Protect query of multiplicative duals and fix objective term on duals

benchmark/MOI_benchmarks.jl

@@ -10,120 +10,122 @@ using GLPK
 import Random
 #using SparseArrays
 using TimerOutputs
-​
+
 const MOI = MathOptInterface
 const POI = ParametricOptInterface
 SOLVER = GLPK
-​
+
 if SOLVER == GLPK
     MAX_ITER_PARAM = "it_lim"
 elseif SOLVER == Gurobi
     MAX_ITER_PARAM = "IterationLimit"
 elseif SOLVER == Xpress
     MAX_ITER_PARAM = "LPITERLIMIT"
 end
-​
+
 struct PMedianData
     num_facilities::Int
     num_customers::Int
     num_locations::Int
     customer_locations::Vector{Float64}
 end
-​
+
 # This is the LP relaxation.
 function generate_poi_problem(model, data::PMedianData, add_parameters::Bool)
     NL = data.num_locations
     NC = data.num_customers
-​
+
     ###
     ### 0 <= facility_variables <= 1
     ###
-​
+
     facility_variables = MOI.add_variables(model, NL)
-​
+
     for v in facility_variables
         MOI.add_constraint(model, v, MOI.Interval(0.0, 1.0))
     end
-​
+
     ###
     ### assignment_variables >= 0
     ###
-​
+
     assignment_variables = reshape(MOI.add_variables(model, NC * NL), NC, NL)
     for v in assignment_variables
         MOI.add_constraint(model, v, MOI.GreaterThan(0.0))
         # "Less than 1.0" constraint is redundant.
     end
-​
+
     ###
     ### Objective function
     ###
-​
+
     MOI.set(
         model,
         MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
         MOI.ScalarAffineFunction(
             [
                 MOI.ScalarAffineTerm(
                     data.customer_locations[i] - j,
-                    assignment_variables[i, j]
-                )
-                for i in 1:NC for j in 1:NL
+                    assignment_variables[i, j],
+                ) for i in 1:NC for j in 1:NL
             ],
             0.0,
         ),
     )
     MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
-​
+
     ###
     ### assignment_variables[i, j] <= facility_variables[j]
     ###
-​
+
     for i in 1:NC, j in 1:NL
         MOI.add_constraint(
             model,
             MOI.ScalarAffineFunction(
                 [
                     MOI.ScalarAffineTerm(1.0, assignment_variables[i, j]),
-                    MOI.ScalarAffineTerm(-1.0, facility_variables[j])
+                    MOI.ScalarAffineTerm(-1.0, facility_variables[j]),
                 ],
                 0.0,
             ),
             MOI.LessThan(0.0),
         )
     end
-​
+
     ###
     ### sum_j assignment_variables[i, j] = 1
     ###
-​
+
     for i in 1:NC
         MOI.add_constraint(
             model,
             MOI.ScalarAffineFunction(
                 [
-                    MOI.ScalarAffineTerm(1.0, assignment_variables[i, j])
-                    for j in 1:NL
+                    MOI.ScalarAffineTerm(1.0, assignment_variables[i, j]) for
+                    j in 1:NL
                 ],
                 0.0,
             ),
             MOI.EqualTo(1.0),
         )
     end
-​
+
     ###
     ### sum_j facility_variables[j] == num_facilities
     ###
-​
+
     if add_parameters
-        d, cd = MOI.add_constrained_variable(model, POI.Parameter(data.num_facilities))
+        d, cd = MOI.add_constrained_variable(
+            model,
+            POI.Parameter(data.num_facilities),
+        )
     end
-​
+
     if add_parameters
         MOI.add_constraint(
             model,
             MOI.ScalarAffineFunction(
-                MOI.ScalarAffineTerm.(1.0, vcat(facility_variables,d)),
+                MOI.ScalarAffineTerm.(1.0, vcat(facility_variables, d)),
                 0.0,
             ),
             MOI.EqualTo{Float64}(0),
@@ -138,11 +140,17 @@ function generate_poi_problem(model, data::PMedianData, add_parameters::Bool)
             MOI.EqualTo{Float64}(data.num_facilities),
         )
     end
-​
+
     return assignment_variables, facility_variables
 end
-​
-function solve_moi(data::PMedianData, optimizer; vector_version, params, add_parameters=false)
+
+function solve_moi(
+    data::PMedianData,
+    optimizer;
+    vector_version,
+    params,
+    add_parameters = false,
+)
     model = optimizer()
     for (param, value) in params
         MOI.set(model, param, value)
@@ -155,108 +163,164 @@ function solve_moi(data::PMedianData, optimizer; vector_version, params, add_par
     @timeit "solve" MOI.optimize!(model)
     return MOI.get(model, MOI.ObjectiveValue())
 end
-​
+
 function POI_OPTIMIZER()
     return POI.Optimizer(SOLVER.Optimizer())
 end
-​
+
 function MOI_OPTIMIZER()
     return SOLVER.Optimizer()
 end
-​
-function solve_moi_loop(data::PMedianData; vector_version, max_iters=Inf, time_limit_sec=Inf, loops)
+
+function solve_moi_loop(
+    data::PMedianData;
+    vector_version,
+    max_iters = Inf,
+    time_limit_sec = Inf,
+    loops,
+)
     params = []
     if isfinite(time_limit_sec)
         push!(params, (MOI.TimeLimitSec(), time_limit_sec))
     end
     if isfinite(max_iters)
         push!(params, (MOI.RawOptimizerAttribute(MAX_ITER_PARAM), max_iters))
     end
-    push!(params, (MOI.Silent(),true))
+    push!(params, (MOI.Silent(), true))
     s_type = vector_version ? "vector" : "scalar"
-​
+
     @timeit(
         "$(SOLVER) MOI $(s_type)",
         for _ in 1:loops
             solve_moi(
-                data, MOI_OPTIMIZER; vector_version=vector_version, params=params
+                data,
+                MOI_OPTIMIZER;
+                vector_version = vector_version,
+                params = params,
             )
         end
     )
 end
-​
-function solve_poi_no_params_loop(data::PMedianData; vector_version, max_iters=Inf, time_limit_sec=Inf, loops)
+
+function solve_poi_no_params_loop(
+    data::PMedianData;
+    vector_version,
+    max_iters = Inf,
+    time_limit_sec = Inf,
+    loops,
+)
     params = []
     if isfinite(time_limit_sec)
         push!(params, (MOI.TimeLimitSec(), time_limit_sec))
     end
     if isfinite(max_iters)
         push!(params, (MOI.RawOptimizerAttribute(MAX_ITER_PARAM), max_iters))
     end
-    push!(params, (MOI.Silent(),true))
+    push!(params, (MOI.Silent(), true))
     s_type = vector_version ? "vector" : "scalar"
     @timeit(
         "$(SOLVER) POI NO PARAMS $(s_type)",
         for _ in 1:loops
             solve_moi(
-                data,POI_OPTIMIZER; vector_version=vector_version, params=params
+                data,
+                POI_OPTIMIZER;
+                vector_version = vector_version,
+                params = params,
             )
         end
     )
 end
-​
-function solve_poi_loop(data::PMedianData; vector_version, max_iters=Inf, time_limit_sec=Inf, loops=1)
+
+function solve_poi_loop(
+    data::PMedianData;
+    vector_version,
+    max_iters = Inf,
+    time_limit_sec = Inf,
+    loops = 1,
+)
     params = []
     if isfinite(time_limit_sec)
         push!(params, (MOI.TimeLimitSec(), time_limit_sec))
     end
     if isfinite(max_iters)
         push!(params, (MOI.RawOptimizerAttribute(MAX_ITER_PARAM), max_iters))
     end
-    push!(params, (MOI.Silent(),true))
+    push!(params, (MOI.Silent(), true))
     s_type = vector_version ? "vector" : "scalar"
-​
+
     @timeit(
         "$(SOLVER) POI $(s_type)",
         for _ in 1:loops
             solve_moi(
-                data,POI_OPTIMIZER; vector_version=vector_version, params=params, add_parameters = true
+                data,
+                POI_OPTIMIZER;
+                vector_version = vector_version,
+                params = params,
+                add_parameters = true,
             )
         end
     )
 end
-​
+
 function run_benchmark(;
-    num_facilities, num_customers, num_locations, time_limit_sec, max_iters, loops
+    num_facilities,
+    num_customers,
+    num_locations,
+    time_limit_sec,
+    max_iters,
+    loops,
 )
     Random.seed!(10)
     reset_timer!()
-    data = PMedianData(num_facilities, num_customers, num_locations, rand(num_customers) .* num_locations)
+    data = PMedianData(
+        num_facilities,
+        num_customers,
+        num_locations,
+        rand(num_customers) .* num_locations,
+    )
     GC.gc()
-    solve_moi_loop(data, vector_version=false, max_iters=max_iters, time_limit_sec=time_limit_sec, loops=loops)
+    solve_moi_loop(
+        data,
+        vector_version = false,
+        max_iters = max_iters,
+        time_limit_sec = time_limit_sec,
+        loops = loops,
+    )
     GC.gc()
-    solve_poi_no_params_loop(data, vector_version=false, max_iters=max_iters, time_limit_sec=time_limit_sec, loops=loops)
+    solve_poi_no_params_loop(
+        data,
+        vector_version = false,
+        max_iters = max_iters,
+        time_limit_sec = time_limit_sec,
+        loops = loops,
+    )
     GC.gc()
-    solve_poi_loop(data, vector_version=false, max_iters=max_iters, time_limit_sec=time_limit_sec, loops=loops)
+    solve_poi_loop(
+        data,
+        vector_version = false,
+        max_iters = max_iters,
+        time_limit_sec = time_limit_sec,
+        loops = loops,
+    )
     GC.gc()
     print_timer()
-    println()
+    return println()
 end
-​
+
 run_benchmark(
-            num_facilities = 100,
-            num_customers = 100,
-            num_locations = 100,
-            time_limit_sec = 0.0001,
-            max_iters = 1,
-            loops = 1
-        )
-​
+    num_facilities = 100,
+    num_customers = 100,
+    num_locations = 100,
+    time_limit_sec = 0.0001,
+    max_iters = 1,
+    loops = 1,
+)
+
 run_benchmark(
     num_facilities = 100,
     num_customers = 100,
     num_locations = 100,
     time_limit_sec = 0.0001,
     max_iters = 1,
-    loops = 100
+    loops = 100,
 )