MOI 1.0 and JuMP 0.23

.github/workflows/ci.yml

@@ -17,10 +17,10 @@ jobs:
           - version: '1'
             os: ubuntu-latest
             arch: x64
-          - version: '1.0'
+          - version: '1.6'
             os: ubuntu-latest
             arch: x64
-          - version: '1.0'
+          - version: '1.6'
             os: ubuntu-latest
             arch: x86
     steps:

.github/workflows/documentation.yml

@@ -13,11 +13,10 @@ jobs:
       - uses: julia-actions/setup-julia@latest
         with:
           # Build documentation on Julia 1.0
-          version: '1.0'
+          version: '1.6'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
         run: julia --project=docs/ docs/make.jl

Project.toml

@@ -1,23 +1,13 @@
 name = "Dualization"
 uuid = "191a621a-6537-11e9-281d-650236a99e60"
 authors = ["guilhermebodin <guilherme.b.moraes@gmail.com>"]
-version = "0.4.0"
+version = "0.5.0"
 
 [deps]
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 
 [compat]
-JuMP = "0.22"
-MathOptInterface = "0.10"
-julia = "1"
-
-[extras]
-CSDP = "0a46da34-8e4b-519e-b418-48813639ff34"
-ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
-GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
-SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Test", "GLPK", "ECOS", "CSDP", "SCS"]
+JuMP = "0.23"
+MathOptInterface = "1"
+julia = "1.6"

README.md

@@ -29,7 +29,7 @@ Dualization.jl has two main features.
 dual_model = dualize(model)
 ```
 
- * The `DualOptimizer` that will pass the dual representation of the model to the solver of your choice.
+ * The `DualOptimizer` that will pass the dual representation of the model to the `SOLVER` of your choice.
 
 ```julia
 model = Model(dual_optimizer(SOLVER.Optimizer))

src/MOI_wrapper.jl

@@ -28,15 +28,15 @@ The user can define the model providing the `DualOptimizer` and the solver of it
 Example:
 
 ```julia
-julia> using Dualization, JuMP, GLPK
+julia> using Dualization, JuMP, HiGHS
 
-julia> model = Model(dual_optimizer(GLPK.Optimizer))
+julia> model = Model(dual_optimizer(HiGHS.Optimizer))
 A JuMP Model
 Feasibility problem with:
 Variables: 0
 Model mode: AUTOMATIC
 CachingOptimizer state: EMPTY_OPTIMIZER
-Solver name: Dual model with GLPK attached
+Solver name: Dual model with HiGHS attached
 ```
 """
 function DualOptimizer(dual_optimizer::OT) where {OT<:MOI.ModelLike}

test/Project.toml

@@ -0,0 +1,12 @@
+[deps]
+CSDP = "0a46da34-8e4b-519e-b418-48813639ff34"
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+CSDP = "1.0.0"
+HiGHS = "1.1.0"
+SCS = "1.0.1"

test/Solvers/highs_test.jl

@@ -0,0 +1,35 @@
+using HiGHS
+const HiGHS_PRIMAL_FACTORY =
+    MOI.OptimizerWithAttributes(HiGHS.Optimizer, MOI.Silent() => true)
+const HiGHS_DUAL_FACTORY = dual_optimizer(HiGHS_PRIMAL_FACTORY)
+const HiGHS_PRIMAL_OPT = MOI.instantiate(HiGHS_PRIMAL_FACTORY)
+const HiGHS_DUAL_OPT = MOI.instantiate(HiGHS_DUAL_FACTORY)
+
+push!(primal_linear_factory, HiGHS_PRIMAL_FACTORY)
+push!(dual_linear_factory, HiGHS_DUAL_FACTORY)
+push!(dual_linear_optimizer, HiGHS_DUAL_OPT)
+push!(primal_linear_optimizer, HiGHS_PRIMAL_OPT)
+
+@testset "HiGHS Linear Problems" begin
+    list_of_linear_problems = [
+        lp1_test,
+        lp2_test,
+        lp3_test,
+        lp4_test,
+        lp5_test,
+        lp6_test,
+        lp7_test,
+        # lp8_test, Int64 problem, does not work
+        # lp9_test, Interval is not implemented
+        lp10_test,
+        # lp11_test, Feasibility not supported
+        lp12_test,
+        lp13_test,
+    ]
+    test_strong_duality(list_of_linear_problems, HiGHS_PRIMAL_FACTORY)
+end
+
+@testset "HiGHS Quadratic Problems" begin
+    list_of_quad_problems = [qp1_test, qp2_test]
+    test_strong_duality(list_of_quad_problems, HiGHS_PRIMAL_FACTORY)
+end

test/Solvers/scs_test.jl

@@ -10,7 +10,42 @@ push!(dual_power_cone_factory, SCS_DUAL_FACTORY)
 push!(dual_power_cone_optimizer, SCS_DUAL_OPT)
 push!(primal_power_cone_optimizer, SCS_PRIMAL_OPT)
 
-@testset "SCS Exponential Cone Problems" begin
+@testset "SCS Exponential Cone problems" begin
     list_of_exp_problems = [exp1_test, exp2_test]
     test_strong_duality(list_of_exp_problems, SCS_PRIMAL_FACTORY)
 end
+
+@testset "SCS SOC problems" begin
+    list_of_soc_problems = [soc3_test, soc4_test, soc5_test, soc6_test]
+    test_strong_duality(list_of_soc_problems, SCS_PRIMAL_FACTORY)
+    list_of_soc_problems = [soc1_test, soc2_test]
+    test_strong_duality(
+        list_of_soc_problems,
+        SCS_PRIMAL_FACTORY,
+        atol = 1e-3,
+        rtol = 1e-3,
+    )
+end
+
+@testset "SCS RotatedSOC problems" begin
+    list_of_rsoc_problems = [rsoc1_test, rsoc2_test, rsoc3_test]
+    test_strong_duality(list_of_rsoc_problems, SCS_PRIMAL_FACTORY)
+    list_of_rsoc_problems = [rsoc4_test]
+    test_strong_duality(
+        list_of_rsoc_problems,
+        SCS_PRIMAL_FACTORY,
+        atol = 1e-3,
+        rtol = 1e-3,
+    )
+end
+
+@testset "SCS SDP triangle problems" begin
+    list_of_sdp_triang_problems =
+        [sdpt1_test, sdpt2_test, sdpt3_test, sdpt4_test]
+    test_strong_duality(list_of_sdp_triang_problems, SCS_PRIMAL_FACTORY)
+end
+
+@testset "SCS Power Cone problems" begin
+    list_of_pow_problems = [pow1_test, pow2_test]
+    test_strong_duality(list_of_pow_problems, SCS_PRIMAL_FACTORY)
+end

test/Tests/test_JuMP_dualize.jl

@@ -27,7 +27,7 @@ end
             dual_JuMP_model = dualize(JuMP_model, primal_linear_factory[i])
             @test backend(dual_JuMP_model).state == MOIU.EMPTY_OPTIMIZER
             @test MOI.get(backend(dual_JuMP_model), MOI.SolverName()) ==
-                  MOI.get(primal_linear_optimizer[i], MOI.SolverName())#"GLPK"
+                  MOI.get(primal_linear_optimizer[i], MOI.SolverName())
         end
     end
     @testset "set_dot on different sets" begin

test/Tests/test_dualize_linear.jl

@@ -93,8 +93,7 @@
         @test obj_type == SAF{Float64}
         obj = MOI.get(dual_model, MOI.ObjectiveFunction{obj_type}())
         @test MOI.constant(obj) == -1.0
-        @test MOI.coefficient.(obj.terms) ==
-              (Sys.WORD_SIZE == 32 ? [1.0, 3.0, 3.0] : [3.0; 1.0; 3.0])
+        @test Set(MOI.coefficient.(obj.terms)) == Set([3.0; 1.0; 3.0])
     end
 
     @testset "lp10_test" begin

test/Tests/test_modify.jl

@@ -1,5 +1,5 @@
 @testset "modify" begin
-    model = Model(GLPK_DUAL_FACTORY)
+    model = Model(HiGHS_DUAL_FACTORY)
     @variable(model, x[1:2] >= 0)
     @constraint(model, 2x[1] + x[2] <= 4)
     @constraint(model, x[1] + 2x[2] <= 4)
@@ -10,7 +10,7 @@
     optimize!(model)
     @test objective_value(model) ≈ 10.6666666666
 
-    model = Model(GLPK_DUAL_FACTORY)
+    model = Model(HiGHS_DUAL_FACTORY)
     @variable(model, x[1:2] >= 0)
     @constraint(model, 2x[1] + x[2] <= 4)
     @constraint(model, x[1] + 2x[2] <= 4)

test/optimize_abstract_models.jl

@@ -11,7 +11,11 @@ function solve_abstract_model(
     set_optimizer(JuMP_model, optimizer_constructor)
     optimize!(JuMP_model)
     termination_status = JuMP.termination_status(JuMP_model)
-    obj_val = JuMP.objective_value(JuMP_model)
+    obj_val = try
+        JuMP.objective_value(JuMP_model)
+    catch
+        NaN
+    end
     return termination_status, obj_val
 end
 
@@ -45,11 +49,13 @@ function test_strong_duality(
            (dual_term_status == MOI.DUAL_INFEASIBLE)
         return true
     end
+    @show primal_term_status, primal_obj_val
+    @show dual_term_status, dual_obj_val
     return false # In case strong duality doesn't hold
 end
 
 function test_strong_duality(
-    primal_problems::Array{Function},
+    primal_problems::Vector,
     optimizer_constructor;
     atol = 1e-6,
     rtol = 1e-4,

test/runtests.jl

@@ -103,8 +103,7 @@ primal_power_cone_optimizer = []
 
 # Load & Test strong duality in linear/conic problems
 # Comment the solver that are not available for development
-include("Solvers/glpk_test.jl")
-include("Solvers/ecos_test.jl")
+include("Solvers/highs_test.jl")
 include("Solvers/csdp_test.jl")
 include("Solvers/scs_test.jl")