[MOI] Allow accessing multiple results

Project.toml

@@ -1,7 +1,7 @@
 name = "Gurobi"
 uuid = "2e9cd046-0924-5485-92f1-d5272153d98b"
 repo = "https://github.com/jump-dev/Gurobi.jl"
-version = "0.9.7"
+version = "0.9.8"
 
 [deps]
 CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"

src/MOI_wrapper.jl

@@ -2533,17 +2533,34 @@ function _has_primal_ray(model::Optimizer)
     return ret == 0
 end
 
-function MOI.get(model::Optimizer, attr::MOI.VariablePrimal, x::MOI.VariableIndex)
-    _throw_if_optimize_in_progress(model, attr)
-    MOI.check_result_index_bounds(model, attr)
-    key = model.has_unbounded_ray ? "UnbdRay" : "X"
+function _get_dbl_attr_variable(
+    model::Optimizer, key::String, x::MOI.VariableIndex
+)
     col = Cint(column(model, x) - 1)
     valueP = Ref{Cdouble}()
     ret = GRBgetdblattrelement(model, key, col, valueP)
     _check_ret(model, ret)
     return valueP[]
 end
 
+function MOI.get(
+    model::Optimizer, attr::MOI.VariablePrimal, x::MOI.VariableIndex
+)
+    _throw_if_optimize_in_progress(model, attr)
+    if model.has_unbounded_ray
+        return _get_dbl_attr_variable(model, "UnbdRay", x)
+    end
+    result_count = MOI.get(model, MOI.ResultCount())
+    if !(1 <= attr.N <= result_count)
+        throw(MOI.ResultIndexBoundsError(attr, result_count))
+    elseif attr.N == 1
+        return _get_dbl_attr_variable(model, "X", x)
+    else
+        MOI.set(model, MOI.RawParameter("SolutionNumber"), attr.N - 1)
+        return _get_dbl_attr_variable(model, "Xn", x)
+    end
+end
+
 function MOI.get(
     model::Optimizer, attr::MOI.ConstraintPrimal,
     c::MOI.ConstraintIndex{MOI.SingleVariable, <:Any}
@@ -2760,11 +2777,21 @@ end
 
 function MOI.get(model::Optimizer, attr::MOI.ObjectiveValue)
     _throw_if_optimize_in_progress(model, attr)
-    MOI.check_result_index_bounds(model, attr)
-    valueP = Ref{Cdouble}()
-    ret = GRBgetdblattr(model, "ObjVal", valueP)
-    _check_ret(model, ret)
-    return valueP[]
+    result_count = MOI.get(model, MOI.ResultCount())
+    if !(1 <= attr.result_index <= result_count)
+        throw(MOI.ResultIndexBoundsError(attr, result_count))
+    elseif attr.result_index == 1
+        valueP = Ref{Cdouble}()
+        ret = GRBgetdblattr(model, "ObjVal", valueP)
+        _check_ret(model, ret)
+        return valueP[]
+    else
+        MOI.set(model, MOI.RawParameter("SolutionNumber"), attr.result_index - 1)
+        valueP = Ref{Cdouble}()
+        ret = GRBgetdblattr(model, "PoolObjVal", valueP)
+        _check_ret(model, ret)
+        return valueP[]
+    end
 end
 
 function MOI.get(model::Optimizer, attr::MOI.ObjectiveBound)

test/MOI/MOI_wrapper.jl

@@ -530,7 +530,7 @@ end
 function test_Add_and_delete_sos_constraints()
     model = Gurobi.Optimizer(GRB_ENV)
     MOI.set(model, MOI.Silent(), true)
-    # SOS2 to model piecewise linear function   
+    # SOS2 to model piecewise linear function
     xp1 = [1.0, 2.0, 3.0]
     yp1 = [3.0, 2.0, 3.0]
     λ = MOI.add_variables(model, length(xp1))
@@ -1532,6 +1532,58 @@ function test_add_constrained_variables()
     @test MOI.get(model, MOI.ConstraintSet(), ci) == set
 end
 
+function _is_binary(x; atol = 1e-6)
+    return isapprox(x, 0; atol = atol) || isapprox(x, 1; atol = atol)
+end
+
+function test_multiple_solutions()
+    model = Gurobi.Optimizer(GRB_ENV)
+    MOI.set(model, MOI.RawParameter("OutputFlag"), 0)
+    MOI.set(model, MOI.RawParameter("Cuts"), 0)
+    MOI.set(model, MOI.RawParameter("Presolve"), 0)
+    MOI.set(model, MOI.RawParameter("PreCrush"), 0)
+    MOI.set(model, MOI.RawParameter("Heuristics"), 0)
+    N = 30
+    x = MOI.add_variables(model, N)
+    MOI.add_constraints(model, MOI.SingleVariable.(x), MOI.ZeroOne())
+    MOI.set.(model, MOI.VariablePrimalStart(), x, 0.0)
+    Random.seed!(1)
+    item_weights, item_values = rand(N), rand(N)
+    MOI.add_constraint(
+        model,
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(item_weights, x), 0.0),
+        MOI.LessThan(10.0)
+    )
+    MOI.set(
+        model,
+        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(item_values, x), 0.0)
+    )
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+    MOI.optimize!(model)
+    RC = MOI.get(model, MOI.ResultCount())
+    @test RC > 1
+    for n in [0, RC + 1]
+        @test_throws(
+            MOI.ResultIndexBoundsError,
+            MOI.get(model, MOI.VariablePrimal(n), x),
+        )
+        @test_throws(
+            MOI.ResultIndexBoundsError,
+            MOI.get(model, MOI.ObjectiveValue(n)),
+        )
+    end
+    for n = 1:RC
+        xn = MOI.get(model, MOI.VariablePrimal(n), x)
+        @test all(_is_binary, xn)
+        @test isapprox(
+            MOI.get(model, MOI.ObjectiveValue(n)),
+            item_values' * xn,
+            atol=1e-6,
+        )
+    end
+end
+
 end
 
 runtests(TestMOIWrapper)