Change MOA.SolutionLimit to be an alias for MOA.SolutionLimit

Author: odow

Project.toml

@@ -18,7 +18,7 @@ Combinatorics = "1"
 HiGHS = "1"
 Ipopt = "1"
 JSON = "0.21"
-MathOptInterface = "1.19"
+MathOptInterface = "1.21"
 Polyhedra = "0.8"
 Test = "1"
 julia = "1.10"
@@ -27,8 +27,8 @@ julia = "1.10"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Polyhedra = "67491407-f73d-577b-9b50-8179a7c68029"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["HiGHS", "Ipopt", "JSON", "Test", "Polyhedra"]
+test = ["HiGHS", "Ipopt", "JSON", "Polyhedra", "Test"]

README.md

@@ -37,7 +37,7 @@ import HiGHS
 import MultiObjectiveAlgorithms as MOA
 model = JuMP.Model(() -> MOA.Optimizer(HiGHS.Optimizer))
 set_attribute(model, MOA.Algorithm(), MOA.Dichotomy())
-set_attribute(model, MOA.SolutionLimit(), 4)
+set_attribute(model, MOI.SolutionLimit(), 4)
 ```
 
 For worked examples, see the [Simple multi-objective examples](https://jump.dev/JuMP.jl/stable/tutorials/linear/multi_objective_examples/)
@@ -82,7 +82,7 @@ the solution process.
  * `MOA.ObjectivePriority(index::Int)`
  * `MOA.ObjectiveRelativeTolerance(index::Int)`
  * `MOA.ObjectiveWeight(index::Int)`
- * `MOA.SolutionLimit()`
+ * `MOI.SolutionLimit()`
  * `MOI.TimeLimitSec()`
 
 Query the number of scalar subproblems that were solved using

src/MultiObjectiveAlgorithms.jl

@@ -263,16 +263,9 @@ function MOI.get(model::Optimizer, attr::AbstractAlgorithmAttribute)
     return MOI.get(model.algorithm, attr)
 end
 
-"""
-    SolutionLimit <: AbstractAlgorithmAttribute -> Int
-
-Terminate the algorithm once the set number of solutions have been found.
-
-Defaults to `typemax(Int)`.
-"""
-struct SolutionLimit <: AbstractAlgorithmAttribute end
+const SolutionLimit = MOI.SolutionLimit
 
-default(::SolutionLimit) = typemax(Int)
+default(::MOI.SolutionLimit) = typemax(Int)
 
 """
     ObjectivePriority(index::Int) <: AbstractAlgorithmAttribute -> Int

src/algorithms/Dichotomy.jl

@@ -16,7 +16,7 @@ Science 25(1), 73-78.
  * `MOI.TimeLimitSec()`: terminate if the time limit is exceeded and return the
    list of current solutions.
 
- * `MOA.SolutionLimit()`: terminate once this many solutions have been found.
+ * `MOI.SolutionLimit()`: terminate once this many solutions have been found.
 """
 mutable struct Dichotomy <: AbstractAlgorithm
     solution_limit::Union{Nothing,Int}
@@ -39,18 +39,18 @@ trade‐offs: An algorithm for bicriterion problems. Water Resources Research,
 
 ## Supported optimizer attributes
 
- * `MOA.SolutionLimit()`
+ * `MOI.SolutionLimit()`
 """
 NISE() = Dichotomy()
 
-MOI.supports(::Dichotomy, ::SolutionLimit) = true
+MOI.supports(::Dichotomy, ::MOI.SolutionLimit) = true
 
-function MOI.set(alg::Dichotomy, ::SolutionLimit, value)
+function MOI.set(alg::Dichotomy, ::MOI.SolutionLimit, value)
     alg.solution_limit = value
     return
 end
 
-function MOI.get(alg::Dichotomy, attr::SolutionLimit)
+function MOI.get(alg::Dichotomy, attr::MOI.SolutionLimit)
     return something(alg.solution_limit, default(alg, attr))
 end
 
@@ -100,7 +100,7 @@ function optimize_multiobjective!(algorithm::Dichotomy, model::Optimizer)
     if !(solutions[0.0] ≈ solutions[1.0])
         push!(queue, (0.0, 1.0))
     end
-    limit = MOI.get(algorithm, SolutionLimit())
+    limit = MOI.get(algorithm, MOI.SolutionLimit())
     status = MOI.OPTIMAL
     while length(queue) > 0 && length(solutions) < limit
         if (ret = _check_premature_termination(model, start_time)) !== nothing

src/algorithms/EpsilonConstraint.jl

@@ -16,12 +16,15 @@ bi-objective programs.
    default is `1`, so that for a pure integer program this algorithm will
    enumerate all non-dominated solutions.
 
- * `MOA.SolutionLimit()`: if this attribute is set then, instead of using the
+ * `MOI.SolutionLimit()`: if this attribute is set then, instead of using the
    `MOA.EpsilonConstraintStep`, with a slight abuse of notation,
    `EpsilonConstraint` divides the width of the first-objective's domain in
-   objective space by `SolutionLimit` to obtain the epsilon to use when
-   iterating. Thus, there can be at most `SolutionLimit` solutions returned, but
-   there may be fewer.
+   objective space by `MOI.SolutionLimit` to obtain the epsilon to use when
+   iterating. Thus, there can be at most `MOI.SolutionLimit` solutions returned,
+   but there may be fewer.
+
+ * `MOI.TimeLimitSec()`: terminate if the time limit is exceeded and return the
+   list of current solutions.
 """
 mutable struct EpsilonConstraint <: AbstractAlgorithm
     solution_limit::Union{Nothing,Int}
@@ -30,14 +33,14 @@ mutable struct EpsilonConstraint <: AbstractAlgorithm
     EpsilonConstraint() = new(nothing, nothing)
 end
 
-MOI.supports(::EpsilonConstraint, ::SolutionLimit) = true
+MOI.supports(::EpsilonConstraint, ::MOI.SolutionLimit) = true
 
-function MOI.set(alg::EpsilonConstraint, ::SolutionLimit, value)
+function MOI.set(alg::EpsilonConstraint, ::MOI.SolutionLimit, value)
     alg.solution_limit = value
     return
 end
 
-function MOI.get(alg::EpsilonConstraint, attr::SolutionLimit)
+function MOI.get(alg::EpsilonConstraint, attr::MOI.SolutionLimit)
     return something(alg.solution_limit, default(alg, attr))
 end
 
@@ -91,8 +94,8 @@ function minimize_multiobjective!(
     model.ideal_point .= min.(solution_1[1].y, solution_2[1].y)
     # Compute the epsilon that we will be incrementing by each iteration
     ε = MOI.get(algorithm, EpsilonConstraintStep())
-    n_points = MOI.get(algorithm, SolutionLimit())
-    if n_points != default(algorithm, SolutionLimit())
+    n_points = MOI.get(algorithm, MOI.SolutionLimit())
+    if n_points != default(algorithm, MOI.SolutionLimit())
         ε = abs(right - left) / (n_points - 1)
     end
     solutions = SolutionPoint[only(solution_1), only(solution_2)]

src/algorithms/RandomWeighting.jl

@@ -14,7 +14,7 @@ random weights.
  * `MOI.TimeLimitSec()`: terminate if the time limit is exceeded and return the
    list of current solutions.
 
- * `MOA.SolutionLimit()`: terminate once this many solutions have been found.
+ * `MOI.SolutionLimit()`: terminate once this many solutions have been found.
 
 At least one of these two limits must be set.
 """
@@ -23,21 +23,21 @@ mutable struct RandomWeighting <: AbstractAlgorithm
     RandomWeighting() = new(nothing)
 end
 
-MOI.supports(::RandomWeighting, ::SolutionLimit) = true
+MOI.supports(::RandomWeighting, ::MOI.SolutionLimit) = true
 
-function MOI.set(alg::RandomWeighting, ::SolutionLimit, value)
+function MOI.set(alg::RandomWeighting, ::MOI.SolutionLimit, value)
     alg.solution_limit = value
     return
 end
 
-function MOI.get(alg::RandomWeighting, attr::SolutionLimit)
+function MOI.get(alg::RandomWeighting, attr::MOI.SolutionLimit)
     return something(alg.solution_limit, default(alg, attr))
 end
 
 function optimize_multiobjective!(algorithm::RandomWeighting, model::Optimizer)
     if MOI.get(model, MOI.TimeLimitSec()) === nothing &&
        algorithm.solution_limit === nothing
-        error("At least `MOI.TimeLimitSec` or `MOA.SolutionLimit` must be set")
+        error("At least `MOI.TimeLimitSec` or `MOI.SolutionLimit` must be set")
     end
     start_time = time()
     solutions = SolutionPoint[]

test/algorithms/Dichotomy.jl

@@ -28,12 +28,12 @@ end
 function test_Dichotomy_SolutionLimit()
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.Dichotomy())
-    @test MOI.supports(MOA.Dichotomy(), MOA.SolutionLimit())
-    @test MOI.supports(model, MOA.SolutionLimit())
-    @test MOI.get(model, MOA.SolutionLimit()) ==
-          MOA.default(MOA.SolutionLimit())
-    MOI.set(model, MOA.SolutionLimit(), 1)
-    @test MOI.get(model, MOA.SolutionLimit()) == 1
+    @test MOI.supports(MOA.Dichotomy(), MOI.SolutionLimit())
+    @test MOI.supports(model, MOI.SolutionLimit())
+    @test MOI.get(model, MOI.SolutionLimit()) ==
+          MOA.default(MOI.SolutionLimit())
+    MOI.set(model, MOI.SolutionLimit(), 1)
+    @test MOI.get(model, MOI.SolutionLimit()) == 1
     return
 end
 
@@ -371,7 +371,7 @@ function test_quadratic()
     N = 2
     model = MOA.Optimizer(Ipopt.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.Dichotomy())
-    MOI.set(model, MOA.SolutionLimit(), 10)
+    MOI.set(model, MOI.SolutionLimit(), 10)
     MOI.set(model, MOI.Silent(), true)
     w = MOI.add_variables(model, N)
     MOI.add_constraint.(model, w, MOI.GreaterThan(0.0))

test/algorithms/EpsilonConstraint.jl

@@ -40,7 +40,7 @@ function test_biobjective_knapsack()
     w = [80, 87, 68, 72, 66, 77, 99, 85, 70, 93, 98, 72, 100, 89, 67, 86, 91]
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    MOI.set(model, MOA.SolutionLimit(), 100)
+    MOI.set(model, MOI.SolutionLimit(), 100)
     MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, length(w))
     MOI.add_constraint.(model, x, MOI.ZeroOne())
@@ -170,7 +170,7 @@ function test_biobjective_knapsack_min()
     w = [80, 87, 68, 72, 66, 77, 99, 85, 70, 93, 98, 72, 100, 89, 67, 86, 91]
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    MOI.set(model, MOA.SolutionLimit(), 100)
+    MOI.set(model, MOI.SolutionLimit(), 100)
     MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, length(w))
     MOI.add_constraint.(model, x, MOI.ZeroOne())
@@ -215,8 +215,8 @@ function test_biobjective_knapsack_min_solution_limit()
     w = [80, 87, 68, 72, 66, 77, 99, 85, 70, 93, 98, 72, 100, 89, 67, 86, 91]
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    @test MOI.supports(model, MOA.SolutionLimit())
-    MOI.set(model, MOA.SolutionLimit(), 3)
+    @test MOI.supports(model, MOI.SolutionLimit())
+    MOI.set(model, MOI.SolutionLimit(), 3)
     MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, length(w))
     MOI.add_constraint.(model, x, MOI.ZeroOne())
@@ -315,7 +315,7 @@ function test_quadratic()
     N = 2
     model = MOA.Optimizer(Ipopt.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    MOI.set(model, MOA.SolutionLimit(), 10)
+    MOI.set(model, MOI.SolutionLimit(), 10)
     MOI.set(model, MOI.Silent(), true)
     w = MOI.add_variables(model, N)
     MOI.add_constraint.(model, w, MOI.GreaterThan(0.0))
@@ -343,7 +343,7 @@ function test_poor_numerics()
     N = 2
     model = MOA.Optimizer(Ipopt.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    MOI.set(model, MOA.SolutionLimit(), 10)
+    MOI.set(model, MOI.SolutionLimit(), 10)
     MOI.set(model, MOI.Silent(), true)
     w = MOI.add_variables(model, N)
     sharpe = MOI.add_variable(model)
@@ -387,7 +387,7 @@ function test_vectornonlinearfunction()
     N = 2
     model = MOA.Optimizer(Ipopt.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
-    MOI.set(model, MOA.SolutionLimit(), 10)
+    MOI.set(model, MOI.SolutionLimit(), 10)
     MOI.set(model, MOI.Silent(), true)
     w = MOI.add_variables(model, N)
     MOI.add_constraint.(model, w, MOI.GreaterThan(0.0))

test/algorithms/RandomWeighting.jl

@@ -33,7 +33,7 @@ function test_error_attribute()
     MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
     @test_throws(
         ErrorException(
-            "At least `MOI.TimeLimitSec` or `MOA.SolutionLimit` must be set",
+            "At least `MOI.TimeLimitSec` or `MOI.SolutionLimit` must be set",
         ),
         MOI.optimize!(model),
     )
@@ -50,7 +50,7 @@ function test_knapsack_min()
     w = Float64[557, 898, 148, 63, 78, 964, 246, 662, 386, 272]
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.RandomWeighting())
-    MOI.set(model, MOA.SolutionLimit(), 3)
+    MOI.set(model, MOI.SolutionLimit(), 3)
     MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, n)
     MOI.add_constraint.(model, x, MOI.ZeroOne())
@@ -97,7 +97,7 @@ function test_knapsack_max()
     w = Float64[557, 898, 148, 63, 78, 964, 246, 662, 386, 272]
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.RandomWeighting())
-    MOI.set(model, MOA.SolutionLimit(), 3)
+    MOI.set(model, MOI.SolutionLimit(), 3)
     MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, n)
     MOI.add_constraint.(model, x, MOI.ZeroOne())
@@ -174,8 +174,8 @@ function test_unbounded()
     model = MOA.Optimizer(HiGHS.Optimizer)
     MOI.set(model, MOA.Algorithm(), MOA.RandomWeighting())
     MOI.set(model, MOI.Silent(), true)
-    @test MOI.supports(model, MOA.SolutionLimit())
-    MOI.set(model, MOA.SolutionLimit(), 10)
+    @test MOI.supports(model, MOI.SolutionLimit())
+    MOI.set(model, MOI.SolutionLimit(), 10)
     x = MOI.add_variables(model, 2)
     MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
     f = MOI.Utilities.operate(vcat, Float64, 1.0 .* x...)

test/test_model.jl

@@ -234,6 +234,11 @@ function test_check_interrupt()
     return
 end
 
+function test_SolutionLimit()
+    @test MOI.SolutionLimit === MOA.SolutionLimit
+    return
+end
+
 end  # module
 
 TestModel.run_tests()