Add warnings when dimension is modified

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_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 +10,12 @@ function bearing(;
   # nx > 0 # grid points in 1st direction
   # ny > 0 # grid points in 2nd direction
 
+  n_orig = n
+  nx = max(1, nx)
+  ny = max(1, ny)
+  n = (nx + 2) * (ny + 2)
+  @adjust_nvar_warn("bearing", n_orig, n)
+
   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 +

src/OptimizationProblems.jl

@@ -2,6 +2,23 @@ module OptimizationProblems
 
 using DataFrames
 
+_adjust_nvar_warn_message(problem_name, n_orig, n) =
+  string(problem_name, ": number of variables adjusted from ", n_orig, " to ", n)
+
+"""
+    @adjust_nvar_warn(problem_name, n_orig, n)
+
+Issue a warning if the number of variables was adjusted, showing both original and adjusted values.
+"""
+macro adjust_nvar_warn(problem_name, n_orig, n)
+  helper = GlobalRef(@__MODULE__, :_adjust_nvar_warn_message)
+  return quote
+    local _n_orig = $(esc(n_orig))
+    local _n = $(esc(n))
+    (_n == _n_orig) || @warn($helper($(esc(problem_name)), _n_orig, _n))
+  end
+end
+
 include("ADNLPProblems/ADNLPProblems.jl")
 include("PureJuMP/PureJuMP.jl")
 

src/PureJuMP/NZF1.jl

@@ -7,9 +7,10 @@
 export NZF1
 
 function NZF1(args...; n::Int = default_nvar, kwargs...)
-  mod(n, 13) != 0 && @warn("NZF1: number of variables adjusted to be divisible by 13 and ≥ 26")
+  n_orig = n
   nbis = max(2, div(n, 13))
   n = 13 * nbis
+  @adjust_nvar_warn("NZF1", n_orig, n)
 
   l = div(n, 13)
 

src/PureJuMP/PureJuMP.jl

@@ -19,6 +19,7 @@ function _ensure_data!(key::Symbol, relpath::AbstractString)
 end
 
 using JuMP, LinearAlgebra, SpecialFunctions
+import ..OptimizationProblems: @adjust_nvar_warn
 
 path = dirname(@__FILE__)
 files = filter(x -> x[(end - 2):end] == ".jl", readdir(path))

src/PureJuMP/bearing.jl

@@ -28,6 +28,12 @@ function bearing(
   # nx > 0 # grid points in 1st direction
   # ny > 0 # grid points in 2nd direction
 
+  n_orig = n
+  nx = max(1, nx)
+  ny = max(1, ny)
+  n = (nx + 2) * (ny + 2)
+  @adjust_nvar_warn("bearing", n_orig, n)
+
   b = 10  # grid is (0,2*pi)x(0,2*b)
   e = 0.1 # eccentricity
 

src/PureJuMP/broydn7d.jl

@@ -46,9 +46,10 @@ export broydn7d
 
 "Broyden 7-diagonal model in size `n`"
 function broydn7d(args...; n::Int = default_nvar, p::Float64 = 7 / 3, kwargs...)
-  mod(n, 2) > 0 && @warn("broydn7d: number of variables adjusted to be even")
+  n_orig = n
   n2 = max(1, div(n, 2))
   n = 2 * n2
+  @adjust_nvar_warn("broydn7d", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/catenary.jl

@@ -17,10 +17,10 @@
 export catenary
 
 function catenary(args...; n::Int = default_nvar, Bl = 1.0, FRACT = 0.6, kwargs...)
-  (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
 

src/PureJuMP/chainwoo.jl

@@ -35,8 +35,9 @@ export chainwoo
 
 "The chained Woods function in size `n`, a variant on the Woods function"
 function chainwoo(args...; n::Int = default_nvar, kwargs...)
-  (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)
 
   nlp = Model()
 

src/PureJuMP/clplatea.jl

@@ -26,9 +26,10 @@ export clplatea
 
 "The clamped plate problem (Strang, Nocedal, Dax)."
 function clplatea(args...; n::Int = default_nvar, wght::Float64 = -0.1, kwargs...)
+  n_orig = n
   p = floor(Int, sqrt(n))
-  p * p != n && @warn("clplatea: number of variables adjusted from $n down to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplatea", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/clplateb.jl

@@ -27,9 +27,10 @@ export clplateb
 
 "The clamped plate problem (Strang, Nocedal, Dax)."
 function clplateb(args...; n::Int = default_nvar, wght::Float64 = -0.1, kwargs...)
+  n_orig = n
   p = floor(Int, sqrt(n))
-  p * p != n && @warn("clplateb: number of variables adjusted from $n down to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplateb", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/clplatec.jl

@@ -33,9 +33,10 @@ function clplatec(
   l::Float64 = 0.01,
   kwargs...,
 )
+  n_orig = n
   p = floor(Int, sqrt(n))
-  p * p != n && @warn("clplatec: number of variables adjusted from $n down to $(p*p)")
   n = p * p
+  @adjust_nvar_warn("clplatec", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/dixmaan_efgh.jl

@@ -33,9 +33,10 @@ function dixmaane(
   δ::Float64 = 0.125,
   kwargs...,
 )
-  (n % 3 == 0) || @warn("dixmaan: number of variables adjusted to be a multiple of 3")
+  n_orig = n
   m = max(1, div(n, 3))
   n = 3 * m
+  @adjust_nvar_warn("dixmaan", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/dixmaan_ijkl.jl

@@ -33,9 +33,10 @@ function dixmaani(
   δ::Float64 = 0.125,
   kwargs...,
 )
-  (n % 3 == 0) || @warn("dixmaan: number of variables adjusted to be a multiple of 3")
+  n_orig = n
   m = max(1, div(n, 3))
   n = 3 * m
+  @adjust_nvar_warn("dixmaan", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/dixmaan_mnop.jl

@@ -31,9 +31,10 @@ function dixmaanm(
   δ::Float64 = 0.125,
   kwargs...,
 )
-  (n % 3 == 0) || @warn("dixmaan: number of variables adjusted to be a multiple of 3")
+  n_orig = n
   m = max(1, div(n, 3))
   n = 3 * m
+  @adjust_nvar_warn("dixmaan", n_orig, n)
 
   nlp = Model()
 

src/PureJuMP/fminsrf2.jl

@@ -21,12 +21,12 @@
 export fminsrf2
 
 function fminsrf2(args...; n::Int = default_nvar, kwargs...)
-  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 = 1.0
   slopej = 4.0