Detect NLS pattern

src/JSOSuite.jl

@@ -10,6 +10,12 @@ using LinearOperators, NLPModelsModifiers, SolverCore, SolverParameters
 # JSO solvers
 using JSOSolvers, Percival
 
+@init begin
+  @require ExpressionTreeForge = "93090adf-0e31-445f-8c8f-44d91f61d7ad" begin
+    include("parse-functions.jl")
+  end
+end
+
 include("optimizers.jl")
 include("selection.jl")
 include("solve-model.jl")

src/parse-functions.jl

@@ -0,0 +1,18 @@
+"""
+    isnls(nlp::ADNLPModels.AbstractADNLPModel)
+    isnls(nlp::MathOptInterface.Nonlinear.Model)
+    isnls(nlp::JuMP.Model)
+    isnls(nlp::MathOptNLPModel)
+
+Check if the given model has a nonlinear least squares objective.
+
+The function uses the package ExpressionTreeForge to get the expression tree of the objective function,
+then try to detect the least squares pattern. There is no guarantee that this function detects it accurately.
+"""
+function isnls(nlp)
+  expr_tree = ExpressionTreeForge.get_expression_tree(nlp)
+  F_expr = ExpressionTreeForge.extract_element_functions(expr_tree)
+  test_square(expr) = expr.field == ExpressionTreeForge.M_power_operator.Power_operator{Int}(2)
+  is_nls = mapreduce(test_square, &, F_expr)::Bool
+  return is_nls
+end

test/Project.toml

@@ -3,6 +3,7 @@ ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 CaNNOLeS = "5a1c9e79-9c58-5ec0-afc4-3298fdea2875"
 DCISolver = "bee2e536-65f6-11e9-3844-e5bb4c9c55c9"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+ExpressionTreeForge = "93090adf-0e31-445f-8c8f-44d91f61d7ad"
 FletcherPenaltySolver = "e59f0261-166d-4fee-8bf3-5e50457de5db"
 JSOSolvers = "10dff2fc-5484-5881-a0e0-c90441020f8a"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -32,6 +33,7 @@ ADNLPModels = "0.8.4"
 CaNNOLeS = "0.7"
 DCISolver = "0.4"
 DataFrames = "1"
+ExpressionTreeForge = "^0.1.7"
 FletcherPenaltySolver = "0.2"
 JSOSolvers = "0.10, 0.11"
 JuMP = "1"

test/parse-function-test.jl

@@ -0,0 +1,23 @@
+problems_names = meta[!, :name][1:50] # we test only the 50 first problems
+problems_symbols =
+  map(name -> OptimizationProblems.ADNLPProblems.eval(Meta.parse(name)), problems_names)
+
+@testset "NLS from NLP" begin
+  result_list_isnls = Bool[]
+  for (index, problem) in enumerate(problems_symbols)
+    try
+      detect_nls = JSOSuite.isnls(problem())
+      is_nls = meta[meta.name .== problems_names[index], :objtype][1] == :least_squares
+      push!(result_list_isnls, detect_nls)
+      if detect_nls
+        @test detect_nls == is_nls
+        @debug "Least squares objective, $index : $problem"
+        # detect_nls != is_nls && (@debug "$index : $problem")
+      else
+        @debug "Not least squares objective, $index : $problem"
+      end
+    catch
+      @debug "the $index-th $(problems_names[index]) problem is unsupported."
+    end
+  end
+end

test/runtests.jl

@@ -11,6 +11,12 @@ using JuMP, NLPModelsJuMP
 using ADNLPModels, NLPModels, NLSProblems, QuadraticModels, OptimizationProblems, SparseMatricesCOO
 using JSOSolvers, Percival, SolverCore
 
+meta = OptimizationProblems.meta
+
+using ExpressionTreeForge
+
+include("parse-function-test.jl")
+
 @testset "Test not loaded solvers" begin
   nlp = ADNLPModel(x -> sum(x), ones(2))
 
@@ -30,8 +36,6 @@ end
 using CaNNOLeS, DCISolver, FletcherPenaltySolver, NLPModelsIpopt, RipQP
 using SolverBenchmark
 
-meta = OptimizationProblems.meta
-
 function test_in_place_solve(nlp, solver_name)
   pkg_name = JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :name_pkg][1]
   pkg_name = replace(pkg_name, ".jl" => "")