🔧 Fix polishing unit test

Project.toml

@@ -22,8 +22,11 @@ julia = "1"
 
 [extras]
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
+FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Compat", "Random"]
+test = ["Test", "Compat", "Random", "FileIO", "JLD2"]

test/polishing.jl

@@ -72,53 +72,20 @@ tol = 1e-3
         @test results.info.status_polish == 1
     end
 
-
     @testset "polish_random" begin
-        seed!(1)
 
-        n = 30
-        m = 50
-        Pt = sprandn(n, n, 0.5)
-        P = Pt * Pt'
-        q = randn(n)
-        A = sprandn(m, n, 0.5)
-        l = -3 .+ randn(m)
-        u = 3 .+ randn(m)
-
-        # Solve problem
+        # load randomly generated problem with known accurate solution from Mosek
+        problem_data = FileIO.load("./problem_data/random_polish_qp.jld2");
+        P = problem_data["P"]; q = problem_data["q"]; A = problem_data["A"]; u = problem_data["u"]; l = problem_data["l"];
         options = setup_polishing()
         model = OSQP.Model()
         OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
         results = OSQP.solve!(model)
 
 
-        # # Solve with Gurobi
-        # using Gurobi
-        # env = Gurobi.Env()
-        # setparam!(env, "OutputFlag", 1)
-        # model = Gurobi.Model(env, "model")
-        # add_cvars!(model, q); update_model!(model)
-        # add_constrs!(model, A, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A, repmat(['>'], m), l); update_model!(model)
-        # add_qpterms!(model, P); update_model!(model)
-        # optimize(model)
-        # x_test = get_solution(model)
-        # obj_test= get_objval(model)
-        # y_test = -Gurobi.get_dblattrarray(model, "Pi", 1, 2 * m)
-        # y_test = y_test[m+1:end] + y_test[1:m]
-                #
-        # println(x_test)
-        # println(y_test)
-        # println(obj_test)
-
-        x_test = [0.369834, -0.0209277, 0.068939, -0.604151, 0.60773, -0.715965, -0.128837, -0.593642, 0.928932, 0.678794, 0.194334, 0.228124, 0.188779, 0.607246, 0.479752, 0.0878951, 0.275489, 0.761593, 0.0600479, -0.950663, -0.191354, 1.12286, -0.593261, -0.0932879, 0.0286826, -0.0257784, 0.358456, -0.0693514, -0.00358498, -0.0270959]
-        y_test = [4.88779e-14, 0.441478, 5.89704e-14, 1.76439e-13, 8.10529e-15, 4.9186e-14, 5.00637e-14, 2.00889e-14, 5.39402e-13, -1.9796e-14, 1.52041e-13, 0.149979, 8.97844e-12, 5.93941e-14, -4.11719e-14, -2.38358e-13, 0.226114, -1.8933e-14, 1.54839e-13, 2.99422e-14, 7.16687e-15, -5.32536e-14, 0.116129, -2.53245e-14, 1.21586e-14, -1.66761e-13, 3.04905e-14, 7.35909e-14, 3.84878e-12, -3.09855e-15, 4.30889e-9, 4.09921e-14, -2.4863e-14, -2.46841e-14, 4.37156e-15, 0.219029, -5.14155e-14, 8.31705e-14, 4.51252e-15, -7.90941e-14, 0.206435, -0.196264, 1.06762e-14, 0.338783, 7.58781e-10, -0.105739, 3.30014e-13, -4.05467e-14, 3.3212e-14, -0.161755]
-
-        obj_test = -6.021607803961248
-
-        @test isapprox(results.x, x_test, atol=tol)
-        @test isapprox(results.y, y_test, atol=tol)
-        @test isapprox(results.info.obj_val, obj_test, atol=tol)
+        @test isapprox(results.x, problem_data["x_test"], atol=tol)
+        @test isapprox(results.y, problem_data["y_test"], atol=tol)
+        @test isapprox(results.info.obj_val, problem_data["obj_test"], atol=tol)
         @test results.info.status_polish == 1
 
     end

test/runtests.jl

@@ -1,7 +1,7 @@
 using OSQP
 using Test, SparseArrays, LinearAlgebra
 using Random: seed!
-
+using FileIO
 tests = [
     "basic.jl",
     "dual_infeasibility.jl",