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DualGMI: multiple fixes

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This commit is contained in:
Alinson S. Xavier
2025-10-02 10:10:42 -05:00
parent 67e706d727
commit 295e29c351
4 changed files with 155 additions and 44 deletions
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187
src/Cuts/tableau/gmi_dual.jl
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@@ -264,6 +264,10 @@ function collect_gmi_dual(
)
end
# TODO:
# blp-ic98
# neos-3627168-kasai
function collect_gmi_FisSal2011(
mps_filename;
interval_print_sec = 1,
@@ -271,8 +275,9 @@ function collect_gmi_FisSal2011(
max_pool_size_mb = 1024,
optimizer,
silent_solver = true,
time_limit = 14_400,
time_limit = 300,
variant = :miplearn,
verify_cuts = true,
)
variant in [:subg, :hybr, :fast, :faster, :miplearn] || error("unknown variant: $variant")
if variant == :subg
@@ -395,6 +400,16 @@ function collect_gmi_FisSal2011(
for round = 1:max_rounds
log_prefix = ' '
log_should_print = false
is_last_iteration = false
if round == max_rounds
is_last_iteration = true
end
elapsed_time = time() - initial_time
if elapsed_time > time_limit
@info "Time limit exceeded. Stopping after current iteration."
is_last_iteration = true
end
if round > 1
@timeit "Build Lagrangian term" begin
@@ -430,7 +445,12 @@ function collect_gmi_FisSal2011(
optimize!(model_s)
basis_cache = get_basis(model_s)
status = termination_status(model_s)
if status != MOI.OPTIMAL
if status == MOI.DUAL_INFEASIBLE
@warn "LP is unbounded (dual infeasible). Resetting to best known multipliers."
copy!(multipliers_curr, multipliers_best)
obj_curr = obj_best
continue
elseif status != MOI.OPTIMAL
error("Non-optimal termination status: $status")
end
sol_frac = get_x(model_s)
@@ -602,11 +622,15 @@ function collect_gmi_FisSal2011(
pool_size_mb = Base.summarysize(pool) / 1024^2
while pool_size_mb >= max_pool_size_mb
@timeit "Identify cuts to remove" begin
σ = sortperm(pool_cut_age, rev=true)
scores = collect(zip(multipliers_best .> 1e-6, -pool_cut_age))
σ = sortperm(scores, rev=true)
pool_size = length(pool.ub)
n_keep = Int(floor(pool_size * 0.8))
idx_keep = σ[1:n_keep]
idx_remove = σ[(n_keep+1):end]
positive_multipliers_dropped = sum(multipliers_best[idx_remove] .> 1e-6)
@info "Dropping $(length(idx_remove)) cuts ($(positive_multipliers_dropped) with multipliers_best)"
end
@timeit "Update cut hashes" begin
for idx in idx_remove
@@ -641,7 +665,7 @@ function collect_gmi_FisSal2011(
end
end
if mod(round - 1, interval_large_lp) == 0 || round == max_rounds
if mod(round - 1, interval_large_lp) == 0 || is_last_iteration
log_should_print = true
@timeit "Update multipliers (large LP)" begin
selected_idx = []
@@ -756,6 +780,10 @@ function collect_gmi_FisSal2011(
log_should_print = true
end
if is_last_iteration
log_should_print = true
end
if log_should_print
last_print_time = time()
@printf(
@@ -783,17 +811,18 @@ function collect_gmi_FisSal2011(
end
end
elapsed_time = time() - initial_time
if elapsed_time > time_limit
@info "Time limit exceeded. Stopping."
if is_last_iteration
break
end
end
end
@info "Best gap closure: $(gapcl_best)"
@timeit "Keep only active cuts" begin
positive_idx = findall(multipliers_best .> 1e-6)
if length(positive_idx) == 0 && gapcl_best > 0
error("gap closure with zero cuts")
end
@timeit "Clean up cut pool" begin
pool.lhs = pool.lhs[:, positive_idx]
@@ -821,41 +850,94 @@ function collect_gmi_FisSal2011(
@info "Keeping $(length(positive_idx)) cuts from $(length(used_basis_ids)) unique bases"
end
@timeit "Write cuts to H5" begin
if !isempty(cut_basis_id)
@timeit "Convert IDs to offsets" begin
id_to_offset = Dict{Int, Int}()
gmi_basis_vars = []
gmi_basis_sizes = []
for (offset, basis_id) in enumerate(sort(collect(keys(basis_id_to_vars))))
id_to_offset[basis_id] = offset
push!(gmi_basis_vars, basis_id_to_vars[basis_id])
push!(gmi_basis_sizes, basis_id_to_sizes[basis_id])
end
gmi_cut_basis = [id_to_offset[basis_id] for basis_id in cut_basis_id]
gmi_cut_row = cut_row
end
@timeit "Convert to matrices" begin
gmi_basis_vars_matrix = hcat(gmi_basis_vars...)'
gmi_basis_sizes_matrix = hcat(gmi_basis_sizes...)'
end
@timeit "Write H5" begin
h5 = H5File(h5_filename, "r+")
h5.put_array("gmi_basis_vars", gmi_basis_vars_matrix)
h5.put_array("gmi_basis_sizes", gmi_basis_sizes_matrix)
h5.put_array("gmi_cut_basis", gmi_cut_basis)
h5.put_array("gmi_cut_row", gmi_cut_row)
h5.file.close()
end
end
end
to = TimerOutputs.get_defaulttimer()
stats_time = TimerOutputs.tottime(to) / 1e9
print_timer()
if length(positive_idx) > 0
@timeit "Write cuts to H5" begin
if !isempty(cut_basis_id)
@timeit "Convert IDs to offsets" begin
id_to_offset = Dict{Int, Int}()
gmi_basis_vars = []
gmi_basis_sizes = []
for (offset, basis_id) in enumerate(sort(collect(keys(basis_id_to_vars))))
id_to_offset[basis_id] = offset
push!(gmi_basis_vars, basis_id_to_vars[basis_id])
push!(gmi_basis_sizes, basis_id_to_sizes[basis_id])
end
gmi_cut_basis = [id_to_offset[basis_id] for basis_id in cut_basis_id]
gmi_cut_row = cut_row
end
@timeit "Convert to matrices" begin
gmi_basis_vars_matrix = hcat(gmi_basis_vars...)'
gmi_basis_sizes_matrix = hcat(gmi_basis_sizes...)'
end
@timeit "Write H5" begin
h5 = H5File(h5_filename, "r+")
h5.put_array("gmi_basis_vars", gmi_basis_vars_matrix)
h5.put_array("gmi_basis_sizes", gmi_basis_sizes_matrix)
h5.put_array("gmi_cut_basis", gmi_cut_basis)
h5.put_array("gmi_cut_row", gmi_cut_row)
h5.file.close()
end
end
end
if verify_cuts
@timeit "Verify cuts in current model" begin
@info "Verifying cuts in current standard form model using pool..."
if !isempty(cut_basis_id)
@info "Adding $(length(pool.lb)) cuts from pool to current model"
pool.lhs = sparse(pool.lhs')
constrs = build_constraints(model_s, pool)
add_constraint.(model_s, constrs)
set_objective_function(model_s, orig_obj_s)
optimize!(model_s)
status = termination_status(model_s)
if status != MOI.OPTIMAL
error("Non-optimal termination status: $status")
end
obj_verify_s = objective_value(model_s)
gapcl_verify_s = gapcl(obj_verify_s)
@show gapcl_verify_s
@show gapcl_best
if abs(gapcl_best - gapcl_verify_s) > 0.01
error("Gap closures differ: $(gapcl_best) ≠ $(gapcl_verify_s)")
end
@info "Current model gap closure matches: $(gapcl_best) ≈ $(gapcl_verify_s)"
else
@warn "No cuts in pool to verify"
end
end
@timeit "Verify stored cuts" begin
@info "Verifying stored cuts..."
model_verify = read_from_file(mps_filename)
set_optimizer(model_verify, optimizer)
verification_cuts = _dualgmi_generate([h5_filename], model_verify; test_h5=h5_filename)
constrs = build_constraints(model_verify, verification_cuts)
add_constraint.(model_verify, constrs)
relax_integrality(model_verify)
optimize!(model_verify)
status = termination_status(model_verify)
if status != MOI.OPTIMAL
error("Non-optimal termination status: $status")
end
obj_verify = objective_value(model_verify)
gapcl_verify = gapcl(obj_verify)
@show gapcl_verify
@show gapcl_best
if abs(gapcl_best - gapcl_verify) > 0.01
error("Gap closures differ: $(gapcl_best) ≠ $(gapcl_verify)")
end
@info "Gap closure matches gapcl_best: $(gapcl_best) ≈ $(gapcl_verify)"
end
end
end
return OrderedDict(
"gapcl_best" => gapcl_best,
"gapcl_curr" => gapcl_curr,
@@ -977,13 +1059,34 @@ function _dualgmi_compress_h5(h5_filename)
h5.file.close()
end
function _dualgmi_generate(train_h5, model)
function _dualgmi_generate(train_h5, model; test_h5=nothing)
@timeit "Read problem data" begin
data = ProblemData(model)
end
@timeit "Convert to standard form" begin
data_s, transforms = convert_to_standard_form(data)
end
@timeit "Read optimal solution from test H5" begin
sol_opt_dict = nothing
sol_opt = nothing
sol_opt_s = nothing
if test_h5 !== nothing
try
h5 = H5File(test_h5, "r")
var_names = h5.get_array("static_var_names")
var_values = h5.get_array("mip_var_values")
h5.close()
if var_names !== nothing && var_values !== nothing
sol_opt_dict = Dict(zip(var_names, convert(Array{Float64}, var_values)))
sol_opt = [sol_opt_dict[n] for n in data.var_names]
sol_opt_s = forward(transforms, sol_opt)
@info "Loaded optimal solution for cut validation"
end
catch e
@warn "Could not read optimal solution from test H5 file: $e"
end
end
end
@timeit "Collect cuts from H5 files" begin
basis_vars_to_basis_offset = Dict()
combined_basis_sizes = nothing
@@ -1052,6 +1155,10 @@ function _dualgmi_generate(train_h5, model)
tableau = compute_tableau(data_s, current_basis; rows=collect(rows))
cuts_s = compute_gmi(data_s, tableau)
cuts = backwards(transforms, cuts_s)
if sol_opt_s !== nothing && sol_opt !== nothing
assert_does_not_cut_off(cuts_s, sol_opt_s)
assert_does_not_cut_off(cuts, sol_opt)
end
if all_cuts === nothing
all_cuts = cuts
else

4
src/Cuts/tableau/numerics.jl
View File

@@ -30,7 +30,7 @@ function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol = 1e-6)
vals = cuts.lhs * x
for i = 1:length(cuts.lb)
if (vals[i] <= cuts.ub[i] - tol) && (vals[i] >= cuts.lb[i] + tol)
throw(ErrorException("inequality fails to cut off fractional solution"))
throw(ErrorException("inequality $i fails to cut off fractional solution: $(cuts.lb[i]) <= $(vals[i]) <= $(cuts.ub[i])"))
end
end
end
@@ -39,7 +39,7 @@ function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol =
vals = cuts.lhs * x
for i = 1:length(cuts.lb)
if (vals[i] >= cuts.ub[i]) || (vals[i] <= cuts.lb[i])
throw(ErrorException("inequality $i cuts off integer solution"))
throw(ErrorException("inequality $i cuts off integer solution: $(cuts.lb[i]) <= $(vals[i]) <= $(cuts.ub[i])"))
end
end
end

4
src/Cuts/tableau/tableau.jl
View File

@@ -173,6 +173,10 @@ function compute_tableau(
tol = 1e-8,
estimated_density = 0.10,
)::Tableau
if isnan(estimated_density) || estimated_density <= 0
estimated_density = 0.10
end
@timeit "Split data" begin
nrows, ncols = size(data.constr_lhs)
lhs_slacks = sparse(I, nrows, nrows)

4
src/Cuts/tableau/transform.jl
View File

@@ -231,10 +231,10 @@ function backwards!(t::SplitFreeVars, c::ConstraintSet)
# Assert only LE constraints are left (EQ constraints are not supported)
@assert all(c.lb .== -Inf)
# Take minimum (weakest) coefficient
# Combine split free variable coefficients: x = x_p - x_m
B, F = t.B, t.F
for i = 1:F
c.lhs[:, B+i] = min.(c.lhs[:, B+i], -c.lhs[:, B+F+i])
c.lhs[:, B+i] = c.lhs[:, B+i] - c.lhs[:, B+F+i]
end
c.lhs = c.lhs[:, 1:(B+F)]
end