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Make GMI cuts more stable

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This commit is contained in:
Alinson S. Xavier
2024-03-12 13:56:34 -05:00
parent dbd6d156e6
commit 9c61b98cb9
4 changed files with 89 additions and 60 deletions
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1
src/Cuts/Cuts.jl
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@@ -9,6 +9,7 @@ import ..to_str_array
include("tableau/structs.jl")
# include("blackbox/cplex.jl")
include("tableau/numerics.jl")
include("tableau/collect.jl")
include("tableau/gmi.jl")
include("tableau/moi.jl")

32
src/Cuts/tableau/collect.jl
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@@ -5,8 +5,10 @@
import ..H5File
using OrderedCollections
using Statistics
function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_round = 100)
function collect_gmi(mps_filename; optimizer, max_rounds=10, max_cuts_per_round=100, atol=1e-4)
@info mps_filename
reset_timer!()
@@ -27,7 +29,7 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
if obj_mip === nothing
obj_mip = h5.get_scalar("mip_obj_value")
end
obj_lp = nothing
obj_lp = h5.get_scalar("lp_obj_value")
h5.file.close()
# Define relative MIP gap
@@ -58,8 +60,8 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
sol_opt = [sol_opt_dict[n] for n in data.var_names]
# Assert optimal solution is feasible for the original problem
@assert all(data.constr_lb .- 1e-3 .<= data.constr_lhs * sol_opt)
@assert all(data.constr_lhs * sol_opt .<= data.constr_ub .+ 1e-3)
assert_leq(data.constr_lb, data.constr_lhs * sol_opt)
assert_leq(data.constr_lhs * sol_opt, data.constr_ub)
# Convert to standard form
data_s, transforms = convert_to_standard_form(data)
@@ -71,15 +73,17 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
sol_opt_s = forward(transforms, sol_opt)
# Assert converted solution is feasible for standard form problem
@assert data_s.constr_lhs * sol_opt_s ≈ data_s.constr_lb
assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
end
# Optimize standard form
optimize!(model_s)
stats_time_solve += solve_time(model_s)
obj = objective_value(model_s) + data_s.obj_offset
if obj_lp === nothing
obj_lp = obj
if round == 1
# Assert standard form problem has same value as original
assert_eq(obj, obj_lp)
push!(stats_obj, obj)
push!(stats_gap, gap(obj))
push!(stats_ncuts, 0)
@@ -101,8 +105,8 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
tableau = compute_tableau(data_s, basis, sol_frac, rows=selected_rows)
# Assert tableau rows have been computed correctly
@assert tableau.lhs * sol_frac ≈ tableau.rhs
@assert tableau.lhs * sol_opt_s ≈ tableau.rhs
assert_eq(tableau.lhs * sol_frac, tableau.rhs)
assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
end
# Compute GMI cuts
@@ -110,17 +114,12 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
cuts_s = compute_gmi(data_s, tableau)
# Assert cuts have been generated correctly
try
assert_cuts_off(cuts_s, sol_frac)
assert_does_not_cut_off(cuts_s, sol_opt_s)
catch
@warn "Invalid cuts detected. Discarding round $round cuts and aborting."
break
end
# Abort if no cuts are left
if length(cuts_s.lb) == 0
@info "No cuts generated. Aborting."
@info "No cuts generated. Stopping."
break
end
end
@@ -139,7 +138,7 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
push!(stats_gap, gap(obj))
# Store useful cuts; drop useless ones from the problem
useful = [abs(shadow_price(c)) > 1e-3 for c in constrs]
useful = [abs(shadow_price(c)) > atol for c in constrs]
drop = findall(useful .== false)
keep = findall(useful .== true)
delete.(model, constrs[drop])
@@ -174,7 +173,6 @@ function collect_gmi(mps_filename; optimizer, max_rounds = 10, max_cuts_per_roun
"time_tableau" => stats_time_tableau,
"time_gmi" => stats_time_gmi,
"obj_mip" => obj_mip,
"obj_lp" => obj_lp,
"stats_obj" => stats_obj,
"stats_gap" => stats_gap,
"stats_ncuts" => stats_ncuts,

57
src/Cuts/tableau/gmi.jl
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@@ -5,13 +5,14 @@
using SparseArrays
using TimerOutputs
@inline frac(x::Float64) = x - floor(x)
function select_gmi_rows(data, basis, x; max_rows = 10, atol = 0.001)
function select_gmi_rows(data, basis, x; max_rows=10, atol=1e-4)
candidate_rows = [
r for
r = 1:length(basis.var_basic) if (data.var_types[basis.var_basic[r]] != 'C') &&
(frac(x[basis.var_basic[r]]) > atol)
r in 1:length(basis.var_basic) if (
(data.var_types[basis.var_basic[r]] != 'C') &&
(frac(x[basis.var_basic[r]]) > atol) &&
(frac2(x[basis.var_basic[r]]) > atol)
)
]
candidate_vals = frac.(x[basis.var_basic[candidate_rows]])
score = abs.(candidate_vals .- 0.5)
@@ -19,34 +20,36 @@ function select_gmi_rows(data, basis, x; max_rows = 10, atol = 0.001)
return [candidate_rows[perm[i]] for i = 1:min(length(perm), max_rows)]
end
function compute_gmi(data::ProblemData, tableau::Tableau, tol = 1e-8)::ConstraintSet
function compute_gmi(data::ProblemData, tableau::Tableau)::ConstraintSet
nrows, ncols = size(tableau.lhs)
ub = Float64[Inf for _ = 1:nrows]
lb = Float64[0.999 for _ = 1:nrows]
lb = Float64[0.9999 for _ = 1:nrows]
tableau_I, tableau_J, tableau_V = findnz(tableau.lhs)
lhs_I = Int[]
lhs_J = Int[]
lhs_V = Float64[]
@timeit "Compute coefficients" begin
for k = 1:nnz(tableau.lhs)
for k in 1:nnz(tableau.lhs)
i::Int = tableau_I[k]
j::Int = tableau_J[k]
v::Float64 = 0.0
alpha_j = frac(tableau_V[k])
frac_alpha_j = frac(tableau_V[k])
alpha_j = tableau_V[k]
beta = frac(tableau.rhs[i])
if data.var_types[i] == "C"
if data.var_types[j] == 'C'
if alpha_j >= 0
v = alpha_j / beta
else
v = alpha_j / (1 - beta)
v = -alpha_j / (1 - beta)
end
else
if alpha_j <= beta
v = alpha_j / beta
if frac_alpha_j < beta
v = frac_alpha_j / beta
else
v = (1 - alpha_j) / (1 - beta)
v = (1 - frac_alpha_j) / (1 - beta)
end
end
if abs(v) > tol
if abs(v) > 1e-8
push!(lhs_I, i)
push!(lhs_J, tableau_J[k])
push!(lhs_V, v)
@@ -57,28 +60,4 @@ function compute_gmi(data::ProblemData, tableau::Tableau, tol = 1e-8)::Constrain
return ConstraintSet(; lhs, ub, lb)
end
function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol = 1e-6)
for i = 1:length(cuts.lb)
val = cuts.lhs[i, :]' * x
if (val <= cuts.ub[i] - tol) && (val >= cuts.lb[i] + tol)
throw(ErrorException("inequality fails to cut off fractional solution"))
end
end
end
function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol = 1e-6)
for i = 1:length(cuts.lb)
val = cuts.lhs[i, :]' * x
ub = cuts.ub[i]
lb = cuts.lb[i]
if (val >= ub) || (val <= lb)
throw(
ErrorException(
"inequality $i cuts off integer solution ($lb <= $val <= $ub)",
),
)
end
end
end
export compute_gmi, frac, select_gmi_rows, assert_cuts_off, assert_does_not_cut_off

51
src/Cuts/tableau/numerics.jl Normal file
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@@ -0,0 +1,51 @@
@inline frac(x::Float64) = x - floor(x)
@inline frac2(x::Float64) = ceil(x) - x
function assert_leq(a, b; atol=0.01)
if !all(a .<= b .+ atol)
delta = a .- b
for i in eachindex(delta)
if delta[i] > atol
@info "Assertion failed: a[$i] = $(a[i]) <= $(b[i]) = b[$i]"
end
end
error("assert_leq failed")
end
end
function assert_eq(a, b; atol=1e-4)
if !all(abs.(a .- b) .<= atol)
delta = abs.(a .- b)
for i in eachindex(delta)
if delta[i] > atol
@info "Assertion failed: a[$i] = $(a[i]) == $(b[i]) = b[$i]"
end
end
error("assert_eq failed")
end
end
function assert_cuts_off(cuts::ConstraintSet, x::Vector{Float64}, tol=1e-6)
for i = 1:length(cuts.lb)
val = cuts.lhs[i, :]' * x
if (val <= cuts.ub[i] - tol) && (val >= cuts.lb[i] + tol)
throw(ErrorException("inequality fails to cut off fractional solution"))
end
end
end
function assert_does_not_cut_off(cuts::ConstraintSet, x::Vector{Float64}; tol=1e-6)
for i = 1:length(cuts.lb)
val = cuts.lhs[i, :]' * x
ub = cuts.ub[i]
lb = cuts.lb[i]
if (val >= ub) || (val <= lb)
throw(
ErrorException(
"inequality $i cuts off integer solution ($lb <= $val <= $ub)",
),
)
end
end
end