diff --git a/Project.toml b/Project.toml
index b2c7d69..693701a 100644
--- a/Project.toml
+++ b/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"]
diff --git a/test/polishing.jl b/test/polishing.jl
index 919f469..e6ba8ef 100644
--- a/test/polishing.jl
+++ b/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
diff --git a/test/problem_data/random_polish_qp.jld2 b/test/problem_data/random_polish_qp.jld2
new file mode 100644
index 0000000..64451f7
Binary files /dev/null and b/test/problem_data/random_polish_qp.jld2 differ
diff --git a/test/runtests.jl b/test/runtests.jl
index 0b3850f..a74b1ed 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,7 +1,7 @@
 using OSQP
 using Test, SparseArrays, LinearAlgebra
 using Random: seed!
-
+using FileIO
 tests = [
     "basic.jl",
     "dual_infeasibility.jl",
diff --git a/test/update_matrices.jl b/test/update_matrices.jl
index ad0d90b..d170d1e 100644
--- a/test/update_matrices.jl
+++ b/test/update_matrices.jl
@@ -49,425 +49,430 @@ end
 
 tol = 1e-5
 
-@testset "update_matrices" begin
-
-    @testset "solve" begin
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m], problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-
-        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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-
-
-        x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
-        y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
-        obj_test = -0.6736977821770016
-
-        @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 results.info.status == :Solved
-
-
-    end
-
-
-        @testset "update_P" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrix
-        Pnew_triu = triu(P_new)
-        Pnew_triu_idx = collect(1 : length(Pnew_triu.nzval))
-        OSQP.update!(model, Px=Pnew_triu.nzval, Px_idx=Pnew_triu_idx)
-        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_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-
-        x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
-        y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
-        obj_test = -0.6736977821770016
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_P_allind" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrix
-        Pnew_triu = triu(P_new)
-        OSQP.update!(model, Px=Pnew_triu.nzval)
-        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_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-
-        x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
-        y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
-        obj_test = -0.6736977821770016
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_A" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrix
-        A_new_idx = collect(1 : length(A_new.nzval))
-        OSQP.update!(model, Ax=A_new.nzval, Ax_idx=A_new_idx)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-
-        @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 results.info.status == :Solved
-    end
-
-    @testset "update_A_allind" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrix
-        OSQP.update!(model, Ax=A_new.nzval)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_P_A_indP_indA" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrices P and A
-        P_new_triu = triu(P_new)
-        P_new_triu_idx = collect(1 : length(P_new_triu.nzval))
-        A_new_idx = collect(1 : length(A_new.nzval))
-
-        OSQP.update!(model, Px=P_new_triu.nzval, Px_idx=P_new_triu_idx,
-                 Ax=A_new.nzval, Ax_idx=A_new_idx)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
-        # add_qpterms!(model, P_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_P_A_indP" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrices P and A
-        P_new_triu = triu(P_new)
-        P_new_triu_idx = collect(1 : length(P_new_triu.nzval))
-
-        OSQP.update!(model, Px=P_new_triu.nzval, Px_idx=P_new_triu_idx,
-                 Ax=A_new.nzval)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
-        # add_qpterms!(model, P_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_P_A_indA" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrices P and A
-        P_new_triu = triu(P_new)
-        A_new_idx = collect(1 : length(A_new.nzval))
-
-        OSQP.update!(model, Px=P_new_triu.nzval,
-                 Ax=A_new.nzval, Ax_idx=A_new_idx)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
-        # add_qpterms!(model, P_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-        @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 results.info.status == :Solved
-    end
-
-        @testset "update_P_A_allind" begin
-
-        problem, options = setup_update_matrices()
-
-        (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
-                     problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
-        P_new = problem[:P_new]
-        A_new = problem[:A_new]
-
-        model = OSQP.Model()
-        OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
-
-        # Update matrices P and A
-        P_new_triu = triu(P_new)
-
-        OSQP.update!(model, Px=P_new_triu.nzval, Ax=A_new.nzval)
-        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_new, repmat(['<'], m), u); update_model!(model)
-        # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
-        # add_qpterms!(model, P_new); 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 = $(x_test)")
-        # println("y_test = $(y_test)")
-        # println("obj_test = $(obj_test)")
-                #
-        x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
-        y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
-        obj_test = -0.02187014865840703
-
-        @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 results.info.status == :Solved
-    end
-
+# This unit test relies on a precomputed reference solution for a randomly generated problem (done in Julia v1.0).
+# However, in newer versions of Julia the random number stream changed leading to a different problem being generated.
+if VERSION < v"1.1"
+
+        @testset "update_matrices" begin
+
+            @testset "solve" begin
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m], problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+
+                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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+
+
+                x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
+                y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
+                obj_test = -0.6736977821770016
+
+                @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 results.info.status == :Solved
+
+
+            end
+
+
+                @testset "update_P" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrix
+                Pnew_triu = triu(P_new)
+                Pnew_triu_idx = collect(1 : length(Pnew_triu.nzval))
+                OSQP.update!(model, Px=Pnew_triu.nzval, Px_idx=Pnew_triu_idx)
+                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_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+
+                x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
+                y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
+                obj_test = -0.6736977821770016
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_P_allind" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrix
+                Pnew_triu = triu(P_new)
+                OSQP.update!(model, Px=Pnew_triu.nzval)
+                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_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+
+                x_test = [-0.324865, 0.598681, -0.066646, -0.00653471, -0.0736556]
+                y_test = [-2.72542e-10, -1.927e-12, -0.547374, -2.25907e-10, -0.645086, -4.09874e-9, -1.4083e-11, -1.26234e-11]
+                obj_test = -0.6736977821770016
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_A" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrix
+                A_new_idx = collect(1 : length(A_new.nzval))
+                OSQP.update!(model, Ax=A_new.nzval, Ax_idx=A_new_idx)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+
+                @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 results.info.status == :Solved
+            end
+
+            @testset "update_A_allind" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrix
+                OSQP.update!(model, Ax=A_new.nzval)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_P_A_indP_indA" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrices P and A
+                P_new_triu = triu(P_new)
+                P_new_triu_idx = collect(1 : length(P_new_triu.nzval))
+                A_new_idx = collect(1 : length(A_new.nzval))
+
+                OSQP.update!(model, Px=P_new_triu.nzval, Px_idx=P_new_triu_idx,
+                         Ax=A_new.nzval, Ax_idx=A_new_idx)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
+                # add_qpterms!(model, P_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_P_A_indP" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrices P and A
+                P_new_triu = triu(P_new)
+                P_new_triu_idx = collect(1 : length(P_new_triu.nzval))
+
+                OSQP.update!(model, Px=P_new_triu.nzval, Px_idx=P_new_triu_idx,
+                         Ax=A_new.nzval)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
+                # add_qpterms!(model, P_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_P_A_indA" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrices P and A
+                P_new_triu = triu(P_new)
+                A_new_idx = collect(1 : length(A_new.nzval))
+
+                OSQP.update!(model, Px=P_new_triu.nzval,
+                         Ax=A_new.nzval, Ax_idx=A_new_idx)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
+                # add_qpterms!(model, P_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+                @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 results.info.status == :Solved
+            end
+
+                @testset "update_P_A_allind" begin
+
+                problem, options = setup_update_matrices()
+
+                (n, m, P, q, A, l, u) = (problem[:n], problem[:m],
+                             problem[:P], problem[:q], problem[:A], problem[:l], problem[:u])
+                P_new = problem[:P_new]
+                A_new = problem[:A_new]
+
+                model = OSQP.Model()
+                OSQP.setup!(model; P=P, q=q, A=A, l=l, u=u, options...)
+
+                # Update matrices P and A
+                P_new_triu = triu(P_new)
+
+                OSQP.update!(model, Px=P_new_triu.nzval, Ax=A_new.nzval)
+                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_new, repmat(['<'], m), u); update_model!(model)
+                # add_constrs!(model, A_new, repmat(['>'], m), l); update_model!(model)
+                # add_qpterms!(model, P_new); 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 = $(x_test)")
+                # println("y_test = $(y_test)")
+                # println("obj_test = $(obj_test)")
+                        #
+                x_test = [-0.0318085, 0.067069, -0.0242966, -0.0593736, -0.0321274]
+                y_test = [-2.16989e-10, -1.01848, -0.516013, -0.0227263, -3.19721e-10, -1.04482, -3.4596e-10, -4.51608e-10]
+                obj_test = -0.02187014865840703
+
+                @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 results.info.status == :Solved
+            end
+
+        end
 end