

@@ -6,8 +6,10 @@ function NZF1(; use_nls::Bool = false, kwargs...)
 end
 
 function NZF1(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   nbis = max(2, div(n, 13))
   n = 13 * nbis
+  @adjust_nvar_warn("NZF1", n_orig, n)
   l = div(n, 13)
   function f(x; l = l)
     return sum(
@@ -29,8 +31,10 @@ function NZF1(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwarg
 end
 
 function NZF1(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   nbis = max(2, div(n, 13))
   n = 13 * nbis
+  @adjust_nvar_warn("NZF1", n_orig, n)
   l = div(n, 13)
   function F!(r, x; l = l)
     for i = 1:l

src/ADNLPProblems/bearing.jl

@@ -10,6 +10,22 @@ function bearing(;
   # nx > 0 # grid points in 1st direction
   # ny > 0 # grid points in 2nd direction
 
+  # Ensure nx and ny are at least 1, and warn if they need adjustment
+  nx_orig = nx
+  ny_orig = ny
+  nx = max(1, nx)
+  ny = max(1, ny)
+  if nx != nx_orig || ny != ny_orig
+    msg_parts = String[]
+    if nx != nx_orig
+      push!(msg_parts, "nx from $(nx_orig) to $(nx)")
+    end
+    if ny != ny_orig
+      push!(msg_parts, "ny from $(ny_orig) to $(ny)")
+    end
+    @warn("bearing: grid dimensions adjusted: " * join(msg_parts, ", "))
+  end
+
   b = 10  # grid is (0,2*pi)x(0,2*b)
   e = 1 // 10 # eccentricity
 

src/ADNLPProblems/catenary.jl

@@ -8,10 +8,10 @@ function catenary(
   FRACT = 0.6,
   kwargs...,
 ) where {T}
-  (n % 3 == 0) || @warn("catenary: number of variables adjusted to be a multiple of 3")
+  n_orig = n
   n = 3 * max(1, div(n, 3))
-  (n < 6) || @warn("catenary: number of variables adjusted to be greater or equal to 6")
   n = max(n, 6)
+  @adjust_nvar_warn("catenary", n_orig, n)
 
   ## Model Parameters
   N = div(n, 3) - 2

src/ADNLPProblems/chainwoo.jl

@@ -6,8 +6,9 @@ function chainwoo(; use_nls::Bool = false, kwargs...)
 end
 
 function chainwoo(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 4 == 0) || @warn("chainwoo: number of variables adjusted to be a multiple of 4")
+  n_orig = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("chainwoo", n_orig, n)
   function f(x; n = length(x))
     return 1 + sum(
       100 * (x[2 * i] - x[2 * i - 1]^2)^2 +
@@ -23,8 +24,9 @@ function chainwoo(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, k
 end
 
 function chainwoo(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 4 == 0) || @warn("chainwoo: number of variables adjusted to be a multiple of 4")
+  n_orig = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("chainwoo", n_orig, n)
   function F!(r, x; n = length(x))
     nb = div(n, 2) - 1
     r[1] = 1

src/ADNLPProblems/clplatea.jl

@@ -6,9 +6,10 @@ function clplatea(;
   wght = -0.1,
   kwargs...,
 ) where {T}
+  n_orig = n
   p = max(floor(Int, sqrt(n)), 3)
-  p * p != n && @warn("clplatea: number of variables adjusted from $n to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplatea", n_orig, n)
   hp2 = (1 // 2) * p^2
   function f(x; p = p, hp2 = hp2, wght = wght)
     return (eltype(x)(wght) * x[p + (p - 1) * p]) +

src/ADNLPProblems/clplateb.jl

@@ -6,9 +6,10 @@ function clplateb(;
   wght = -0.1,
   kwargs...,
 ) where {T}
+  n_orig = n
   p = max(floor(Int, sqrt(n)), 3)
-  p * p != n && @warn("clplateb: number of variables adjusted from $n to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplateb", n_orig, n)
   hp2 = 1 // 2 * p^2
   function f(x; p = p, hp2 = hp2, wght = wght)
     return sum(eltype(x)(wght) / (p - 1) * x[p + (j - 1) * p] for j = 1:p) +

src/ADNLPProblems/clplatec.jl

@@ -8,9 +8,10 @@ function clplatec(;
   l = 0.01,
   kwargs...,
 ) where {T}
+  n_orig = n
   p = max(floor(Int, sqrt(n)), 3)
-  p * p != n && @warn("clplatec: number of variables adjusted from $n to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplatec", n_orig, n)
 
   hp2 = 1 // 2 * p^2
   function f(x; p = p, hp2 = hp2, wght = wght, r = r, l = l)

src/ADNLPProblems/fminsrf2.jl

@@ -1,12 +1,11 @@
 export fminsrf2
 
 function fminsrf2(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  n < 4 && @warn("fminsrf2: number of variables must be ≥ 4")
+  n_orig = n
   n = max(4, n)
-
   p = floor(Int, sqrt(n))
-  p * p != n && @warn("fminsrf2: number of variables adjusted from $n down to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("fminsrf2", n_orig, n)
 
   h00 = 1
   slopej = 4

src/ADNLPProblems/powellsg.jl

@@ -6,8 +6,9 @@ function powellsg(; use_nls::Bool = false, kwargs...)
 end
 
 function powellsg(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 4 == 0) || @warn("powellsg: number of variables adjusted to be a multiple of 4")
-  n = 4 * max(1, div(n, 4))  # number of variables adjusted to be a multiple of 4
+  n_orig = n
+  n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("powellsg", n_orig, n)
   function f(x; n = length(x))
     return sum(
       (x[j] + 10 * x[j + 1])^2 +
@@ -24,8 +25,9 @@ function powellsg(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, k
 end
 
 function powellsg(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 4 == 0) || @warn("powellsg: number of variables adjusted to be a multiple of 4")
+  n_orig = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("powellsg", n_orig, n)
   function F!(r, x; n = length(x))
     @inbounds for j = 1:4:n
       r[j] = x[j] + 10 * x[j + 1]

src/ADNLPProblems/spmsrtls.jl

@@ -6,8 +6,10 @@ function spmsrtls(; use_nls::Bool = false, kwargs...)
 end
 
 function spmsrtls(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   m = max(Int(round((n + 2) / 3)), 34)
   n = m * 3 - 2
+  @adjust_nvar_warn("spmsrtls", n_orig, n)
   p = [sin(i^2) for i = 1:n]
   x0 = T[p[i] / 5 for i = 1:n]
 
@@ -59,8 +61,10 @@ function spmsrtls(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, k
 end
 
 function spmsrtls(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   m = max(Int(round((n + 2) / 3)), 34)
   n = m * 3 - 2
+  @adjust_nvar_warn("spmsrtls", n_orig, n)
   p = [sin(i^2) for i = 1:n]
   x0 = T[p[i] / 5 for i = 1:n]
 

src/ADNLPProblems/srosenbr.jl

@@ -1,8 +1,9 @@
 export srosenbr
 
 function srosenbr(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 2 == 0) || @warn("srosenbr: number of variables adjusted to be even")
+  n_orig = n
   n = 2 * max(1, div(n, 2))
+  @adjust_nvar_warn("srosenbr", n_orig, n)
   function f(x; n = length(x))
     return sum(100 * (x[2 * i] - x[2 * i - 1]^2)^2 + (x[2 * i - 1] - 1)^2 for i = 1:div(n, 2))
   end

src/ADNLPProblems/watson.jl

@@ -6,7 +6,9 @@ function watson(; use_nls::Bool = false, kwargs...)
 end
 
 function watson(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   n = min(max(n, 2), 31)
+  @adjust_nvar_warn("watson", n_orig, n)
   function f(x; n = n)
     Ti = eltype(x)
     return 1 // 2 * sum(
@@ -31,7 +33,9 @@ function watson(::Val{:nlp}; n::Int = default_nvar, type::Type{T} = Float64, kwa
 end
 
 function watson(::Val{:nls}; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   n = min(max(n, 2), 31)
+  @adjust_nvar_warn("watson", n_orig, n)
   function F!(r, x; n = n)
     Ti = eltype(x)
     for i = 1:29

src/ADNLPProblems/woods.jl

@@ -1,8 +1,9 @@
 export woods
 
 function woods(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  (n % 4 == 0) || @warn("woods: number of variables adjusted to be a multiple of 4")
+  n_orig = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("woods", n_orig, n)
   function f(x; n = length(x))
     return sum(
       100 * (x[4 * i - 2] - x[4 * i - 3]^2)^2 +