diff --git a/src/ADNLPProblems/ADNLPProblems.jl b/src/ADNLPProblems/ADNLPProblems.jl
index 824468a1f..23c40a033 100644
--- a/src/ADNLPProblems/ADNLPProblems.jl
+++ b/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")
diff --git a/src/ADNLPProblems/NZF1.jl b/src/ADNLPProblems/NZF1.jl
index e56c47ea0..5edd35f6f 100644
--- a/src/ADNLPProblems/NZF1.jl
+++ b/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
diff --git a/src/ADNLPProblems/bearing.jl b/src/ADNLPProblems/bearing.jl
index 40ab56ca9..238d07e30 100644
--- a/src/ADNLPProblems/bearing.jl
+++ b/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
 
diff --git a/src/ADNLPProblems/broydn7d.jl b/src/ADNLPProblems/broydn7d.jl
index 0ca69b21d..d76b8fa11 100644
--- a/src/ADNLPProblems/broydn7d.jl
+++ b/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_orig = n
   n2 = max(1, div(n, 2))
   n = 2 * n2
+  @adjust_nvar_warn("broydn7d", n_orig, 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 +
diff --git a/src/ADNLPProblems/catenary.jl b/src/ADNLPProblems/catenary.jl
index 0c4756f65..345a0bdc3 100644
--- a/src/ADNLPProblems/catenary.jl
+++ b/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
diff --git a/src/ADNLPProblems/chain.jl b/src/ADNLPProblems/chain.jl
index f92a59202..5d0db8cb1 100644
--- a/src/ADNLPProblems/chain.jl
+++ b/src/ADNLPProblems/chain.jl
@@ -1,7 +1,10 @@
 export chain
 
 function chain(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   nh = max(2, div(n - 4, 4))
+  n = 4 * nh + 4
+  @adjust_nvar_warn("chain", n_orig, n)
 
   L = 4
   a = 1
diff --git a/src/ADNLPProblems/chainwoo.jl b/src/ADNLPProblems/chainwoo.jl
index 9d1520fe8..154b71ba8 100644
--- a/src/ADNLPProblems/chainwoo.jl
+++ b/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
diff --git a/src/ADNLPProblems/channel.jl b/src/ADNLPProblems/channel.jl
index 154f6ebd5..718d0bf7f 100644
--- a/src/ADNLPProblems/channel.jl
+++ b/src/ADNLPProblems/channel.jl
@@ -1,7 +1,10 @@
 export channel
 
 function channel(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   nh = max(2, div(n, 8))
+  n = 8 * nh
+  @adjust_nvar_warn("channel", n_orig, n)
 
   nc = 4
   nd = 4
diff --git a/src/ADNLPProblems/clnlbeam.jl b/src/ADNLPProblems/clnlbeam.jl
index fc46d8e97..bf25a8b68 100644
--- a/src/ADNLPProblems/clnlbeam.jl
+++ b/src/ADNLPProblems/clnlbeam.jl
@@ -1,7 +1,10 @@
 export clnlbeam
 
 function clnlbeam(args...; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   N = div(n - 3, 3)
+  n = 3 * N + 3
+  @adjust_nvar_warn("clnlbeam", n_orig, n)
   h = 1 // N
   alpha = 350
   function f(y; N = N, h = h, alpha = alpha)
diff --git a/src/ADNLPProblems/clplatea.jl b/src/ADNLPProblems/clplatea.jl
index a54e08ade..7174be032 100644
--- a/src/ADNLPProblems/clplatea.jl
+++ b/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]) +
diff --git a/src/ADNLPProblems/clplateb.jl b/src/ADNLPProblems/clplateb.jl
index a732e4311..f1fbc0120 100644
--- a/src/ADNLPProblems/clplateb.jl
+++ b/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) +
diff --git a/src/ADNLPProblems/clplatec.jl b/src/ADNLPProblems/clplatec.jl
index 5d77a3f36..43d73eae4 100644
--- a/src/ADNLPProblems/clplatec.jl
+++ b/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)
diff --git a/src/ADNLPProblems/dixmaan_efgh.jl b/src/ADNLPProblems/dixmaan_efgh.jl
index 325a3c089..b38fb16e9 100644
--- a/src/ADNLPProblems/dixmaan_efgh.jl
+++ b/src/ADNLPProblems/dixmaan_efgh.jl
@@ -9,9 +9,10 @@ function dixmaane(;
   δ = 125 // 1000,
   kwargs...,
 ) where {T}
-  (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("dixmaane", n_orig, n)
   function f(x; n = length(x), α = α, β = β, γ = γ, δ = δ)
     return 1 +
            sum(i // n * α * x[i]^2 for i = 1:n) +
diff --git a/src/ADNLPProblems/dixmaan_ijkl.jl b/src/ADNLPProblems/dixmaan_ijkl.jl
index f1d86948b..d0b5d8476 100644
--- a/src/ADNLPProblems/dixmaan_ijkl.jl
+++ b/src/ADNLPProblems/dixmaan_ijkl.jl
@@ -9,9 +9,10 @@ function dixmaani(;
   δ = 125 // 1000,
   kwargs...,
 ) where {T}
-  (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("dixmaani", n_orig, n)
   function f(x; n = length(x), α = α, β = β, γ = γ, δ = δ)
     return 1 +
            sum((i // n)^2 * α * x[i]^2 for i = 1:n) +
diff --git a/src/ADNLPProblems/dixmaan_mnop.jl b/src/ADNLPProblems/dixmaan_mnop.jl
index e62e449e4..627353c22 100644
--- a/src/ADNLPProblems/dixmaan_mnop.jl
+++ b/src/ADNLPProblems/dixmaan_mnop.jl
@@ -9,9 +9,10 @@ function dixmaanm(;
   δ = 125 // 1000,
   kwargs...,
 ) where {T}
-  (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("dixmaanm", n_orig, n)
   function f(x; n = length(x), α = α, β = β, γ = γ, δ = δ)
     return 1 +
            sum((i // n)^2 * α * x[i]^2 for i = 1:n) +
diff --git a/src/ADNLPProblems/elec.jl b/src/ADNLPProblems/elec.jl
index 058d7f771..11a6a30e3 100644
--- a/src/ADNLPProblems/elec.jl
+++ b/src/ADNLPProblems/elec.jl
@@ -1,36 +1,39 @@
 export elec
 
 function elec(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  n = max(2, div(n, 3))
+  n_orig = n
+  m = max(2, div(n_orig, 3))
+  n = 3 * m
+  @adjust_nvar_warn("elec", n_orig, n)
   # Define the objective function to minimize
   function f(x; n = n)
     return sum(
       sum(
-        1 / sqrt((x[j] - x[i])^2 + (x[n + j] - x[n + i])^2 + (x[2n + j] - x[2n + i])^2) for
-        j = (i + 1):n
-      ) for i = 1:(n - 1)
+        1 / sqrt((x[j] - x[i])^2 + (x[m + j] - x[m + i])^2 + (x[2m + j] - x[2m + i])^2) for
+        j = (i + 1):m
+      ) for i = 1:(m - 1)
     )
   end
 
   # Define the constraints on these points (sum of the square of the coordinates = 1)
   function c!(cx, x; n = n)
-    for k = 1:n
-      cx[k] = x[k]^2 + x[n + k]^2 + x[2n + k]^2
+    for k = 1:m
+      cx[k] = x[k]^2 + x[m + k]^2 + x[2m + k]^2
     end
     return cx
   end
 
   # bounds on the constraints
-  lcon = ucon = ones(T, n)
+  lcon = ucon = ones(T, m)
 
   # building a feasible x0
-  range0 = T[i / n for i = 1:n]
+  range0 = T[i / m for i = 1:m]
 
   θ0 = 2π .* range0
   ϕ0 = π .* range0
-  xini = T[sin(θ0[i]) * cos(ϕ0[i]) for i = 1:n] # x coordinate
-  yini = T[sin(θ0[i]) * sin(ϕ0[i]) for i = 1:n] # y coordinate 
-  zini = T[cos(θ0[i]) for i = 1:n]            # z coordinate
+  xini = T[sin(θ0[i]) * cos(ϕ0[i]) for i = 1:m] # x coordinate
+  yini = T[sin(θ0[i]) * sin(ϕ0[i]) for i = 1:m] # y coordinate 
+  zini = T[cos(θ0[i]) for i = 1:m]            # z coordinate
   x0 = [xini; yini; zini]
 
   return ADNLPModels.ADNLPModel!(f, x0, c!, lcon, ucon, name = "elec"; kwargs...)
diff --git a/src/ADNLPProblems/fminsrf2.jl b/src/ADNLPProblems/fminsrf2.jl
index c7fe806ea..eae7709ba 100644
--- a/src/ADNLPProblems/fminsrf2.jl
+++ b/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
diff --git a/src/ADNLPProblems/hovercraft1d.jl b/src/ADNLPProblems/hovercraft1d.jl
index e2e1f30c4..2dfab9bb9 100644
--- a/src/ADNLPProblems/hovercraft1d.jl
+++ b/src/ADNLPProblems/hovercraft1d.jl
@@ -11,7 +11,10 @@ function hovercraft1d(
   type::Type{T} = Float64,
   kwargs...,
 ) where {T}
-  N = div(n, 3)
+  n_orig = n
+  N = div(n_orig, 3)
+  n = 3 * N - 1
+  @adjust_nvar_warn("hovercraft1d", n_orig, n)
   function f(y; N = N)
     @views x, v, u = y[1:N], y[(N + 1):(2 * N)], y[(2 * N + 1):end]
     return 1 // 2 * sum(u .^ 2)
@@ -72,7 +75,10 @@ function hovercraft1d(
   type::Type{T} = Float64,
   kwargs...,
 ) where {T}
-  N = div(n, 3)
+  n_orig = n
+  N = div(n_orig, 3)
+  n = 3 * N - 1
+  @adjust_nvar_warn("hovercraft1d", n_orig, n)
   function F!(r, y; N = N)
     @views x, v, u = y[1:N], y[(N + 1):(2 * N)], y[(2 * N + 1):end]
     r .= u
diff --git a/src/ADNLPProblems/marine.jl b/src/ADNLPProblems/marine.jl
index aa96414d9..0ddcdefc0 100644
--- a/src/ADNLPProblems/marine.jl
+++ b/src/ADNLPProblems/marine.jl
@@ -1,12 +1,15 @@
 export marine
 
 function marine(; n::Int = default_nvar, nc::Int = 1, type::Type{T} = Float64, kwargs...) where {T}
+  n_orig = n
   nc = max(min(nc, 4), 1) # number of collocation points 
   ne = 8 # number of differential equations
   nm = 21 # number of measurements
 
   n = max(n, 3 * ne * nc + ne + 2 * ne)
   nh = Int(round((n - 2 * ne + 1) / (3 * ne * nc + ne))) # number of partition intervals
+  n = 8 + 7 + nh * (8 + 3 * 8 * nc)
+  @adjust_nvar_warn("marine", n_orig, n)
 
   # roots of k-th degree Legendre polynomial
   rho = if nc == 1
diff --git a/src/ADNLPProblems/powellsg.jl b/src/ADNLPProblems/powellsg.jl
index 38ac7db65..e95cc1d55 100644
--- a/src/ADNLPProblems/powellsg.jl
+++ b/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]
diff --git a/src/ADNLPProblems/robotarm.jl b/src/ADNLPProblems/robotarm.jl
index a1264eebd..e1f0d23db 100644
--- a/src/ADNLPProblems/robotarm.jl
+++ b/src/ADNLPProblems/robotarm.jl
@@ -10,8 +10,11 @@ export robotarm
 #  classification OOR2-AN-V-V
 
 function robotarm(; n::Int = default_nvar, L = 4.5, type::Type{T} = Float64, kwargs...) where {T}
-  N = max(2, div(n, 9))
+  n_orig = n
+  N = max(2, div(n_orig, 9))
   n = N + 1
+  nvars = 9 * n + 1
+  @adjust_nvar_warn("robotarm", n_orig, nvars)
   L = T(L)
 
   # x : vector of variables, of the form : [ρ(t=t1); ρ(t=t2); ... ρ(t=tf), θ(t=t1), ..., then ρ_dot, ..., then ρ_acc, .. ϕ_acc, tf]
diff --git a/src/ADNLPProblems/spmsrtls.jl b/src/ADNLPProblems/spmsrtls.jl
index d55161026..8cbc967cf 100644
--- a/src/ADNLPProblems/spmsrtls.jl
+++ b/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]
 
diff --git a/src/ADNLPProblems/srosenbr.jl b/src/ADNLPProblems/srosenbr.jl
index 451d0a6e7..8c51d9e64 100644
--- a/src/ADNLPProblems/srosenbr.jl
+++ b/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
diff --git a/src/ADNLPProblems/structural.jl b/src/ADNLPProblems/structural.jl
index 0793f199b..f39948fdf 100644
--- a/src/ADNLPProblems/structural.jl
+++ b/src/ADNLPProblems/structural.jl
@@ -1,7 +1,8 @@
 export structural
 
 function structural(args...; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T}
-  n = max(n, 100)
+  n_orig = n
+  n = max(n_orig, 100)
 
   sub2ind(shape, a, b) = LinearIndices(shape)[CartesianIndex.(a, b)]
   Nx = min(Int(round(n^(1 / 3))), 6)
@@ -23,6 +24,9 @@ function structural(args...; n::Int = default_nvar, type::Type{T} = Float64, kwa
 
   M = Int(N * (N - 1) / 2)  # number of edges
 
+  nvars = 2 * M
+  @adjust_nvar_warn("structural", n_orig, nvars)
+
   # EDGES: columns are the indices of the nodes at either end
   edges = Array{Int}(zeros(M, 2))
 
diff --git a/src/ADNLPProblems/watson.jl b/src/ADNLPProblems/watson.jl
index 56e1f2757..cf75edc30 100644
--- a/src/ADNLPProblems/watson.jl
+++ b/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
diff --git a/src/ADNLPProblems/woods.jl b/src/ADNLPProblems/woods.jl
index 426166561..575630098 100644
--- a/src/ADNLPProblems/woods.jl
+++ b/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 +
diff --git a/src/OptimizationProblems.jl b/src/OptimizationProblems.jl
index 7ea7b664e..1d5a5540d 100644
--- a/src/OptimizationProblems.jl
+++ b/src/OptimizationProblems.jl
@@ -2,6 +2,21 @@ module OptimizationProblems
 
 using DataFrames
 
+"""
+    @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)
+  return quote
+    local _n_orig = $(esc(n_orig))
+    local _n = $(esc(n))
+    (_n == _n_orig) || @warn(
+      string($(esc(problem_name)), ": number of variables adjusted from ", _n_orig, " to ", _n)
+    )
+  end
+end
+
 include("ADNLPProblems/ADNLPProblems.jl")
 include("PureJuMP/PureJuMP.jl")
 
diff --git a/src/PureJuMP/NZF1.jl b/src/PureJuMP/NZF1.jl
index f0eb746c5..886bfc857 100644
--- a/src/PureJuMP/NZF1.jl
+++ b/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)
 
diff --git a/src/PureJuMP/PureJuMP.jl b/src/PureJuMP/PureJuMP.jl
index a855ee252..ae7418db0 100644
--- a/src/PureJuMP/PureJuMP.jl
+++ b/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))
diff --git a/src/PureJuMP/bearing.jl b/src/PureJuMP/bearing.jl
index 86a3d47bc..442de6250 100644
--- a/src/PureJuMP/bearing.jl
+++ b/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
 
diff --git a/src/PureJuMP/broydn7d.jl b/src/PureJuMP/broydn7d.jl
index 5778efbcc..eb24cfc2f 100644
--- a/src/PureJuMP/broydn7d.jl
+++ b/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()
 
diff --git a/src/PureJuMP/catenary.jl b/src/PureJuMP/catenary.jl
index 4da81b57f..6be4300b3 100644
--- a/src/PureJuMP/catenary.jl
+++ b/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
 
diff --git a/src/PureJuMP/catmix.jl b/src/PureJuMP/catmix.jl
index 580f2db7c..ca118b4d8 100644
--- a/src/PureJuMP/catmix.jl
+++ b/src/PureJuMP/catmix.jl
@@ -6,8 +6,11 @@
 export catmix
 
 function catmix(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   ne = 2
   nc = 3
+  n = n_orig
+  @adjust_nvar_warn("catmix", n_orig, 23 * n + 2)
 
   tf = 1
   h = tf / n   # Final time
diff --git a/src/PureJuMP/chain.jl b/src/PureJuMP/chain.jl
index befb5f6cd..dddc3128d 100644
--- a/src/PureJuMP/chain.jl
+++ b/src/PureJuMP/chain.jl
@@ -13,7 +13,10 @@
 export chain
 
 function chain(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   nh = max(2, div(n - 4, 4))
+  n = 4 * nh + 4
+  @adjust_nvar_warn("chain", n_orig, n)
 
   L = 4
   a = 1
diff --git a/src/PureJuMP/chainwoo.jl b/src/PureJuMP/chainwoo.jl
index 0fc8cb893..7d0112043 100644
--- a/src/PureJuMP/chainwoo.jl
+++ b/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()
 
diff --git a/src/PureJuMP/channel.jl b/src/PureJuMP/channel.jl
index cce634660..8054a121d 100644
--- a/src/PureJuMP/channel.jl
+++ b/src/PureJuMP/channel.jl
@@ -12,7 +12,10 @@
 export channel
 
 function channel(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   nh = max(2, div(n, 8))
+  n = 8 * nh
+  @adjust_nvar_warn("channel", n_orig, n)
 
   nc = 4
   nd = 4
diff --git a/src/PureJuMP/clnlbeam.jl b/src/PureJuMP/clnlbeam.jl
index 3849c7fe8..6cafb82cb 100644
--- a/src/PureJuMP/clnlbeam.jl
+++ b/src/PureJuMP/clnlbeam.jl
@@ -14,7 +14,10 @@ export clnlbeam
 
 "The clnlbeam problem in size `n`"
 function clnlbeam(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   N = div(n - 3, 3)
+  n = 3 * N + 3
+  @adjust_nvar_warn("clnlbeam", n_orig, n)
   h = 1 / N
   alpha = 350
   model = Model()
diff --git a/src/PureJuMP/clplatea.jl b/src/PureJuMP/clplatea.jl
index 011e0db9a..a12947dce 100644
--- a/src/PureJuMP/clplatea.jl
+++ b/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()
 
diff --git a/src/PureJuMP/clplateb.jl b/src/PureJuMP/clplateb.jl
index 575e9fe5d..ec6315f17 100644
--- a/src/PureJuMP/clplateb.jl
+++ b/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()
 
diff --git a/src/PureJuMP/clplatec.jl b/src/PureJuMP/clplatec.jl
index 274feda44..e85f9686d 100644
--- a/src/PureJuMP/clplatec.jl
+++ b/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()
 
diff --git a/src/PureJuMP/dixmaan_efgh.jl b/src/PureJuMP/dixmaan_efgh.jl
index 39e2c5976..f25edac80 100644
--- a/src/PureJuMP/dixmaan_efgh.jl
+++ b/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("dixmaane", n_orig, n)
 
   nlp = Model()
 
diff --git a/src/PureJuMP/dixmaan_ijkl.jl b/src/PureJuMP/dixmaan_ijkl.jl
index 11f291714..e4f22ed05 100644
--- a/src/PureJuMP/dixmaan_ijkl.jl
+++ b/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("dixmaani", n_orig, n)
 
   nlp = Model()
 
diff --git a/src/PureJuMP/dixmaan_mnop.jl b/src/PureJuMP/dixmaan_mnop.jl
index c4537bcd5..0c676cc59 100644
--- a/src/PureJuMP/dixmaan_mnop.jl
+++ b/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("dixmaanm", n_orig, n)
 
   nlp = Model()
 
diff --git a/src/PureJuMP/elec.jl b/src/PureJuMP/elec.jl
index a1f0ca3db..0d7f690ae 100644
--- a/src/PureJuMP/elec.jl
+++ b/src/PureJuMP/elec.jl
@@ -11,33 +11,36 @@
 export elec
 
 function elec(args...; n::Int = default_nvar, kwargs...)
-  n = max(2, div(n, 3))
+  n_orig = n
+  m = max(2, div(n_orig, 3))
+  n = 3 * m
+  @adjust_nvar_warn("elec", n_orig, n)
 
   nlp = Model()
 
-  range0 = [i / n for i = 1:n]
+  range0 = [i / m for i = 1:m]
 
   θ0 = 2π .* range0
   ϕ0 = π .* range0
-  xini = [sin(θ0[i]) * cos(ϕ0[i]) for i = 1:n] # x coordinate
-  yini = [sin(θ0[i]) * sin(ϕ0[i]) for i = 1:n] # y coordinate 
-  zini = [cos(θ0[i]) for i = 1:n]            # z coordinate
+  xini = [sin(θ0[i]) * cos(ϕ0[i]) for i = 1:m] # x coordinate
+  yini = [sin(θ0[i]) * sin(ϕ0[i]) for i = 1:m] # y coordinate 
+  zini = [cos(θ0[i]) for i = 1:m]            # z coordinate
   x0 = [xini; yini; zini]
 
-  @variable(nlp, x[i = 1:(3n)], start = x0[i])
+  @variable(nlp, x[i = 1:(3m)], start = x0[i])
 
   @objective(
     nlp,
     Min,
     sum(
       sum(
-        1 / sqrt((x[j] - x[i])^2 + (x[n + j] - x[n + i])^2 + (x[2n + j] - x[2n + i])^2) for
-        j = (i + 1):n
-      ) for i = 1:(n - 1)
+        1 / sqrt((x[j] - x[i])^2 + (x[m + j] - x[m + i])^2 + (x[2m + j] - x[2m + i])^2) for
+        j = (i + 1):m
+      ) for i = 1:(m - 1)
     )
   )
 
-  @constraint(nlp, [k = 1:n], x[k]^2 + x[n + k]^2 + x[2n + k]^2 == 1)
+  @constraint(nlp, [k = 1:m], x[k]^2 + x[m + k]^2 + x[2m + k]^2 == 1)
 
   return nlp
 end
diff --git a/src/PureJuMP/fminsrf2.jl b/src/PureJuMP/fminsrf2.jl
index b6acb5a8f..d73c4c630 100644
--- a/src/PureJuMP/fminsrf2.jl
+++ b/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
diff --git a/src/PureJuMP/gasoil.jl b/src/PureJuMP/gasoil.jl
index c39531f3c..577fa93f8 100644
--- a/src/PureJuMP/gasoil.jl
+++ b/src/PureJuMP/gasoil.jl
@@ -8,7 +8,10 @@
 export gasoil
 
 function gasoil(; n::Int = default_nvar, kwargs...)
+  n_orig = n
   nc = 4        # number of collocation points
+  n = n_orig
+  @adjust_nvar_warn("gasoil", n_orig, 26 * n + 3)
   ne = 2        # number of differential equations
   np = 3        # number of ODE parameters
   nm = 21       # number of measurements
diff --git a/src/PureJuMP/glider.jl b/src/PureJuMP/glider.jl
index 9d217b716..af1d0c3cf 100644
--- a/src/PureJuMP/glider.jl
+++ b/src/PureJuMP/glider.jl
@@ -8,7 +8,10 @@
 export glider
 
 function glider(; n::Int = default_nvar, kwargs...)
+  n_orig = n
   # Design parameters
+  n = n_orig
+  @adjust_nvar_warn("glider", n_orig, 5 * n + 6)
   x_0 = 0.0
   y_0 = 1000.0
   y_f = 900.0
diff --git a/src/PureJuMP/hovercraft1d.jl b/src/PureJuMP/hovercraft1d.jl
index 0ddce6c3f..f98eacb56 100644
--- a/src/PureJuMP/hovercraft1d.jl
+++ b/src/PureJuMP/hovercraft1d.jl
@@ -6,9 +6,12 @@
 export hovercraft1d
 
 function hovercraft1d(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   nlp = Model()
 
-  T = div(n, 3)           # length of time horizon
+  T = div(n_orig, 3)           # length of time horizon
+  n = 3 * T - 1
+  @adjust_nvar_warn("hovercraft1d", n_orig, n)
   @variable(nlp, x[1:T])    # resulting position
   @variable(nlp, v[1:T])    # resulting velocity
   @variable(nlp, u[1:(T - 1)])  # thruster input
diff --git a/src/PureJuMP/marine.jl b/src/PureJuMP/marine.jl
index afd07b22d..462d775a7 100644
--- a/src/PureJuMP/marine.jl
+++ b/src/PureJuMP/marine.jl
@@ -18,14 +18,17 @@
 export marine
 
 function marine(args...; n::Int = default_nvar, nc::Int = 1, kwargs...)
+  n_orig = n
   nlp = Model()
 
   nc = max(min(nc, 4), 1) # number of collocation points 
   ne = 8 # number of differential equations
   nm = 21 # number of measurements
 
-  n = max(n, 3 * ne * nc + ne + 2 * ne)
+  n = max(n_orig, 3 * ne * nc + ne + 2 * ne)
   nh = Int(round((n - 2 * ne + 1) / (3 * ne * nc + ne))) # number of partition intervals
+  n = 8 + 7 + nh * (8 + 3 * 8 * nc)
+  @adjust_nvar_warn("marine", n_orig, n)
 
   # roots of k-th degree Legendre polynomial
   rho = if nc == 1
diff --git a/src/PureJuMP/methanol.jl b/src/PureJuMP/methanol.jl
index f27f055ca..2e70e2309 100644
--- a/src/PureJuMP/methanol.jl
+++ b/src/PureJuMP/methanol.jl
@@ -8,6 +8,7 @@
 export methanol
 
 function methanol(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   ne = 3
   np = 5
   nc = 3
@@ -35,6 +36,8 @@ function methanol(args...; n::Int = default_nvar, kwargs...)
     1.122,
   ]
   tf = tau[nm]                   # ODEs defined in [0,tf]
+  n = n_orig
+  @adjust_nvar_warn("methanol", n_orig, 30 * n + 5)
   h = tf / n                    # uniform interval length
   t = [(i-1)*h for i = 1:(n + 1)]  # partition
   fact = [factorial(k) for k = 0:nc]
diff --git a/src/PureJuMP/minsurf.jl b/src/PureJuMP/minsurf.jl
index a50e97f15..f755d60bf 100644
--- a/src/PureJuMP/minsurf.jl
+++ b/src/PureJuMP/minsurf.jl
@@ -12,10 +12,13 @@
 export minsurf
 
 function minsurf(args...; n = default_nvar, kwargs...)
+  n_orig = n
   # number of variables is (nx + 2) x (ny + 2)
   if !((:nx in keys(kwargs)) & (:ny in keys(kwargs)))
-    nx, ny = Int(round(sqrt(max(1, n - 2)))), Int(round(sqrt(max(1, n - 2))))
+    nx, ny = Int(round(sqrt(max(1, n_orig - 2)))), Int(round(sqrt(max(1, n_orig - 2))))
   end
+  n = (nx + 2) * (ny + 2)
+  @adjust_nvar_warn("minsurf", n_orig, n)
   x_mesh = LinRange(0, 1, nx + 2) # coordinates of the mesh points x
 
   v0 = zeros(nx + 2, ny + 2) # Surface matrix initialization
diff --git a/src/PureJuMP/pinene.jl b/src/PureJuMP/pinene.jl
index 347fa83d5..314813113 100644
--- a/src/PureJuMP/pinene.jl
+++ b/src/PureJuMP/pinene.jl
@@ -13,6 +13,7 @@
 export pinene
 
 function pinene(; n::Int = default_nvar, kwargs...)
+  n_orig = n
   nc = 3        # number of collocation points
   ne = 5        # number of differential equations
   np = 5        # number of ODE parameters
@@ -25,6 +26,8 @@ function pinene(; n::Int = default_nvar, kwargs...)
   # times at which observations made
   tau = [1230.0, 3060.0, 4920.0, 7800.0, 10680.0, 15030.0, 22620.0, 36420.0]
   tf = tau[nm]                       # ODEs defined in [0,tf]
+  n = n_orig
+  @adjust_nvar_warn("pinene", n_orig, 50 * n + 5)
   h = tf / n                        # uniform interval length
   t = [(i-1)*h for i = 1:(n + 1)]      # partition
 
diff --git a/src/PureJuMP/powellsg.jl b/src/PureJuMP/powellsg.jl
index bcb9f7deb..e7ecdd2a5 100644
--- a/src/PureJuMP/powellsg.jl
+++ b/src/PureJuMP/powellsg.jl
@@ -37,8 +37,9 @@ export powellsg
 
 "The extended Powell singular problem in size 'n' "
 function powellsg(args...; n::Int = default_nvar, kwargs...)
-  (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)
 
   x0 = zeros(n)
   x0[4 * (collect(1:div(n, 4))) .- 3] .= 3.0
diff --git a/src/PureJuMP/robotarm.jl b/src/PureJuMP/robotarm.jl
index b6252bd90..950187b4a 100644
--- a/src/PureJuMP/robotarm.jl
+++ b/src/PureJuMP/robotarm.jl
@@ -14,8 +14,11 @@
 export robotarm
 
 function robotarm(; n::Int = default_nvar, L = 4.5, kwargs...)
-  N = max(2, div(n, 9))
+  n_orig = n
+  N = max(2, div(n_orig, 9))
   n = N + 1
+  nvars = 9 * n + 1
+  @adjust_nvar_warn("robotarm", n_orig, nvars)
 
   nlp = Model()
 
diff --git a/src/PureJuMP/rocket.jl b/src/PureJuMP/rocket.jl
index 56ab5a499..c824cdeca 100644
--- a/src/PureJuMP/rocket.jl
+++ b/src/PureJuMP/rocket.jl
@@ -7,7 +7,10 @@
 export rocket
 
 function rocket(; n::Int = default_nvar, kwargs...)
+  n_orig = n
   h_0 = 1.0
+  nvars = 4 * n_orig + 5
+  @adjust_nvar_warn("rocket", n_orig, nvars)
   v_0 = 0.0
   m_0 = 1.0
   g_0 = 1.0
diff --git a/src/PureJuMP/spmsrtls.jl b/src/PureJuMP/spmsrtls.jl
index 98ae91cc2..4e08dd863 100644
--- a/src/PureJuMP/spmsrtls.jl
+++ b/src/PureJuMP/spmsrtls.jl
@@ -21,8 +21,10 @@
 export spmsrtls
 
 function spmsrtls(args...; n::Int = default_nvar, kwargs...)
+  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 = [p[i] / 5 for i = 1:n]
 
diff --git a/src/PureJuMP/srosenbr.jl b/src/PureJuMP/srosenbr.jl
index fc971e61d..1381f38c0 100644
--- a/src/PureJuMP/srosenbr.jl
+++ b/src/PureJuMP/srosenbr.jl
@@ -21,8 +21,9 @@ export srosenbr
 
 "The separable extension of Rosenbrock's function 'n' "
 function srosenbr(args...; n::Int = default_nvar, kwargs...)
-  (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)
 
   x0 = ones(n)
   x0[2 * (collect(1:div(n, 2))) .- 1] .= -1.2
diff --git a/src/PureJuMP/steering.jl b/src/PureJuMP/steering.jl
index 353be640b..9a72508a0 100644
--- a/src/PureJuMP/steering.jl
+++ b/src/PureJuMP/steering.jl
@@ -7,7 +7,10 @@
 export steering
 
 function steering(; n::Int = default_nvar, kwargs...)
+  n_orig = n
   a = 100.0  # Magnitude of force.
+  nvars = 5 * n_orig + 6
+  @adjust_nvar_warn("steering", n_orig, nvars)
   # Bounds on the control
   u_min, u_max = -pi/2.0, pi/2.0
   xs = zeros(4)
diff --git a/src/PureJuMP/structural.jl b/src/PureJuMP/structural.jl
index 65ba54d47..4bfbb8184 100644
--- a/src/PureJuMP/structural.jl
+++ b/src/PureJuMP/structural.jl
@@ -6,7 +6,8 @@
 export structural
 
 function structural(args...; n::Int = default_nvar, kwargs...)
-  n = max(n, 100)
+  n_orig = n
+  n = max(n_orig, 100)
 
   sub2ind(shape, a, b) = LinearIndices(shape)[CartesianIndex.(a, b)]
   Nx = min(Int(round(n^(1 / 3))), 6)
@@ -29,6 +30,9 @@ function structural(args...; n::Int = default_nvar, kwargs...)
 
   M = Int(N * (N - 1) / 2)  # number of edges
 
+  nvars = 2 * M
+  @adjust_nvar_warn("structural", n_orig, nvars)
+
   # EDGES: columns are the indices of the nodes at either end
   edges = Array{Int}(zeros(M, 2))
 
diff --git a/src/PureJuMP/torsion.jl b/src/PureJuMP/torsion.jl
index 926d1e456..8fee5c164 100644
--- a/src/PureJuMP/torsion.jl
+++ b/src/PureJuMP/torsion.jl
@@ -7,9 +7,12 @@ export torsion
 
 function torsion(args...; n = default_nvar, kwargs...)
   # number of variables is (nx + 1) x (ny + 1)
+  n_orig = n
   if !((:nx in keys(kwargs)) & (:ny in keys(kwargs)))
-    nx, ny = Int(round(sqrt(max(1, n - 2)))), Int(round(sqrt(max(1, n - 2))))
+    nx, ny = Int(round(sqrt(max(1, n_orig - 2)))), Int(round(sqrt(max(1, n_orig - 2))))
   end
+  nvars = (nx + 2) * (ny + 2)
+  @adjust_nvar_warn("torsion", n_orig, nvars)
   c = 5.0
   hx = 1.0 / (nx + 1.0)    # grid spacing
   hy = 1.0 / (ny + 1.0)    # grid spacing
diff --git a/src/PureJuMP/watson.jl b/src/PureJuMP/watson.jl
index 6dc9def6e..e9f6a582c 100644
--- a/src/PureJuMP/watson.jl
+++ b/src/PureJuMP/watson.jl
@@ -17,7 +17,9 @@
 export watson
 
 function watson(args...; n::Int = default_nvar, kwargs...)
+  n_orig = n
   n = min(max(n, 2), 31)
+  @adjust_nvar_warn("watson", n_orig, n)
   m = 31
 
   nlp = Model()
diff --git a/src/PureJuMP/woods.jl b/src/PureJuMP/woods.jl
index 8127b51ab..c9f5b8938 100644
--- a/src/PureJuMP/woods.jl
+++ b/src/PureJuMP/woods.jl
@@ -39,8 +39,9 @@ export woods
 
 "The extended Woods problem `n` "
 function woods(args...; n::Int = default_nvar, kwargs...)
-  (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)
 
   nlp = Model()
 
diff --git a/test/test-defined-problems.jl b/test/test-defined-problems.jl
index 0007a346b..4db6dbdd0 100644
--- a/test/test-defined-problems.jl
+++ b/test/test-defined-problems.jl
@@ -22,6 +22,34 @@ probes = @sync begin
 end
 @info "PureJuMP missing per worker" probes
 
+@testset "Adjusted dimension warnings" begin
+  var_probs = OptimizationProblems.meta[OptimizationProblems.meta.variable_nvar, :name]
+  @test !isempty(var_probs)
+
+  for prob_name in var_probs
+    prob_sym = Symbol(prob_name)
+    
+    get_nvar_func = getfield(OptimizationProblems, Symbol("get_", prob_name, "_nvar"))
+
+    for n in (50, 100, 150)
+      n_adjusted = get_nvar_func(; n = n)
+      n_adjusted == n && continue  # Skip if no adjustment for this n
+
+      msg_re = Regex("number of variables adjusted from $(n) to $(n_adjusted)")
+
+      for mod in (ADNLPProblems, PureJuMP)
+        isdefined(mod, prob_sym) || continue
+
+        constructor = getfield(mod, prob_sym)
+
+            @test_logs (:warn, msg_re) constructor(; n = n)
+      end
+      
+      break
+    end
+  end
+end
+
 @test setdiff(union(names(ADNLPProblems), list_problems_not_ADNLPProblems), list_problems) ==
       [:ADNLPProblems]
 @test setdiff(union(names(PureJuMP), list_problems_not_PureJuMP), list_problems) == [:PureJuMP]