Merge pull request #63 from oxfordcontrol/bl/single_variable_obj

Remove support for SingleVariable objective

Project.toml

@@ -16,7 +16,7 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 BinaryProvider = "≥ 0.3.0"
-MathOptInterface = "~0.9.1"
+MathOptInterface = "~0.9.3"
 MathProgBase = "~0.5.0, ~0.6, ~0.7"
 julia = "1"
 

src/MOI_wrapper.jl

@@ -209,12 +209,7 @@ function processobjective(src::MOI.ModelLike, idxmap)
     q = zeros(n)
     if sense != MOI.FEASIBILITY_SENSE
         function_type = MOI.get(src, MOI.ObjectiveFunctionType())
-        if function_type == MOI.SingleVariable
-            fsingle = MOI.get(src, MOI.ObjectiveFunction{MOI.SingleVariable}())
-            P = spzeros(n, n)
-            q[idxmap[fsingle.variable].value] = 1
-            c = 0.
-        elseif function_type == MOI.ScalarAffineFunction{Float64}
+        if function_type == MOI.ScalarAffineFunction{Float64}
             faffine = MOI.get(src, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
             P = spzeros(n, n)
             processlinearterms!(q, faffine.terms, idxmap)
@@ -476,25 +471,15 @@ end
 MOI.get(optimizer::Optimizer, ::MOI.RawSolver) = optimizer.inner
 MOI.get(optimizer::Optimizer, ::MOI.ResultCount) = 1
 
-MOI.supports(::Optimizer, ::MOI.ObjectiveFunction{MOI.SingleVariable}) = true
 MOI.supports(::Optimizer, ::MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}) = true
 MOI.supports(::Optimizer, ::MOI.ObjectiveFunction{Quadratic}) = true
 MOI.supports(::Optimizer, ::MOI.ObjectiveSense) = true
 
-function MOI.set(optimizer::Optimizer, a::MOI.ObjectiveFunction{MOI.SingleVariable}, obj::MOI.SingleVariable)
-    MOI.is_empty(optimizer) && throw(MOI.SetAttributeNotAllowed(a))
-    optimizer.modcache.P[:] = 0
-    optimizer.modcache.q[:] = 0
-    optimizer.modcache.q[obj.variable.value] = 1
-    optimizer.objconstant = 0
-    nothing
-end
-
 function MOI.set(optimizer::Optimizer, a::MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}, obj::MOI.ScalarAffineFunction{Float64})
     MOI.is_empty(optimizer) && throw(MOI.SetAttributeNotAllowed(a))
     optimizer.modcache.P[:] = 0
     processlinearterms!(optimizer.modcache.q, obj.terms)
-    optimizer.objconstant = obj.constant
+    optimizer.objconstant = MOI.constant(obj)
     nothing
 end
 
@@ -510,7 +495,7 @@ function MOI.set(optimizer::Optimizer, a::MOI.ObjectiveFunction{Quadratic}, obj:
         cache.P[row, col] += coeff
     end
     processlinearterms!(optimizer.modcache.q, obj.affine_terms)
-    optimizer.objconstant = obj.constant
+    optimizer.objconstant = MOI.constant(obj)
     nothing
 end
 

test/MOI_wrapper.jl

@@ -73,7 +73,7 @@ const Affine = MOI.ScalarAffineFunction{Float64}
     Pmod = sparse(1.0I, n, n)
     OSQP.setup!(model4; P = Pmod, q = modcache.q.data, A = Amod, l = l, u = u, verbose = false, eps_abs = 1e-8, eps_rel = 1e-16)
     Pmod_setup_results = OSQP.solve!(model4)
-    @test Pmod_update_results.x ≈ Pmod_setup_results.x atol = 1e-8
+    @test Pmod_update_results.x ≈ Pmod_setup_results.x atol = 1e-7
 
     # Modifying the sparsity pattern is not allowed
     nzinds = map(CartesianIndex, zip(rows, cols))
@@ -303,7 +303,7 @@ term(c, x::MOI.VariableIndex, y::MOI.VariableIndex) = MOI.ScalarQuadraticTerm(c,
     # flip the feasible set around from what it was originally and minimize +x
     test_optimizer_modification(model, optimizer, idxmap, defaultoptimizer(), config) do m
         # objective
-        newobjf = MOI.SingleVariable(mapfrommodel(m, x))
+        newobjf = convert(MOI.ScalarAffineFunction{Float64}, MOI.SingleVariable(mapfrommodel(m, x)))
         F = typeof(newobjf)
         MOI.set(m, MOI.ObjectiveFunction{F}(), newobjf)
 
@@ -530,8 +530,10 @@ end
     MOI.empty!(model)
     x = MOI.add_variable(model)
     c = MOI.add_constraint(model, 1.0MOI.SingleVariable(x), MOI.GreaterThan(2.0))
+    fx = MOI.SingleVariable(x)
+    obj = convert(MOI.ScalarAffineFunction{Float64}, fx)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
-    MOI.set(model, MOI.ObjectiveFunction{MOI.SingleVariable}(), MOI.SingleVariable(x))
+    MOI.set(model, MOI.ObjectiveFunction{typeof(obj)}(), obj)
 
     MOI.copy_to(optimizer, model)
     inner = MOI.get(optimizer, MOI.RawSolver())