Vectorized matrix unknown

src/ADNLPProblems/clplatea.jl

@@ -1,20 +1,29 @@
 export clplatea
 
-function clplatea(; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs...) where {T}
+function clplatea(;
+  n::Int = default_nvar,
+  type::Val{T} = Val(Float64),
+  wght = -0.1,
+  kwargs...,
+) where {T}
   p = floor(Int, sqrt(n))
   p * p != n && @warn("clplatea: number of variables adjusted from $n down to $(p*p)")
   n = p * p
-  hp2 = 0.5 * p^2
+  hp2 = T(1 / 2) * p^2
   function f(x)
-    return (wght * x[p, p]) +
+    return (T(wght) * x[p + (p - 1) * p]) +
            sum(
-             sum(0.5 * (x[i, j] - x[i, j - 1])^2 + hp2 * (x[i, j] - x[i, j - 1])^4 for j = 2:p) for
-             i = 2:p
+             sum(
+               T(1 / 2) * (x[i + (j - 1) * p] - x[i + (j - 2) * p])^2 +
+               hp2 * (x[i + (j - 1) * p] - x[i + (j - 2) * p])^4 for j = 2:p
+             ) for i = 2:p
            ) +
-           sum(0.5 * (x[2, j])^2 + hp2 * (x[2, j])^4 for j = 2:p) +
+           sum(T(1 / 2) * (x[2 + (j - 1) * p])^2 + hp2 * (x[2 + (j - 1) * p])^4 for j = 2:p) +
            sum(
-             sum(0.5 * (x[i, j] - x[i - 1, j])^2 + hp2 * (x[i, j] - x[i - 1, j])^4 for j = 2:p) for
-             i = 3:p
+             sum(
+               T(1 / 2) * (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^2 +
+               hp2 * (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^4 for j = 2:p
+             ) for i = 3:p
            )
   end
   x0 = zeros(T, n)

src/ADNLPProblems/clplateb.jl

@@ -1,22 +1,30 @@
 export clplateb
 
-function clplateb(; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs...) where {T}
+function clplateb(;
+  n::Int = default_nvar,
+  type::Val{T} = Val(Float64),
+  wght = -0.1,
+  kwargs...,
+) where {T}
   p = floor(Int, sqrt(n))
   p * p != n && @warn("clplateb: number of variables adjusted from $n down to $(p*p)")
   n = p * p
-  hp2 = 0.5 * p^2
-  wght = -T(0.1)
-  disw = wght / (p - 1)
+  hp2 = T(1 / 2) * p^2
+  disw = T(wght) / (p - 1)
   function f(x)
-    return sum(disw * x[p, j] for j = 1:p) +
+    return sum(disw * x[p + (j - 1) * p] for j = 1:p) +
            sum(
-             sum(0.5 * (x[i, j] - x[i, j - 1])^2 + hp2 * (x[i, j] - x[i, j - 1])^4 for j = 2:p) for
-             i = 2:p
+             sum(
+               T(1 / 2) * (x[i + (j - 1) * p] - x[i + (j - 2) * p])^2 +
+               hp2 * (x[i + (j - 1) * p] - x[i + (j - 2) * p])^4 for j = 2:p
+             ) for i = 2:p
            ) +
-           sum(0.5 * (x[2, j])^2 + hp2 * (x[2, j])^4 for j = 2:p) +
+           sum(T(1 / 2) * (x[2 + (j - 1) * p])^2 + hp2 * (x[2 + (j - 1) * p])^4 for j = 2:p) +
            sum(
-             sum(0.5 * (x[i, j] - x[i - 1, j])^2 + hp2 * (x[i, j] - x[i - 1, j])^4 for j = 2:p) for
-             i = 3:p
+             sum(
+               T(1 / 2) * (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^2 +
+               hp2 * (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^4 for j = 2:p
+             ) for i = 3:p
            )
   end
   x0 = zeros(T, n)

src/ADNLPProblems/clplatec.jl

@@ -1,27 +1,36 @@
 export clplatec
 
-function clplatec(; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs...) where {T}
+function clplatec(;
+  n::Int = default_nvar,
+  type::Val{T} = Val(Float64),
+  wght = -0.1,
+  r = 0.99,
+  l = 0.01,
+  kwargs...,
+) where {T}
   p = floor(Int, sqrt(n))
   p * p != n && @warn("clplatec: number of variables adjusted from $n down to $(p*p)")
   n = p * p
-  wght = -T(0.1)
-  r = T(0.99)
-  l = T(0.01)
-  hp2 = 0.5 * p^2
-  wr = wght * r
-  wl = wght * l
+
+  hp2 = T(1 / 2) * p^2
+  wr = T(wght * r)
+  wl = T(wght * l)
   function f(x)
-    return wr * x[p, p] +
-           wl * x[p, 1] +
+    return wr * x[p + (p - 1) * p] +
+           wl * x[p] +
            sum(
-             sum(hp2 * (x[i, j] - x[i, j - 1])^2 + (x[i, j] - x[i, j - 1])^4 for j = 2:p) for
-             i = 2:p
+             sum(
+               hp2 * (x[i + (j - 1) * p] - x[i + (j - 2) * p])^2 +
+               (x[i + (j - 1) * p] - x[i + (j - 2) * p])^4 for j = 2:p
+             ) for i = 2:p
            ) +
            sum(
-             sum(0.5 * (x[i, j] - x[i - 1, j])^2 + (x[i, j] - x[i - 1, j])^4 for j = 2:p) for
-             i = 3:p
+             sum(
+               T(1 / 2) * (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^2 +
+               (x[i + (j - 1) * p] - x[i - 1 + (j - 1) * p])^4 for j = 2:p
+             ) for i = 3:p
            ) +
-           sum(0.5 * (x[2, j])^2 + (x[2, j])^4 for j = 2:p)
+           sum(T(1 / 2) * (x[2 + (j - 1) * p])^2 + (x[2 + (j - 1) * p])^4 for j = 2:p)
   end
   x0 = zeros(T, n)
   return ADNLPModels.ADNLPModel(f, x0, name = "clplatec"; kwargs...)

src/ADNLPProblems/fminsrf2.jl

@@ -8,9 +8,9 @@ function fminsrf2(; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs..
   p * p != n && @warn("fminsrf2: number of variables adjusted from $n down to $(p*p)")
   n = p * p
 
-  h00 = 1.0
-  slopej = 4.0
-  slopei = 8.0
+  h00 = one(T)
+  slopej = T(4)
+  slopei = T(8)
 
   scale = (p - 1)^2
 
@@ -20,23 +20,26 @@ function fminsrf2(; n::Int = default_nvar, type::Val{T} = Val(Float64), kwargs..
   h10 = h00 + slopei
   mid = div(p, 2)
 
-  x0 = zeros(p, p)
+  x0 = zeros(T, p * p)
   I = 2:(p - 1)
   J = 1:p
 
-  x0[I, 1] .= (I .- 1) * ston .+ h01
-  x0[I, p] .= (I .- 1) * ston .+ h00
-  x0[1, J] .= (J .- 1) * wtoe .+ h00
-  x0[p, J] .= (J .- 1) * wtoe .+ h10
+  x0[I] .= (I .- 1) * ston .+ h01
+  x0[I .+ (p .- 1) * p] .= (I .- 1) * ston .+ h00
+  x0[1 .+ (J .- 1) * p] .= (J .- 1) * wtoe .+ h00
+  x0[p .+ (J .- 1) * p] .= (J .- 1) * wtoe .+ h10
   function f(x)
     n = length(x)
     return sum(
       sum(
-        100.0 * sqrt(
-          0.5 * (p - 1)^2 * ((x[i, j] - x[i + 1, j + 1])^2 + (x[i + 1, j] - x[i, j + 1])^2) + 1.0,
+        100 * sqrt(
+          T(1 / 2) *
+          (p - 1)^2 *
+          ((x[i + (j - 1) * p] - x[i + 1 + j * p])^2 + (x[i + 1 + (j - 1) * p] - x[i + j * p])^2) +
+          1,
         ) / scale for i = 1:(p - 1)
       ) for j = 1:(p - 1)
-    ) + 100.0 * (x[mid, mid])^2 / n
+    ) + 100 * (x[mid + (mid - 1) * p])^2 / n
   end
   return ADNLPModels.ADNLPModel(f, x0, name = "fminsrf2"; kwargs...)
 end

test/runtests.jl

@@ -43,7 +43,6 @@ for prob in names(PureJuMP)
   model = prob_fn(ndef)
 
   prob == :hs61 && continue #because nlpmodelsjump is not working here https://github.com/JuliaSmoothOptimizers/NLPModelsJuMP.jl/issues/84
-  prob in [:clplatea, :clplateb, :clplatec, :fminsrf2] && continue # issue because variable is a matrix
 
   nlp_jump = MathOptNLPModel(model)
   nlp_ad = eval(Meta.parse("ADNLPProblems.$(prob)()"))
@@ -72,7 +71,6 @@ end
 
 for prob in names(ADNLPProblems)
   prob == :ADNLPProblems && continue
-  prob in [:clplatea, :clplateb, :clplatec, :fminsrf2] && continue # issue because variable is a matrix
 
   println(prob)