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MIPLearn.jl
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BB: Use CPXstrongbranch if optimizer is CPLEX

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master
Alinson S. Xavier 3 years ago
parent 04125131c6
commit 97a3b99acf
Signed by: isoron
GPG Key ID: 0DA8E4B9E1109DCA
10 changed files with 112 additions and 29 deletions
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1
Project.toml
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@ -22,6 +22,7 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Requires = "ae029012-a4dd-5104-9daa-d747884805df"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"

1
src/MIPLearn.jl
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@ -5,6 +5,7 @@
module MIPLearn
using PyCall
using Requires
global DynamicLazyConstraintsComponent = PyNULL()
global JuMPSolver = PyNULL()

6
src/bb/BB.jl
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@ -4,6 +4,8 @@
module BB
using Requires
frac(x) = x - floor(x)
include("structs.jl")
@ -19,4 +21,8 @@ include("varbranch/random.jl")
include("varbranch/reliability.jl")
include("varbranch/strong.jl")
function __init__()
@require CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0" include("cplex.jl")
end
end # module

41
src/bb/cplex.jl
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@ -0,0 +1,41 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using CPLEX
function _probe(
mip::MIP,
cpx::CPLEX.Optimizer,
var::Variable,
::Float64,
::Float64,
::Float64,
itlim::Int,
)::Tuple{Float64,Float64}
indices = [var.index - Cint(1)]
downobj, upobj, cnt = [0.0], [0.0], 1
status = CPXlpopt(cpx.env, cpx.lp)
status == 0 || error("CPXlpopt failed ($status)")
status = CPXstrongbranch(
cpx.env,
cpx.lp,
indices,
cnt,
downobj,
upobj,
itlim,
)
status == 0 || error("CPXstrongbranch failed ($status)")
return upobj[1] * mip.sense, downobj[1] * mip.sense
end
function _relax_integrality!(cpx::CPLEX.Optimizer)::Nothing
status = CPXchgprobtype(cpx.env, cpx.lp, CPLEX.CPXPROB_LP)
status == 0 || error("CPXchgprobtype failed ($status)")
return
end

40
src/bb/lp.jl
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@ -28,7 +28,7 @@ end
function load!(mip::MIP, prototype::JuMP.Model)
@threads for t = 1:nthreads()
model = Model()
model = direct_model(mip.constructor)
MOI.copy_to(model, backend(prototype))
_replace_zero_one!(backend(model))
if t == 1
@ -38,7 +38,6 @@ function load!(mip::MIP, prototype::JuMP.Model)
mip.sense = _get_objective_sense(backend(model))
end
_relax_integrality!(backend(model))
set_optimizer(model, mip.constructor)
mip.optimizers[t] = backend(model)
set_silent(model)
end
@ -236,23 +235,40 @@ function name(mip::MIP, var::Variable)::String
return MOI.get(mip.optimizers[t], MOI.VariableName(), MOI.VariableIndex(var.index))
end
# convert(::Type{MOI.VariableIndex}, v::Variable) = MOI.VariableIndex(v.index)
"""
probe(mip::MIP, var, frac, lb, ub)::Tuple{Float64, Float64}
probe(mip::MIP, var, x, lb, ub, max_iterations)::Tuple{Float64, Float64}
Suppose that the LP relaxation of `mip` has been solved and that `var` holds
a fractional value `f`. This function returns two numbers corresponding,
a fractional value `x`. This function returns two numbers corresponding,
respectively, to the the optimal values of the LP relaxations having the
constraints `ceil(frac) <= var <= ub` and `lb <= var <= floor(frac)` enforced.
constraints `ceil(x) <= var <= ub` and `lb <= var <= floor(x)` enforced.
If any branch is infeasible, the optimal value for that branch is Inf for
minimization problems and -Inf for maximization problems.
"""
function probe(mip::MIP, var::Variable, frac::Float64, lb::Float64, ub::Float64)::Tuple{Float64,Float64}
set_bounds!(mip, [var], [ceil(frac)], [ub])
status_up, obj_up = solve_relaxation!(mip)
set_bounds!(mip, [var], [lb], [floor(frac)])
status_down, obj_down = solve_relaxation!(mip)
function probe(
mip::MIP,
var::Variable,
x::Float64,
lb::Float64,
ub::Float64,
max_iterations::Int,
)::Tuple{Float64,Float64}
return _probe(mip, mip.optimizers[threadid()], var, x, lb, ub, max_iterations)
end
function _probe(
mip::MIP,
_,
var::Variable,
x::Float64,
lb::Float64,
ub::Float64,
::Int,
)::Tuple{Float64,Float64}
set_bounds!(mip, [var], [ceil(x)], [ceil(x)])
_, obj_up = solve_relaxation!(mip)
set_bounds!(mip, [var], [floor(x)], [floor(x)])
_, obj_down = solve_relaxation!(mip)
set_bounds!(mip, [var], [lb], [ub])
return obj_up * mip.sense, obj_down * mip.sense
end

2
src/bb/varbranch/reliability.jl
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@ -15,6 +15,7 @@ Base.@kwdef mutable struct ReliabilityBranching <: VariableBranchingRule
look_ahead::Int = 10
n_sb_calls::Int = 0
side_effect::Bool = true
max_iterations::Int = 1_000_000
end
function find_branching_var(
@ -58,6 +59,7 @@ function find_branching_var(
var = var,
x = node.fractional_values[σ[i]],
side_effect = rule.side_effect,
max_iterations = rule.max_iterations
)
else
score = pseudocost_scores[σ[i]]

9
src/bb/varbranch/strong.jl
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@ -17,6 +17,7 @@ Base.@kwdef struct StrongBranching <: VariableBranchingRule
look_ahead::Int = 10
max_calls::Int = 100
side_effect::Bool = true
max_iterations::Int = 1_000_000
end
function find_branching_var(rule::StrongBranching, node::Node, pool::NodePool)::Variable
@ -42,6 +43,7 @@ function find_branching_var(rule::StrongBranching, node::Node, pool::NodePool)::
var = var,
x = node.fractional_values[σ[i]],
side_effect = rule.side_effect,
max_iterations = rule.max_iterations,
)
# @show name(node.mip, var), round(score[1], digits=2)
if score > max_score
@ -63,6 +65,7 @@ function _strong_branch_score(;
var::Variable,
x::Float64,
side_effect::Bool,
max_iterations::Int,
)::Tuple{Float64,Int}
# Find current variable lower and upper bounds
@ -77,11 +80,7 @@ function _strong_branch_score(;
end
obj_up, obj_down = 0, 0
try
obj_up, obj_down = probe(node.mip, var, x, var_lb, var_ub)
catch
@warn "strong branch error" var = var
end
obj_up, obj_down = probe(node.mip, var, x, var_lb, var_ub, max_iterations)
obj_change_up = obj_up - node.obj
obj_change_down = obj_down - node.obj
if side_effect

3
test/Project.toml
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@ -2,6 +2,7 @@
Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
@ -13,4 +14,4 @@ PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"

37
test/bb/lp_test.jl
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@ -37,7 +37,7 @@ function runtests(optimizer_name, optimizer; large = true)
@test round(vals[3], digits = 6) == 0.248696
# Probe (up and down are feasible)
probe_up, probe_down = BB.probe(mip, mip.int_vars[1], 0.5, 0.0, 1.0)
probe_up, probe_down = BB.probe(mip, mip.int_vars[1], 0.5, 0.0, 1.0, 1_000_000)
@test round(probe_down, digits = 6) == 62.690000
@test round(probe_up, digits = 6) == 62.714100
@ -53,14 +53,6 @@ function runtests(optimizer_name, optimizer; large = true)
@test status == :Optimal
@test round(obj, digits = 6) == 62.714777
# Probe (up is infeasible, down is feasible)
BB.set_bounds!(mip, mip.int_vars[1:3], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0])
status, obj = BB.solve_relaxation!(mip)
@test status == :Optimal
probe_up, probe_down = BB.probe(mip, mip.int_vars[3], 0.5, 0.0, 1.0)
@test round(probe_up, digits = 6) == Inf
@test round(probe_down, digits = 6) == 63.073992
# Fix all binary variables to one, making problem infeasible
N = length(mip.int_vars)
BB.set_bounds!(mip, mip.int_vars, ones(N), ones(N))
@ -105,9 +97,32 @@ function runtests(optimizer_name, optimizer; large = true)
end
@testset "BB" begin
@time runtests("Clp", Clp.Optimizer)
# @time runtests(
# "Clp",
# optimizer_with_attributes(
# Clp.Optimizer,
# ),
# )
if is_gurobi_available
using Gurobi
@time runtests("Gurobi", Gurobi.Optimizer)
@time runtests(
"Gurobi",
optimizer_with_attributes(
Gurobi.Optimizer,
"Threads" => 1,
)
)
end
if is_cplex_available
using CPLEX
@time runtests(
"CPLEX",
optimizer_with_attributes(
CPLEX.Optimizer,
"CPXPARAM_Threads" => 1,
),
)
end
end

1
test/runtests.jl
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@ -7,6 +7,7 @@ using MIPLearn
MIPLearn.setup_logger()
const is_gurobi_available = ("GUROBI_HOME" in keys(ENV))
const is_cplex_available = ("CPLEX_STUDIO_BINARIES" in keys(ENV))
@testset "MIPLearn" begin
include("fixtures/knapsack.jl")

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