Add warnings when dimension is modified (#406)

* Fix #354: Add warnings when dimension is modified on NZF1, spmsrtls, and bearing

- NZF1: Warn when n is not a multiple of 13 (adjusted to nearest multiple)
- spmsrtls: Warn when n is adjusted due to minimum dimension requirement (n >= 100)
- bearing: Warn when grid dimensions are adjusted to ensure nx > 0 and ny > 0

These warnings follow the pattern already established in dixmaan* problems.

* Update src/ADNLPProblems/bearing.jl

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

* n%13 removal

* Add @adjust_nvar_warn macro and update all dimension adjustment warnings

- Created @adjust_nvar_warn macro in ADNLPProblems module to standardize
  dimension adjustment warnings across all problems
- Updated warning messages to consistently show both original and adjusted
  values following pattern: 'problem_name: number of variables adjusted
  from {n_orig} to {n}'
- Applied macro to all problems with dimension adjustments:
  - NZF1 (multiple of 13)
  - spmsrtls (adjusted formula)
  - chainwoo (multiple of 4, both :nlp and :nls variants)
  - woods (multiple of 4)
  - srosenbr (multiple of 2)
  - catenary (multiple of 3, minimum 6)
  - clplatea, clplateb, clplatec (perfect squares)
  - fminsrf2 (minimum 4, then perfect square)
  - powellsg (multiple of 4, both :nlp and :nls variants)
  - watson (clamped between 2 and 31, both :nlp and :nls variants)

Addresses issue #354

* Apply suggestions from code review

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

* Update powellsg :nls variant to use @adjust_nvar_warn macro

Ensures consistent warning messages between :nlp and :nls variants,
showing both original and adjusted dimension values.

* PureJuMP implementations

* Update src/PureJuMP/PureJuMP.jl

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

* Add regression tests for dimension-adjustment warnings

* unifying macros for defining problems and dimensions, and added warnings for dimension mismatches in the defined problems.

* adding copilot suggestions to fix dimension warnings in ADNLPProblems and PureJuMP

* Update OptimizationProblems.jl

* final changes

* Update src/OptimizationProblems.jl

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

* addressing review comments

* addressing review comments

* format

* addressing review comments

* further macro additions

* addressing review comments

* missing macro

* fixing failing checks

* reverting elec.jl to the version before the dimension warnings were added. The changes in this commit are to fix the dimension warnings that were introduced in the previous commit. The changes include changing the bounds on the constraints, building a feasible x0, and changing the number of variables and constraints in the model. The changes are made in both the ADNLPProblems and PureJuMP versions of elec.jl.

* passing elec.jl macro

* Update elec.jl

* PureJuMP changes

* Update catenary.jl

* Apply suggestions from code review

Co-authored-by: Tangi Migot <tangi.migot@gmail.com>

* ADNLProblems changes

* Update powellsg.jl

* updating fminsrf2.jl to remove dimension warnings

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Tangi Migot <tangi.migot@gmail.com>

Author: 3 people

src/ADNLPProblems/ADNLPProblems.jl

@@ -1,6 +1,7 @@
 module ADNLPProblems
 
 using Requires
+import ..OptimizationProblems: @adjust_nvar_warn
 
 const default_nvar = 100
 const data_path = joinpath(@__DIR__, "..", "..", "data")

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_org = n
   nbis = max(2, div(n, 13))
   n = 13 * nbis
+  @adjust_nvar_warn("NZF1", n_org, 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_org = n
   nbis = max(2, div(n, 13))
   n = 13 * nbis
+  @adjust_nvar_warn("NZF1", n_org, n)
   l = div(n, 13)
   function F!(r, x; l = l)
     for i = 1:l

src/ADNLPProblems/bearing.jl

@@ -10,6 +10,10 @@ function bearing(;
   # nx > 0 # grid points in 1st direction
   # ny > 0 # grid points in 2nd direction
 
+  nx = max(1, nx)
+  ny = max(1, ny)
+  @adjust_nvar_warn("bearing", n, (nx + 2) * (ny + 2))
+
   b = 10  # grid is (0,2*pi)x(0,2*b)
   e = 1 // 10 # eccentricity
 

src/ADNLPProblems/broydn7d.jl

@@ -1,9 +1,10 @@
 export broydn7d
 
 function broydn7d(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  mod(n, 2) > 0 && @warn("broydn7d: number of variables adjusted to be even")
+  n_org = n
   n2 = max(1, div(n, 2))
   n = 2 * n2
+  @adjust_nvar_warn("broydn7d", n_org, n)
   function f(x; n = length(x), n2 = n2)
     p = 7 // 3
     return abs(1 - 2 * x[2] + (3 - x[1] / 2) * x[1])^p +

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_org = 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_org, n)
 
   ## Model Parameters
   N = div(n, 3) - 2

src/ADNLPProblems/chain.jl

@@ -2,6 +2,7 @@ export chain
 
 function chain(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
   nh = max(2, div(n - 4, 4))
+  @adjust_nvar_warn("chain", n, 4 * nh + 4)
 
   L = 4
   a = 1

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_org = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("chainwoo", n_org, 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_org = n
   n = 4 * max(1, div(n, 4))
+  @adjust_nvar_warn("chainwoo", n_org, n)
   function F!(r, x; n = length(x))
     nb = div(n, 2) - 1
     r[1] = 1

src/ADNLPProblems/channel.jl

@@ -2,6 +2,7 @@ export channel
 
 function channel(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
   nh = max(2, div(n, 8))
+  @adjust_nvar_warn("channel", n, 8 * nh)
 
   nc = 4
   nd = 4

src/ADNLPProblems/clnlbeam.jl

@@ -2,6 +2,7 @@ export clnlbeam
 
 function clnlbeam(args...; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
   N = div(n - 3, 3)
+  @adjust_nvar_warn("clnlbeam", n, 3 * N + 3)
   h = 1 // N
   alpha = 350
   function f(y; N = N, h = h, alpha = alpha)

src/ADNLPProblems/clplatea.jl

@@ -6,9 +6,10 @@ function clplatea(;
   wght = -0.1,
   kwargs...,
 ) where {T}
+  n_org = 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_org, n)
   hp2 = (1 // 2) * p^2
   function f(x; p = p, hp2 = hp2, wght = wght)
     return (eltype(x)(wght) * x[p + (p - 1) * p]) +