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MOI_benchmarks.jl
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MOI_benchmarks.jl
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push!(LOAD_PATH, "./src")
using ParametricOptInterface
using MathOptInterface
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
],
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])
],
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
],
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))
end
if add_parameters
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.(1.0, vcat(facility_variables,d)),
0.0,
),
MOI.EqualTo{Float64}(0),
)
else
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.(1.0, facility_variables),
0.0,
),
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)
model = optimizer()
for (param, value) in params
MOI.set(model, param, value)
end
@timeit "generate" x, y = if vector_version
generate_poi_problem_vector(model, data, add_parameters)
else
generate_poi_problem(model, data, add_parameters)
end
@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)
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))
s_type = vector_version ? "vector" : "scalar"
@timeit(
"$(SOLVER) MOI $(s_type)",
for _ in 1:loops
solve_moi(
data, MOI_OPTIMIZER; vector_version=vector_version, params=params
)
end
)
end
function solve_poi_no_params_loop(data::PMedianData; vector_version, max_iters=Inf, time_limit_sec=Inf, loops)
params = []
if isfinite(time_limit_sec)
push!(params, (MOI.TimeLimitSec(), time_limit_sec))
end
if isfinite(max_iters)
push!(params, (MOI.RawOptimizerAttribute(MAX_ITER_PARAM), max_iters))
end
push!(params, (MOI.Silent(),true))
s_type = vector_version ? "vector" : "scalar"
@timeit(
"$(SOLVER) POI NO PARAMS $(s_type)",
for _ in 1:loops
solve_moi(
data,POI_OPTIMIZER; vector_version=vector_version, params=params
)
end
)
end
function solve_poi_loop(data::PMedianData; vector_version, max_iters=Inf, time_limit_sec=Inf, loops=1)
params = []
if isfinite(time_limit_sec)
push!(params, (MOI.TimeLimitSec(), time_limit_sec))
end
if isfinite(max_iters)
push!(params, (MOI.RawOptimizerAttribute(MAX_ITER_PARAM), max_iters))
end
push!(params, (MOI.Silent(),true))
s_type = vector_version ? "vector" : "scalar"
@timeit(
"$(SOLVER) POI $(s_type)",
for _ in 1:loops
solve_moi(
data,POI_OPTIMIZER; vector_version=vector_version, params=params, add_parameters = true
)
end
)
end
function run_benchmark(;
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)
GC.gc()
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)
GC.gc()
solve_poi_loop(data, vector_version=false, max_iters=max_iters, time_limit_sec=time_limit_sec, loops=loops)
GC.gc()
print_timer()
println()
end
run_benchmark(
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
)