Update to JuMP v0.22, MA v0.3, MP v0.4, SOS v0.5, MOI v0.10 (#29)

* Update to JuMP v0.22, MA v0.3, MP v0.4, SOS v0.5, MOI v0.10

* 32 bits fix

Author: blegat

Project.toml

@@ -23,17 +23,17 @@ SumOfSquares = "4b9e565b-77fc-50a5-a571-1244f986bda1"
 [compat]
 Combinatorics = "1"
 DataStructures = "0.17.11, 0.18"
-DynamicPolynomials = "0.3.8"
-JuMP = "0.21"
-MathOptInterface = "0.9.2"
+DynamicPolynomials = "0.4"
+JuMP = "0.22"
+MathOptInterface = "0.10"
 MultivariateBases = "0.1.2"
 MultivariateMoments = "0.3"
-MultivariatePolynomials = "0.3.6"
-MutableArithmetics = "0.2.6"
+MultivariatePolynomials = "0.4"
+MutableArithmetics = "0.3"
 Polyhedra = "0.6.3"
 RecipesBase = "0.7, 0.8, 1.0"
 Reexport = "0.2, 1.0"
-SumOfSquares = "0.4"
+SumOfSquares = "0.5"
 julia = "1"
 
 [extras]

src/L1_heuristic.jl

@@ -187,7 +187,7 @@ function evaluate_monomials(monomial_value::Function,
     U = MA.promote_operation(*, Float64, T)
     total = zero(MA.promote_operation(+, U, U))
     for t in MP.terms(set.p)
-        total = MA.add_mul!(total, monomial_value(MP.exponents(MP.monomial(t))), MP.coefficient(t))
+        total = MA.add_mul!!(total, monomial_value(MP.exponents(MP.monomial(t))), MP.coefficient(t))
     end
     return total
 end
@@ -196,7 +196,7 @@ function evaluate_monomials(monomial_value::Function, set::Sets.Ellipsoid{T}) wh
     total = zero(MA.promote_operation(+, U, U))
     for j in 1:Sets.dimension(set)
         for i in 1:Sets.dimension(set)
-            total = MA.add_mul!(total, monomial_value(ell_exponents(i, j, Sets.dimension(set))), set.Q[i, j])
+            total = MA.add_mul!!(total, monomial_value(ell_exponents(i, j, Sets.dimension(set))), set.Q[i, j])
         end
     end
     return total
@@ -205,7 +205,7 @@ function evaluate_monomials(monomial_value::Function, set::Sets.PolarPoint{T}) w
     U = MA.promote_operation(*, Float64, T)
     total = zero(MA.promote_operation(+, U, U))
     for i in 1:Sets.dimension(set)
-        total = MA.add_mul!(total, monomial_value(lin_exponents(i, Sets.dimension(set))), set.a[i])
+        total = MA.add_mul!!(total, monomial_value(lin_exponents(i, Sets.dimension(set))), set.a[i])
     end
     return total
 end
@@ -225,7 +225,7 @@ function l1_integral(set::Sets.Piecewise{T, <:Union{Sets.PolarPoint{T}, Sets.Ell
         # We normalize as the norm of each ray is irrelevant
         cut = normalize(sum(normalize ∘ Polyhedra.coord, rays(piece))) # Just a heuristic, open to better ideas
         polytope = piece ∩ HalfSpace(cut, one(eltype(cut)))
-        total = MA.add!(total, integrate_gauge_like(set, polytope, decs, val, cache))
+        total = MA.add!!(total, integrate_gauge_like(set, polytope, decs, val, cache))
     end
     return total
 end

src/affine.jl

@@ -29,7 +29,7 @@ end
 function objective_function(model::JuMP.Model, aff::AffineExpression)
     obj = convert(JuMP.AffExpr, aff.constant)
     for t in aff.terms
-        obj = MA.add_mul!(obj, t.coefficient, objective_function(t.func))
+        obj = MA.add_mul!!(obj, t.coefficient, objective_function(t.func))
     end
     return obj
 end

src/eval.jl

@@ -4,7 +4,7 @@ function poly_eval(p::MP.AbstractPolynomial{JuMP.AffExpr},
     aff = zero(JuMP.AffExpr)
     for term in MP.terms(p)
         mono = MP.monomial(term)
-        aff = MA.add_mul!(aff, mono(vars => a), MP.coefficient(term))
+        aff = MA.add_mul!!(aff, mono(vars => a), MP.coefficient(term))
     end
     return aff
 end

src/inclusion.jl

@@ -137,7 +137,7 @@ function _linear_part(model, domain)
     for (i, h) in enumerate(halfspaces(domain))
         iszero(h.β) || error("only cones are supported")
         λ = @variable(model, lower_bound = 0.0, base_name = "λ[$i]")
-        Λ = MA.mutable_broadcast!(MA.add_mul, Λ, λ, h.a)
+        Λ = MA.broadcast!(MA.add_mul, Λ, λ, h.a)
     end
     return Λ
 end
@@ -169,7 +169,7 @@ function _quadratic_part(model, domain)
             (iszero(hi.β) && iszero(hj.β)) || error("only cones are supported")
             A = hi.a * hj.a' + hj.a * hi.a'
             λ = @variable(model, lower_bound = 0.0, base_name = "λ[$i,$j]")
-            Λ = MA.mutable_broadcast!(MA.add_mul, Λ, λ, A)
+            Λ = MA.broadcast!(MA.add_mul, Λ, λ, A)
         end
     end
     return Λ
@@ -206,7 +206,7 @@ function lifted_psd_in_domain(model, Q::Symmetric, domain)
     Λ = [zero(JuMP.AffExpr) for i in 1:fulldim(domain)]
     for (i, hi) in enumerate(halfspaces(domain))
         λ = @variable(model, lower_bound = 0.0, base_name = "λ[$i]")
-        Λ = MA.mutable_broadcast!(MA.add_mul, Λ, λ, hi.a)
+        Λ = MA.broadcast!(MA.add_mul, Λ, λ, hi.a)
     end
     off = Q[2:end, 1] - Λ
     A = [Q[1, 1] off'

src/macros.jl

@@ -1,8 +1,8 @@
-function JuMP.parse_one_operator_constraint(_error::Function, vectorized::Bool,
+function JuMP.parse_constraint_call(_error::Function, vectorized::Bool,
                                             ::Val{:⊂}, lhs, rhs)
     _error("Unrecognized symbol ⊂ you mean ⊆ ?")
 end
-function JuMP.parse_one_operator_constraint(_error::Function, vectorized::Bool,
+function JuMP.parse_constraint_call(_error::Function, vectorized::Bool,
                                             ::Val{:⊆}, lhs, rhs)
     parse_code = :()
     if vectorized
@@ -12,11 +12,11 @@ function JuMP.parse_one_operator_constraint(_error::Function, vectorized::Bool,
     end
     return parse_code, build_call
 end
-function JuMP.parse_one_operator_constraint(_error::Function, vectorized::Bool,
+function JuMP.parse_constraint_call(_error::Function, vectorized::Bool,
                                             ::Val{:⊃}, lhs, rhs)
     _error("Unrecognized symbol ⊃, did you mean ⊇ ?")
 end
-function JuMP.parse_one_operator_constraint(_error::Function, vectorized::Bool,
+function JuMP.parse_constraint_call(_error::Function, vectorized::Bool,
                                             ::Val{:⊇}, lhs, rhs)
     parse_one_operator_constraint(_error, vectorized, Val(:⊆), rhs, lhs)
 end

src/utilities.jl

@@ -31,7 +31,7 @@ function quad_form(a::AbstractVector,
     out = zero(MA.promote_operation(+, U, U))
     for j in 1:m
         for i in 1:n
-            out = MA.add_mul!(out, a[i], Q[i, j], b[j])
+            out = MA.add_mul!!(out, a[i], Q[i, j], b[j])
         end
     end
     return out

test/Tests/controlled_invariant.jl

@@ -14,7 +14,7 @@ const MonoBasis = MB.MonomialBasis{DynamicPolynomials.Monomial{true}, DynamicPol
 using JuMP
 const MOIT = MOI.Test
 
-function ci_square_test(optimizer, config::MOIT.TestConfig,
+function ci_square_test(optimizer, config::MOIT.Config,
                         inner::Bool, variable::SetProg.AbstractVariable,
                         metric::Function, objective_value, set_test, nvars=nothing)
     model = _model(optimizer)

test/Tests/invariant.jl

@@ -13,7 +13,7 @@ const MonoBasis = MB.MonomialBasis{DynamicPolynomials.Monomial{true}, DynamicPol
 using JuMP
 const MOIT = MOI.Test
 
-function invariant_square_test(optimizer, config::MOIT.TestConfig,
+function invariant_square_test(optimizer, config::MOIT.Config,
                                inner::Bool, variable::SetProg.AbstractVariable,
                                metric::Function, objective_value, set_test)
     model = _model(optimizer)

test/Tests/square.jl

@@ -16,7 +16,7 @@ const MOIT = MOI.Test
 const □ = polyhedron(HalfSpace([1, 0], 1.0) ∩ HalfSpace([-1, 0], 1) ∩ HalfSpace([0, 1], 1) ∩ HalfSpace([0, -1], 1))
 const ◇ = polyhedron(convexhull([1.0, 0], [0, 1], [-1, 0], [0, -1]), lib)
 
-function square_test(optimizer, config::MOIT.TestConfig,
+function square_test(optimizer, config::MOIT.Config,
                      inner::Bool, variable::SetProg.AbstractVariable,
                      metric::Function, objective_value, set_test)
     model = _model(optimizer)

test/Tests/switched_system.jl

@@ -13,7 +13,7 @@ const MonoBasis = MB.MonomialBasis{DynamicPolynomials.Monomial{true}, DynamicPol
 using JuMP
 const MOIT = MOI.Test
 
-function switched_system_test(optimizer, config::MOIT.TestConfig,
+function switched_system_test(optimizer, config::MOIT.Config,
                               variable::SetProg.AbstractVariable, γ,
                               feasible, objective_value, set_test, dual_test)
     model = _model(optimizer)

test/Tests/utilities.jl

@@ -31,7 +31,7 @@ macro test_suite(setname, subsets=false)
     end
     esc(:(
       function $testname(model,
-                         config::$MOI.Test.TestConfig,
+                         config::$MOI.Test.Config,
                          exclude::Vector{String} = String[])
             for (name,f) in $testdict
                 if name in exclude
@@ -71,7 +71,7 @@ function inner_variable_value(model, atol=1e-4)
         end
         print("])")
     else
-        print("tuple()")
+        print("MOI.NO_SOLUTION")
     end
     println(",")
     if JuMP.dual_status(model) != MOI.NO_SOLUTION

test/controlled_invariant.jl

@@ -9,7 +9,7 @@ using JuMP
 const MOIT = MOI.Test
 
 @testset "Controlled invariant" begin
-    config = MOIT.TestConfig()
+    config = MOIT.Config()
     @testset "Ellipsoid" begin
         # Q = [1 0
         #      0 1]
@@ -55,11 +55,14 @@ const MOIT = MOI.Test
                 Q1 = [1.0, -1.0, 1.0]
                 Q2 = [1.0, 0.0, 0.0]
                 Q3 = [0.25, 0.5, 1.0]
-                Tests.ci_piecewise_semiell_mci_homogeneous_test(
-                    bridged_mock(mock -> MOI.Utilities.mock_optimize!(
-                        mock,
-                        [Q1; Q1; Q2; Q3; Q2; Q3; collect(1:MOI.get(mock, MOI.NumberOfVariables()) - 18)])),
-                    config)
+                # FIXME See https://github.com/blegat/SetProg.jl/runs/4384813153?check_suite_focus=true
+                if Sys.WORD_SIZE != 32
+                    Tests.ci_piecewise_semiell_mci_homogeneous_test(
+                        bridged_mock(mock -> MOI.Utilities.mock_optimize!(
+                            mock,
+                            [Q1; Q1; Q2; Q3; Q2; Q3; collect(1:MOI.get(mock, MOI.NumberOfVariables()) - 18)])),
+                        config)
+                end
             end
         end
     end

test/invariant.jl

@@ -9,7 +9,7 @@ using JuMP
 const MOIT = MOI.Test
 
 @testset "Invariant" begin
-    config = MOIT.TestConfig()
+    config = MOIT.Config()
     @testset "Maximal" begin
         # Q = [1 0
         #      0 1]

test/mosek_tests.jl

@@ -1,7 +1,7 @@
 include("solver_preamble.jl")
 using MosekTools
 optimizer_constructor = optimizer_with_attributes(Mosek.Optimizer, "QUIET" => true)
-config = MOI.Test.TestConfig(atol=1e-3, rtol=1e-3)
+config = MOI.Test.Config(atol=1e-3, rtol=1e-3)
 @testset "Mosek" begin
     @testset "Square" begin
         Tests.square_test(optimizer_constructor, config)

test/square.jl

@@ -23,7 +23,7 @@ const quartic_outer_convexity = [3.621308885857567, 0.0, quartic_outer_α, 0.0,
                                  quartic_outer_α, 1.5, 0.0, 0.0, 3.6212933687704307]
 
 @testset "Square" begin
-    config = MOIT.TestConfig()
+    config = MOIT.Config()
     @testset "Ellipsoid" begin
         @testset "John" begin
             # Q = [1 0

test/switched_system.jl

@@ -10,7 +10,7 @@ using JuMP
 const MOIT = MOI.Test
 
 @testset "Switched System" begin
-    config = MOIT.TestConfig()
+    config = MOIT.Config()
     @testset "Ellipsoid" begin
         @testset "Feasible" begin
             optimize!(mock) = MOIU.mock_optimize!(
@@ -19,7 +19,7 @@ const MOIT = MOI.Test
         end
         @testset "Infeasible" begin
             optimize!(mock) = MOIU.mock_optimize!(
-                mock, MOI.INFEASIBLE, tuple(),
+                mock, MOI.INFEASIBLE, MOI.NO_SOLUTION, MOI.INFEASIBILITY_CERTIFICATE,
                 (MOI.VectorOfVariables, SetProg.SumOfSquares.PositiveSemidefinite2x2ConeTriangle) => [[0.00282108, 0.0, 0.00282108]],
                 (MOI.VectorAffineFunction{Float64}, SetProg.SumOfSquares.PositiveSemidefinite2x2ConeTriangle) => [[0.00117187, 0.0, 2.82044], [2.82044, 0.0, 0.00117187]]
             )