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This commit is contained in:
Alinson S. Xavier
2024-01-11 11:26:45 -06:00
parent 1ea432fb57
commit 20d6570ea6
20 changed files with 532 additions and 142 deletions
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4
src/BB/log.jl
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@@ -31,9 +31,9 @@ function print_progress(
node::Node; node::Node;
time_elapsed::Float64, time_elapsed::Float64,
print_interval::Int, print_interval::Int,
primal_update::Bool, primal_update::Bool
)::Nothing )::Nothing
if (pool.processed % print_interval == 0) || isempty(pool.pending) || primal_update if (pool.processed % print_interval == 0) || isempty(pool.pending)
if isempty(node.branch_vars) if isempty(node.branch_vars)
branch_var_name = "---" branch_var_name = "---"
branch_lb = "---" branch_lb = "---"

28
src/BB/nodepool.jl
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@@ -8,10 +8,10 @@ import Base.Threads: threadid
function take( function take(
pool::NodePool; pool::NodePool;
suggestions::Array{Node} = [], suggestions::Array{Node}=[],
time_remaining::Float64, time_remaining::Float64,
gap_limit::Float64, gap_limit::Float64,
node_limit::Int, node_limit::Int
)::Union{Symbol,Node} )::Union{Symbol,Node}
t = threadid() t = threadid()
lock(pool.lock) do lock(pool.lock) do
@@ -53,8 +53,8 @@ function offer(
pool::NodePool; pool::NodePool;
parent_node::Union{Nothing,Node}, parent_node::Union{Nothing,Node},
child_nodes::Vector{Node}, child_nodes::Vector{Node},
time_elapsed::Float64 = 0.0, time_elapsed::Float64=0.0,
print_interval::Int = 100, print_interval::Int=100
)::Nothing )::Nothing
lock(pool.lock) do lock(pool.lock) do
primal_update = false primal_update = false
@@ -101,15 +101,16 @@ function offer(
# Update branching variable history # Update branching variable history
branch_var = child_nodes[1].branch_vars[end] branch_var = child_nodes[1].branch_vars[end]
offset = findfirst(isequal(branch_var), parent_node.fractional_variables) offset = findfirst(isequal(branch_var), parent_node.fractional_variables)
if offset !== nothing
x = parent_node.fractional_values[offset] x = parent_node.fractional_values[offset]
obj_change_up = child_nodes[1].obj - parent_node.obj obj_change_up = child_nodes[1].obj - parent_node.obj
obj_change_down = child_nodes[2].obj - parent_node.obj obj_change_down = child_nodes[2].obj - parent_node.obj
_update_var_history( _update_var_history(
pool = pool, pool=pool,
var = branch_var, var=branch_var,
x = x, x=x,
obj_change_down = obj_change_down, obj_change_down=obj_change_down,
obj_change_up = obj_change_up, obj_change_up=obj_change_up,
) )
# Update global history # Update global history
pool.history.avg_pseudocost_up = pool.history.avg_pseudocost_up =
@@ -117,14 +118,15 @@ function offer(
pool.history.avg_pseudocost_down = pool.history.avg_pseudocost_down =
mean(vh.pseudocost_down for vh in values(pool.var_history)) mean(vh.pseudocost_down for vh in values(pool.var_history))
end end
end
for node in child_nodes for node in child_nodes
print_progress( print_progress(
pool, pool,
node, node,
time_elapsed = time_elapsed, time_elapsed=time_elapsed,
print_interval = print_interval, print_interval=print_interval,
primal_update = isfinite(node.obj) && isempty(node.fractional_variables), primal_update=isfinite(node.obj) && isempty(node.fractional_variables),
) )
end end
end end
@@ -136,7 +138,7 @@ function _update_var_history(;
var::Variable, var::Variable,
x::Float64, x::Float64,
obj_change_down::Float64, obj_change_down::Float64,
obj_change_up::Float64, obj_change_up::Float64
)::Nothing )::Nothing
# Create new history entry # Create new history entry
if var ∉ keys(pool.var_history) if var ∉ keys(pool.var_history)

98
src/BB/optimize.jl
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@@ -10,16 +10,22 @@ import ..H5File
function solve!( function solve!(
mip::MIP; mip::MIP;
time_limit::Float64 = Inf, time_limit::Float64=Inf,
node_limit::Int = typemax(Int), node_limit::Int=typemax(Int),
gap_limit::Float64 = 1e-4, gap_limit::Float64=1e-4,
print_interval::Int = 5, print_interval::Int=5,
initial_primal_bound::Float64 = Inf, initial_primal_bound::Float64=Inf,
branch_rule::VariableBranchingRule = ReliabilityBranching(), branch_rule::VariableBranchingRule=ReliabilityBranching(),
enable_plunging = true, enable_plunging=true,
)::NodePool replay=nothing
)::Tuple{NodePool,ReplayInfo}
if replay === nothing
replay = ReplayInfo()
end
time_initial = time() time_initial = time()
pool = NodePool(mip = mip) pool = NodePool(mip=mip, next_index=replay.next_index)
pool.primal_bound = initial_primal_bound pool.primal_bound = initial_primal_bound
root_node = _create_node(mip) root_node = _create_node(mip)
@@ -34,9 +40,9 @@ function solve!(
offer( offer(
pool, pool,
parent_node = nothing, parent_node=nothing,
child_nodes = [root_node], child_nodes=[root_node],
print_interval = print_interval, print_interval=print_interval,
) )
@threads for t = 1:nthreads() @threads for t = 1:nthreads()
child_one, child_zero, suggestions = nothing, nothing, Node[] child_one, child_zero, suggestions = nothing, nothing, Node[]
@@ -47,10 +53,10 @@ function solve!(
end end
node = take( node = take(
pool, pool,
suggestions = suggestions, suggestions=suggestions,
time_remaining = time_limit - time_elapsed, time_remaining=time_limit - time_elapsed,
node_limit = node_limit, node_limit=node_limit,
gap_limit = gap_limit, gap_limit=gap_limit,
) )
if node == :END if node == :END
break break
@@ -64,10 +70,25 @@ function solve!(
@assert status == :Optimal @assert status == :Optimal
_unset_node_bounds(node) _unset_node_bounds(node)
if node.index in keys(replay.node_decisions)
decision = replay.node_decisions[node.index]
ids = decision.ids
branch_var = decision.branch_var
var_value = decision.var_value
else
# Find branching variable # Find branching variable
ids = generate_indices(pool, 2) ids = generate_indices(pool, 2)
branch_var = find_branching_var(branch_rule, node, pool) branch_var = find_branching_var(branch_rule, node, pool)
# Query current fractional value
offset = findfirst(isequal(branch_var), node.fractional_variables)
var_value = node.fractional_values[offset]
# Update replay
decision = ReplayNodeDecision(; branch_var, var_value, ids)
replay.node_decisions[node.index] = decision
end
# Find current variable lower and upper bounds # Find current variable lower and upper bounds
offset = findfirst(isequal(branch_var), mip.int_vars) offset = findfirst(isequal(branch_var), mip.int_vars)
var_lb = mip.int_vars_lb[offset] var_lb = mip.int_vars_lb[offset]
@@ -79,46 +100,43 @@ function solve!(
end end
end end
# Query current fractional value
offset = findfirst(isequal(branch_var), node.fractional_variables)
var_value = node.fractional_values[offset]
child_zero = _create_node( child_zero = _create_node(
mip, mip,
index = ids[2], index=ids[2],
parent = node, parent=node,
branch_var = branch_var, branch_var=branch_var,
branch_var_lb = var_lb, branch_var_lb=var_lb,
branch_var_ub = floor(var_value), branch_var_ub=floor(var_value),
) )
child_one = _create_node( child_one = _create_node(
mip, mip,
index = ids[1], index=ids[1],
parent = node, parent=node,
branch_var = branch_var, branch_var=branch_var,
branch_var_lb = ceil(var_value), branch_var_lb=ceil(var_value),
branch_var_ub = var_ub, branch_var_ub=var_ub,
) )
offer( offer(
pool, pool,
parent_node = node, parent_node=node,
child_nodes = [child_one, child_zero], child_nodes=[child_one, child_zero],
time_elapsed = time_elapsed, time_elapsed=time_elapsed,
print_interval = print_interval, print_interval=print_interval,
) )
end end
end end
end end
return pool replay.next_index = pool.next_index
return pool, replay
end end
function _create_node( function _create_node(
mip; mip;
index::Int = 0, index::Int=0,
parent::Union{Nothing,Node} = nothing, parent::Union{Nothing,Node}=nothing,
branch_var::Union{Nothing,Variable} = nothing, branch_var::Union{Nothing,Variable}=nothing,
branch_var_lb::Union{Nothing,Float64} = nothing, branch_var_lb::Union{Nothing,Float64}=nothing,
branch_var_ub::Union{Nothing,Float64} = nothing, branch_var_ub::Union{Nothing,Float64}=nothing
)::Node )::Node
if parent === nothing if parent === nothing
branch_vars = Variable[] branch_vars = Variable[]

11
src/BB/structs.jl
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@@ -72,3 +72,14 @@ Base.@kwdef mutable struct NodePool
history::History = History() history::History = History()
var_history::Dict{Variable,VariableHistory} = Dict() var_history::Dict{Variable,VariableHistory} = Dict()
end end
Base.@kwdef struct ReplayNodeDecision
branch_var
var_value
ids
end
Base.@kwdef mutable struct ReplayInfo
node_decisions::Dict{Int,ReplayNodeDecision} = Dict()
next_index::Int = 1
end

114
src/solvers/jump.jl
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@@ -36,13 +36,14 @@ function _add_constrs(
end end
function _extract_after_load(model::JuMP.Model, h5) function _extract_after_load(model::JuMP.Model, h5)
@info "_extract_after_load"
if JuMP.objective_sense(model) == MOI.MIN_SENSE if JuMP.objective_sense(model) == MOI.MIN_SENSE
h5.put_scalar("static_sense", "min") h5.put_scalar("static_sense", "min")
else else
h5.put_scalar("static_sense", "max") h5.put_scalar("static_sense", "max")
end end
_extract_after_load_vars(model, h5) @time _extract_after_load_vars(model, h5)
_extract_after_load_constrs(model, h5) @time _extract_after_load_constrs(model, h5)
end end
function _extract_after_load_vars(model::JuMP.Model, h5) function _extract_after_load_vars(model::JuMP.Model, h5)
@@ -117,10 +118,11 @@ function _extract_after_load_constrs(model::JuMP.Model, h5)
end end
function _extract_after_lp(model::JuMP.Model, h5) function _extract_after_lp(model::JuMP.Model, h5)
@info "_extract_after_lp"
h5.put_scalar("lp_wallclock_time", solve_time(model)) h5.put_scalar("lp_wallclock_time", solve_time(model))
h5.put_scalar("lp_obj_value", objective_value(model)) h5.put_scalar("lp_obj_value", objective_value(model))
_extract_after_lp_vars(model, h5) @time _extract_after_lp_vars(model, h5)
_extract_after_lp_constrs(model, h5) @time _extract_after_lp_constrs(model, h5)
end end
function _extract_after_lp_vars(model::JuMP.Model, h5) function _extract_after_lp_vars(model::JuMP.Model, h5)
@@ -146,46 +148,46 @@ function _extract_after_lp_vars(model::JuMP.Model, h5)
end end
h5.put_array("lp_var_basis_status", to_str_array(basis_status)) h5.put_array("lp_var_basis_status", to_str_array(basis_status))
# Sensitivity analysis # # Sensitivity analysis
obj_coeffs = h5.get_array("static_var_obj_coeffs") # obj_coeffs = h5.get_array("static_var_obj_coeffs")
sensitivity_report = lp_sensitivity_report(model) # sensitivity_report = lp_sensitivity_report(model)
sa_obj_down, sa_obj_up = Float64[], Float64[] # sa_obj_down, sa_obj_up = Float64[], Float64[]
sa_lb_down, sa_lb_up = Float64[], Float64[] # sa_lb_down, sa_lb_up = Float64[], Float64[]
sa_ub_down, sa_ub_up = Float64[], Float64[] # sa_ub_down, sa_ub_up = Float64[], Float64[]
for (i, v) in enumerate(vars) # for (i, v) in enumerate(vars)
# Objective function # # Objective function
(delta_down, delta_up) = sensitivity_report[v] # (delta_down, delta_up) = sensitivity_report[v]
push!(sa_obj_down, delta_down + obj_coeffs[i]) # push!(sa_obj_down, delta_down + obj_coeffs[i])
push!(sa_obj_up, delta_up + obj_coeffs[i]) # push!(sa_obj_up, delta_up + obj_coeffs[i])
# Lower bound # # Lower bound
if has_lower_bound(v) # if has_lower_bound(v)
constr = LowerBoundRef(v) # constr = LowerBoundRef(v)
(delta_down, delta_up) = sensitivity_report[constr] # (delta_down, delta_up) = sensitivity_report[constr]
push!(sa_lb_down, lower_bound(v) + delta_down) # push!(sa_lb_down, lower_bound(v) + delta_down)
push!(sa_lb_up, lower_bound(v) + delta_up) # push!(sa_lb_up, lower_bound(v) + delta_up)
else # else
push!(sa_lb_down, -Inf) # push!(sa_lb_down, -Inf)
push!(sa_lb_up, -Inf) # push!(sa_lb_up, -Inf)
end # end
# Upper bound # # Upper bound
if has_upper_bound(v) # if has_upper_bound(v)
constr = JuMP.UpperBoundRef(v) # constr = JuMP.UpperBoundRef(v)
(delta_down, delta_up) = sensitivity_report[constr] # (delta_down, delta_up) = sensitivity_report[constr]
push!(sa_ub_down, upper_bound(v) + delta_down) # push!(sa_ub_down, upper_bound(v) + delta_down)
push!(sa_ub_up, upper_bound(v) + delta_up) # push!(sa_ub_up, upper_bound(v) + delta_up)
else # else
push!(sa_ub_down, Inf) # push!(sa_ub_down, Inf)
push!(sa_ub_up, Inf) # push!(sa_ub_up, Inf)
end # end
end # end
h5.put_array("lp_var_sa_obj_up", sa_obj_up) # h5.put_array("lp_var_sa_obj_up", sa_obj_up)
h5.put_array("lp_var_sa_obj_down", sa_obj_down) # h5.put_array("lp_var_sa_obj_down", sa_obj_down)
h5.put_array("lp_var_sa_ub_up", sa_ub_up) # h5.put_array("lp_var_sa_ub_up", sa_ub_up)
h5.put_array("lp_var_sa_ub_down", sa_ub_down) # h5.put_array("lp_var_sa_ub_down", sa_ub_down)
h5.put_array("lp_var_sa_lb_up", sa_lb_up) # h5.put_array("lp_var_sa_lb_up", sa_lb_up)
h5.put_array("lp_var_sa_lb_down", sa_lb_down) # h5.put_array("lp_var_sa_lb_down", sa_lb_down)
end end
@@ -201,7 +203,7 @@ function _extract_after_lp_constrs(model::JuMP.Model, h5)
duals = Float64[] duals = Float64[]
basis_status = [] basis_status = []
constr_idx = 1 constr_idx = 1
sensitivity_report = lp_sensitivity_report(model) # sensitivity_report = lp_sensitivity_report(model)
for (ftype, stype) in JuMP.list_of_constraint_types(model) for (ftype, stype) in JuMP.list_of_constraint_types(model)
for constr in JuMP.all_constraints(model, ftype, stype) for constr in JuMP.all_constraints(model, ftype, stype)
length(JuMP.name(constr)) > 0 || continue length(JuMP.name(constr)) > 0 || continue
@@ -219,21 +221,22 @@ function _extract_after_lp_constrs(model::JuMP.Model, h5)
error("Unknown basis status: $b") error("Unknown basis status: $b")
end end
# Sensitivity analysis # # Sensitivity analysis
(delta_down, delta_up) = sensitivity_report[constr] # (delta_down, delta_up) = sensitivity_report[constr]
push!(sa_rhs_down, rhs[constr_idx] + delta_down) # push!(sa_rhs_down, rhs[constr_idx] + delta_down)
push!(sa_rhs_up, rhs[constr_idx] + delta_up) # push!(sa_rhs_up, rhs[constr_idx] + delta_up)
constr_idx += 1 constr_idx += 1
end end
end end
h5.put_array("lp_constr_dual_values", duals) h5.put_array("lp_constr_dual_values", duals)
h5.put_array("lp_constr_basis_status", to_str_array(basis_status)) h5.put_array("lp_constr_basis_status", to_str_array(basis_status))
h5.put_array("lp_constr_sa_rhs_up", sa_rhs_up) # h5.put_array("lp_constr_sa_rhs_up", sa_rhs_up)
h5.put_array("lp_constr_sa_rhs_down", sa_rhs_down) # h5.put_array("lp_constr_sa_rhs_down", sa_rhs_down)
end end
function _extract_after_mip(model::JuMP.Model, h5) function _extract_after_mip(model::JuMP.Model, h5)
@info "_extract_after_mip"
h5.put_scalar("mip_obj_value", objective_value(model)) h5.put_scalar("mip_obj_value", objective_value(model))
h5.put_scalar("mip_obj_bound", objective_bound(model)) h5.put_scalar("mip_obj_bound", objective_bound(model))
h5.put_scalar("mip_wallclock_time", solve_time(model)) h5.put_scalar("mip_wallclock_time", solve_time(model))
@@ -254,11 +257,14 @@ end
function _fix_variables(model::JuMP.Model, var_names, var_values, stats) function _fix_variables(model::JuMP.Model, var_names, var_values, stats)
vars = [variable_by_name(model, v) for v in var_names] vars = [variable_by_name(model, v) for v in var_names]
for (i, var) in enumerate(vars) for (i, var) in enumerate(vars)
fix(var, var_values[i], force = true) if isfinite(var_values[i])
fix(var, var_values[i], force=true)
end
end end
end end
function _optimize(model::JuMP.Model) function _optimize(model::JuMP.Model)
@info "_optimize"
optimize!(model) optimize!(model)
flush(stdout) flush(stdout)
Libc.flush_cstdio() Libc.flush_cstdio()
@@ -269,7 +275,7 @@ function _relax(model::JuMP.Model)
relax_integrality(relaxed) relax_integrality(relaxed)
# FIXME: Remove hardcoded optimizer # FIXME: Remove hardcoded optimizer
set_optimizer(relaxed, HiGHS.Optimizer) set_optimizer(relaxed, HiGHS.Optimizer)
set_silent(relaxed) # set_silent(relaxed)
return relaxed return relaxed
end end
@@ -303,7 +309,7 @@ function __init_solvers_jump__()
constrs_lhs, constrs_lhs,
constrs_sense, constrs_sense,
constrs_rhs, constrs_rhs,
stats = nothing, stats=nothing,
) = _add_constrs( ) = _add_constrs(
self.inner, self.inner,
from_str_array(var_names), from_str_array(var_names),
@@ -319,14 +325,14 @@ function __init_solvers_jump__()
extract_after_mip(self, h5) = _extract_after_mip(self.inner, h5) extract_after_mip(self, h5) = _extract_after_mip(self.inner, h5)
fix_variables(self, var_names, var_values, stats = nothing) = fix_variables(self, var_names, var_values, stats=nothing) =
_fix_variables(self.inner, from_str_array(var_names), var_values, stats) _fix_variables(self.inner, from_str_array(var_names), var_values, stats)
optimize(self) = _optimize(self.inner) optimize(self) = _optimize(self.inner)
relax(self) = Class(_relax(self.inner)) relax(self) = Class(_relax(self.inner))
set_warm_starts(self, var_names, var_values, stats = nothing) = set_warm_starts(self, var_names, var_values, stats=nothing) =
_set_warm_starts(self.inner, from_str_array(var_names), var_values, stats) _set_warm_starts(self.inner, from_str_array(var_names), var_values, stats)
write(self, filename) = _write(self.inner, filename) write(self, filename) = _write(self.inner, filename)

2
test/Project.toml
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@@ -4,7 +4,9 @@ authors = ["Alinson S. Xavier <git@axavier.org>"]
version = "0.1.0" version = "0.1.0"
[deps] [deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d" Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Glob = "c27321d9-0574-5035-807b-f59d2c89b15c" Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b" HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"

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107
test/src/BB/test_bb.jl
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@@ -10,9 +10,12 @@ using Test
using MIPLearn.BB using MIPLearn.BB
using MIPLearn using MIPLearn
using CSV
using DataFrames
basepath = @__DIR__ basepath = @__DIR__
function bb_run(optimizer_name, optimizer; large = true) function bb_run(optimizer_name, optimizer; large=true)
@testset "Solve ($optimizer_name)" begin @testset "Solve ($optimizer_name)" begin
@testset "interface" begin @testset "interface" begin
filename = "$FIXTURES/danoint.mps.gz" filename = "$FIXTURES/danoint.mps.gz"
@@ -25,7 +28,7 @@ function bb_run(optimizer_name, optimizer; large = true)
status, obj = BB.solve_relaxation!(mip) status, obj = BB.solve_relaxation!(mip)
@test status == :Optimal @test status == :Optimal
@test round(obj, digits = 6) == 62.637280 @test round(obj, digits=6) == 62.637280
@test BB.name(mip, mip.int_vars[1]) == "xab" @test BB.name(mip, mip.int_vars[1]) == "xab"
@test BB.name(mip, mip.int_vars[2]) == "xac" @test BB.name(mip, mip.int_vars[2]) == "xac"
@@ -35,26 +38,26 @@ function bb_run(optimizer_name, optimizer; large = true)
@test mip.int_vars_ub[1] == 1.0 @test mip.int_vars_ub[1] == 1.0
vals = BB.values(mip, mip.int_vars) vals = BB.values(mip, mip.int_vars)
@test round(vals[1], digits = 6) == 0.046933 @test round(vals[1], digits=6) == 0.046933
@test round(vals[2], digits = 6) == 0.000841 @test round(vals[2], digits=6) == 0.000841
@test round(vals[3], digits = 6) == 0.248696 @test round(vals[3], digits=6) == 0.248696
# Probe (up and down are feasible) # Probe (up and down are feasible)
probe_up, probe_down = BB.probe(mip, mip.int_vars[1], 0.5, 0.0, 1.0, 1_000_000) 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_down, digits=6) == 62.690000
@test round(probe_up, digits = 6) == 62.714100 @test round(probe_up, digits=6) == 62.714100
# Fix one variable to zero # Fix one variable to zero
BB.set_bounds!(mip, mip.int_vars[1:1], [0.0], [0.0]) BB.set_bounds!(mip, mip.int_vars[1:1], [0.0], [0.0])
status, obj = BB.solve_relaxation!(mip) status, obj = BB.solve_relaxation!(mip)
@test status == :Optimal @test status == :Optimal
@test round(obj, digits = 6) == 62.690000 @test round(obj, digits=6) == 62.690000
# Fix one variable to one and another variable variable to zero # Fix one variable to one and another variable variable to zero
BB.set_bounds!(mip, mip.int_vars[1:2], [1.0, 0.0], [1.0, 0.0]) BB.set_bounds!(mip, mip.int_vars[1:2], [1.0, 0.0], [1.0, 0.0])
status, obj = BB.solve_relaxation!(mip) status, obj = BB.solve_relaxation!(mip)
@test status == :Optimal @test status == :Optimal
@test round(obj, digits = 6) == 62.714777 @test round(obj, digits=6) == 62.714777
# Fix all binary variables to one, making problem infeasible # Fix all binary variables to one, making problem infeasible
N = length(mip.int_vars) N = length(mip.int_vars)
@@ -68,7 +71,7 @@ function bb_run(optimizer_name, optimizer; large = true)
BB.set_bounds!(mip, mip.int_vars, zeros(N), ones(N)) BB.set_bounds!(mip, mip.int_vars, zeros(N), ones(N))
status, obj = BB.solve_relaxation!(mip) status, obj = BB.solve_relaxation!(mip)
@test status == :Optimal @test status == :Optimal
@test round(obj, digits = 6) == 62.637280 @test round(obj, digits=6) == 62.637280
end end
@testset "varbranch" begin @testset "varbranch" begin
@@ -82,8 +85,8 @@ function bb_run(optimizer_name, optimizer; large = true)
BB.StrongBranching(), BB.StrongBranching(),
BB.ReliabilityBranching(), BB.ReliabilityBranching(),
BB.HybridBranching(), BB.HybridBranching(),
BB.StrongBranching(aggregation = :min), BB.StrongBranching(aggregation=:min),
BB.ReliabilityBranching(aggregation = :min, collect = true), BB.ReliabilityBranching(aggregation=:min, collect=true),
] ]
h5 = H5File("$FIXTURES/$instance.h5") h5 = H5File("$FIXTURES/$instance.h5")
mip_lower_bound = h5.get_scalar("mip_lower_bound") mip_lower_bound = h5.get_scalar("mip_lower_bound")
@@ -98,23 +101,23 @@ function bb_run(optimizer_name, optimizer; large = true)
@info optimizer_name, branch_rule, instance @info optimizer_name, branch_rule, instance
@time BB.solve!( @time BB.solve!(
mip, mip,
initial_primal_bound = mip_primal_bound, initial_primal_bound=mip_primal_bound,
print_interval = 1, print_interval=1,
node_limit = 25, node_limit=25,
branch_rule = branch_rule, branch_rule=branch_rule,
) )
end end
end end
end end
@testset "collect" begin @testset "collect" begin
rule = BB.ReliabilityBranching(collect = true) rule = BB.ReliabilityBranching(collect=true)
BB.collect!( BB.collect!(
optimizer, optimizer,
"$FIXTURES/bell5.mps.gz", "$FIXTURES/bell5.mps.gz",
node_limit = 100, node_limit=100,
print_interval = 10, print_interval=10,
branch_rule = rule, branch_rule=rule,
) )
n_sb = rule.stats.num_strong_branch_calls n_sb = rule.stats.num_strong_branch_calls
h5 = H5File("$FIXTURES/bell5.h5") h5 = H5File("$FIXTURES/bell5.h5")
@@ -132,3 +135,67 @@ function test_bb()
@time bb_run("HiGHS", optimizer_with_attributes(HiGHS.Optimizer)) @time bb_run("HiGHS", optimizer_with_attributes(HiGHS.Optimizer))
# @time bb_run("CPLEX", optimizer_with_attributes(CPLEX.Optimizer, "CPXPARAM_Threads" => 1)) # @time bb_run("CPLEX", optimizer_with_attributes(CPLEX.Optimizer, "CPXPARAM_Threads" => 1))
end end
function test_bb_replay()
rule_sb = BB.StrongBranching()
rule_rb = BB.ReliabilityBranching()
optimizer = optimizer_with_attributes(HiGHS.Optimizer)
filenames = [replace(f, ".h5" => "") for f in glob("test/fixtures/stab/*.h5")]
results_filename = "tmp.csv"
lk = ReentrantLock()
results = []
function push_result(r)
lock(lk) do
push!(results, r)
df = DataFrame()
for row in results
push!(df, row, cols=:union)
end
CSV.write(results_filename, df)
end
end
function solve(filename; replay=nothing, skip=false, rule)
has_replay = (replay !== nothing)
h5 = H5File("$filename.h5", "r")
mip_obj_bound = h5.get_scalar("mip_obj_bound")
@show filename
@show has_replay
h5.file.close()
mip = BB.init(optimizer)
BB.read!(mip, "$filename.mps.gz")
time_solve = @elapsed begin
pool, replay = BB.solve!(
mip,
initial_primal_bound=mip_obj_bound,
print_interval=100,
node_limit=1_000,
branch_rule=rule,
replay=replay,
)
end
if !skip
push_result(
Dict(
"Filename" => filename,
"Replay?" => has_replay,
"Solve time (s)" => time_solve,
"Relative MIP gap (%)" => round(pool.gap * 100, digits=3)
)
)
end
return replay
end
# Solve reference instance
replay = solve(filenames[1], skip=true, rule=rule_sb)
# Solve perturbations
for i in 2:6
solve(filenames[i], rule=rule_rb, replay=nothing)
solve(filenames[i], rule=rule_rb, replay=deepcopy(replay))
end
return
end