Update compat bounds for dependencies

Project.toml

@@ -14,8 +14,8 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
 ChainRulesCore = "1"
+FastDifferentiation = "0.4"
 ForwardDiff = "0.10"
 PATHSolver = "1.4"
-Symbolics = "4,5"
-FastDifferentiation = "0.3"
+Symbolics = "4,5,6"
 julia = "1.8"

src/sparse_utils.jl

@@ -59,10 +59,10 @@ function get_constant_entries(
     M_symbolic::AbstractMatrix{<:FD.Node},
     z_symbolic::AbstractVector{<:FD.Node},
 )
-    _z_syms = FD.variables(z_symbolic)
+    _z_syms = [zs.node_value for zs in FD.variables(z_symbolic)]
     # find all entries that are not a function of any of the symbols in z
     findall(SparseArrays.nonzeros(M_symbolic)) do v
-        _vars_syms = FD.variables(v)
+        _vars_syms = [vs.node_value for vs in FD.variables(v)]
         isempty(intersect(_vars_syms, _z_syms))
     end
 end

test/runtests.jl

@@ -4,10 +4,12 @@ using Random: Random
 using LinearAlgebra: norm
 using Zygote: Zygote
 using FiniteDiff: FiniteDiff
-using Symbolics: Symbolics
 
 @testset "ParametricMCPs.jl" begin
-    for backend in [ParametricMCPs.SymbolicUtils.SymbolicsBackend(), ParametricMCPs.SymbolicUtils.FastDifferentiationBackend()]
+    for backend in [
+        ParametricMCPs.SymbolicUtils.SymbolicsBackend(),
+        ParametricMCPs.SymbolicUtils.FastDifferentiationBackend(),
+    ]
         rng = Random.MersenneTwister(1)
         parameter_dimension = 2
         # setup a dummy MCP which represents a QP with:
@@ -16,8 +18,21 @@ using Symbolics: Symbolics
         f(z, θ) = [2z[1:2] - z[3:4] - 2θ; z[1:2]]
         lower_bounds = [-Inf, -Inf, 0, 0]
         upper_bounds = [Inf, Inf, Inf, Inf]
-        problem = ParametricMCPs.ParametricMCP(f, lower_bounds, upper_bounds, parameter_dimension; backend)
-        problem_no_jacobian = ParametricMCPs.ParametricMCP(f, lower_bounds, upper_bounds, parameter_dimension; compute_sensitivities=false, backend)
+        problem = ParametricMCPs.ParametricMCP(
+            f,
+            lower_bounds,
+            upper_bounds,
+            parameter_dimension;
+            backend,
+        )
+        problem_no_jacobian = ParametricMCPs.ParametricMCP(
+            f,
+            lower_bounds,
+            upper_bounds,
+            parameter_dimension;
+            compute_sensitivities = false,
+            backend,
+        )
 
         feasible_parameters = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [rand(rng, 2) for _ in 1:10]...]
         infeasible_parameters = -feasible_parameters
@@ -42,10 +57,11 @@ using Symbolics: Symbolics
 
             for θ in [feasible_parameters; infeasible_parameters]
                 ∇_autodiff_reverse = only(Zygote.gradient(dummy_pipeline, θ))
-                ∇_autodiff_forward = only(Zygote.gradient(θ -> Zygote.forwarddiff(dummy_pipeline, θ), θ))
+                ∇_autodiff_forward =
+                    only(Zygote.gradient(θ -> Zygote.forwarddiff(dummy_pipeline, θ), θ))
                 ∇_finitediff = FiniteDiff.finite_difference_gradient(dummy_pipeline, θ)
-                @test isapprox(∇_autodiff_reverse, ∇_finitediff; atol=1e-4)
-                @test isapprox(∇_autodiff_reverse, ∇_autodiff_forward; atol=1e-4)
+                @test isapprox(∇_autodiff_reverse, ∇_finitediff; atol = 1e-4)
+                @test isapprox(∇_autodiff_reverse, ∇_autodiff_forward; atol = 1e-4)
             end
         end
 
@@ -55,7 +71,10 @@ using Symbolics: Symbolics
                 sum(solution.z .^ 2)
             end
 
-            @test_throws ArgumentError Zygote.gradient(θ -> dummy_pipeline(θ, problem_no_jacobian), feasible_parameters[1])
+            @test_throws ArgumentError Zygote.gradient(
+                θ -> dummy_pipeline(θ, problem_no_jacobian),
+                feasible_parameters[1],
+            )
         end
     end
 end