Add unit tests for jth_hess_coord

src/julia_interface.jl

@@ -751,24 +751,27 @@ end
 
 function NLPModels.jth_hess_coord!(
   nlp::CUTEstModel{T},
-  x::AbstractVector,
+  x::StrideOneVector{T},
   j::Integer,
-  vals::AbstractVector,
+  vals::StrideOneVector{T},
 ) where {T}
+  increment!(nlp, :neval_jhess)
+  @rangecheck 1 nlp.meta.ncon j
   ref_j = nlp.index
   ref_j[] = j
+  nnzh_output = Ref{Cint}()
   cish(
     T,
     nlp.libsif,
     nlp.status,
     nlp.nvar,
     x,
     ref_j,
-    nlp.nnzh,
+    nnzh_output,
     nlp.nnzh,
     vals,
-    nlp.hrows,
     nlp.hcols,
+    nlp.hrows,
   )
   return vals
 end

test/multiple_precision.jl

@@ -1,15 +1,19 @@
 function multiple_precision()
   @testset "Test multiple precision models" begin
-    for (T, precision) in [(Float32, :single), (Float64, :double)]
+    for T in (Float32, Float64, Float128)
       for p in ["ROSENBR", "HS35", "ALLINITA"]
-        nlp = CUTEstModel(p, precision = precision)
+        nlp = CUTEstModel{T}(p)
         x = T.(nlp.meta.x0)
         fx = obj(nlp, x)
-        @test typeof(obj(nlp, x)) == T
-        @test eltype(grad(nlp, x)) == T
-        @test eltype(hess(nlp, x)) == T
-        @test eltype(cons(nlp, x)) == T
-        @test eltype(jac(nlp, x)) == T
+        gx = grad(nlp, x)
+        Hx = hess(nlp, x)
+        cx = cons(nlp, x)
+        Jx = jac(nlp, x)
+        @test typeof(fx) == T
+        @test eltype(gx) == T
+        @test eltype(Hx) == T
+        @test eltype(cx) == T
+        @test eltype(Jx) == T
         finalize(nlp)
       end
     end

test/nlpmodelstest.jl

@@ -4,7 +4,7 @@ for problem in problems
     nlp_manual = eval(Symbol(problem))()
     nlps = [nlp_cutest, nlp_manual]
 
-    consistent_nlps(nlps, linear_api = true, reimplemented = ["cons", "jac", "jprod", "jtprod"])
+    consistent_nlps(nlps, linear_api = true, reimplemented = ["cons", "jac", "jprod", "jtprod"], exclude = [jth_hprod, ghjvprod])
     view_subarray_nlp(nlp_cutest)
     coord_memory_nlp(nlp_cutest, linear_api = true)
 

test/runtests.jl

@@ -9,6 +9,7 @@ set_mastsif()
 @test ispath(ENV["MASTSIF"])
 @test isfile(joinpath(ENV["MASTSIF"], "CYCLOOCT.SIF"))
 problems = ["BROWNDEN", "HS5", "HS6", "HS10", "HS11", "HS13", "HS14"]
+dict_precision = Dict(Float32 => :single, Float64 => :double, Float128 => :quadruple)
 
 # test sifdecoder
 for pb in problems
@@ -38,9 +39,9 @@ include("coverage.jl")
 include("multiple_precision.jl")
 
 for problem in problems
-  for (T, precision) in [(Float32, :single), (Float64, :double), (Float128, :quadruple)]
-    println("Testing interfaces on problem $problem in $precision precision")
-    nlp = CUTEstModel(problem, precision = precision)
+  for T in (Float32, Float64, Float128)
+    println("Testing interfaces on problem $problem in $(dict_precision[T]) precision")
+    nlp = CUTEstModel{T}(problem)
     nlp_man = eval(Symbol(problem))(T)
 
     test_nlpinterface(nlp, nlp_man)
@@ -60,23 +61,20 @@ problems = CUTEst.select(max_var = 2, max_con = 2)
 problems = randsubseq(problems, 0.1)
 
 for p in problems
-  for (T, precision) in [(Float32, :single), (Float64, :double), (Float128, :quadruple)]
-    nlp = CUTEstModel(p, precision = precision)
+  for T in (Float32, Float64, Float128)
+    nlp = CUTEstModel{T}(p)
     x0 = nlp.meta.x0
     nvar, ncon = nlp.meta.nvar, nlp.meta.ncon
-
     println("$p: julia interface: f(x₀) = $(obj(nlp, x0))")
 
-    status = Cint[0]
-    fval = T[0.0]
+    fval = Ref{T}()
     if ncon > 0
       cx = zeros(T, ncon)
-      CUTEst.cfn(T, nlp.libsif, status, Cint[nvar], Cint[ncon], x0, fval, cx)
+      CUTEst.cfn(T, nlp.libsif, nlp.status, nlp.nvar, nlp.ncon, x0, fval, cx)
     else
-      CUTEst.ufn(T, nlp.libsif, status, Cint[nvar], x0, fval)
+      CUTEst.ufn(T, nlp.libsif, nlp.status, nlp.nvar, x0, fval)
     end
-    println("$p: core interface: f(x₀) = $(fval[1])")
-
+    println("$p: core interface: f(x₀) = $(fval[])")
     finalize(nlp)
   end
 end
@@ -92,15 +90,15 @@ finalize(nlp)
 
 @testset "Test decoding separately (issue #239)" begin
   problems = ["AKIVA", "ROSENBR", "ZANGWIL2"]
-  for (T, precision) in [(Float32, :single), (Float64, :double), (Float128, :quadruple)]
+  for T in (Float32, Float64, Float128)
     # Decoding
     for p in problems
-      nlp = CUTEstModel(p, precision = precision)
+      nlp = CUTEstModel{T}(p)
       finalize(nlp)
     end
     # No decode
     for p in problems
-      nlp = CUTEstModel(p, precision = precision, decode = false)
+      nlp = CUTEstModel{T}(p, decode = false)
       @test nlp.meta.nvar == 2
       finalize(nlp)
     end