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Fix assert failure on MIP problem reducing to LP after presolve #235

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Merged
rapids-bot merged 13 commits into from
Jul 30, 2025
Merged

Fix assert failure on MIP problem reducing to LP after presolve #235

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32c057e
Fix assert failures on MIP problems losing their integer variables af…
aliceb-nv Jul 25, 2025
731405d
fix bug causing initial solutions to be scaled regardless of setting
aliceb-nv Jul 25, 2025
c55e080
added test
aliceb-nv Jul 25, 2025
f81356d
run PDLP when MIP problem reduced to LP
aliceb-nv Jul 25, 2025
c20c9b5
Revert "Fix assert failures on MIP problems losing their integer vari…
aliceb-nv Jul 25, 2025
1a5a7b1
Merge branch 'branch-25.08' into no-int-var-fix
aliceb-nv Jul 25, 2025
79fc52c
Add CTK 12.9 fatbin flags to maintain existing binary sizes (#58)
robertmaynard Jul 25, 2025
a166933
Bump actions/checkout in nightly.yaml to v4 (#230)
ScottBrenner Jul 25, 2025
de128e9
Add helm chart for cuopt service (#224)
rgsl888prabhu Jul 25, 2025
5e71185
Add nightly container support (#180)
rgsl888prabhu Jul 25, 2025
159a433
run concurrent LP instead
aliceb-nv Jul 28, 2025
31c84f4
Merge branch 'branch-25.08' into no-int-var-fix
aliceb-nv Jul 28, 2025
a5e678e
remove todo
aliceb-nv Jul 28, 2025
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10 changes: 8 additions & 2 deletions cpp/src/linear_programming/solve.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -467,7 +467,7 @@ void run_dual_simplex_thread(

template <typename i_t, typename f_t>
optimization_problem_solution_t<i_t, f_t> run_concurrent(
optimization_problem_t<i_t, f_t>& op_problem,
const optimization_problem_t<i_t, f_t>& op_problem,
detail::problem_t<i_t, f_t>& problem,
pdlp_solver_settings_t<i_t, f_t> const& settings,
bool is_batch_mode)
Expand Down Expand Up @@ -540,7 +540,7 @@ optimization_problem_solution_t<i_t, f_t> run_concurrent(

template <typename i_t, typename f_t>
optimization_problem_solution_t<i_t, f_t> solve_lp_with_method(
optimization_problem_t<i_t, f_t>& op_problem,
const optimization_problem_t<i_t, f_t>& op_problem,
detail::problem_t<i_t, f_t>& problem,
pdlp_solver_settings_t<i_t, f_t> const& settings,
bool is_batch_mode)
Expand Down Expand Up @@ -714,6 +714,12 @@ optimization_problem_solution_t<i_t, f_t> solve_lp(
bool problem_checking, \
bool use_pdlp_solver_mode); \
\
template optimization_problem_solution_t<int, F_TYPE> solve_lp_with_method( \
const optimization_problem_t<int, F_TYPE>& op_problem, \
detail::problem_t<int, F_TYPE>& problem, \
pdlp_solver_settings_t<int, F_TYPE> const& settings, \
bool is_batch_mode = false); \
\
template optimization_problem_t<int, F_TYPE> mps_data_model_to_optimization_problem( \
raft::handle_t const* handle_ptr, \
const cuopt::mps_parser::mps_data_model_t<int, F_TYPE>& data_model);
Expand Down
7 changes: 7 additions & 0 deletions cpp/src/linear_programming/solve.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -30,4 +30,11 @@ cuopt::linear_programming::optimization_problem_t<i_t, f_t> mps_data_model_to_op
raft::handle_t const* handle_ptr,
const cuopt::mps_parser::mps_data_model_t<i_t, f_t>& data_model);

template <typename i_t, typename f_t>
cuopt::linear_programming::optimization_problem_solution_t<i_t, f_t> solve_lp_with_method(
const optimization_problem_t<i_t, f_t>& op_problem,
detail::problem_t<i_t, f_t>& problem,
pdlp_solver_settings_t<i_t, f_t> const& settings,
bool is_batch_mode = false);

} // namespace cuopt::linear_programming
10 changes: 6 additions & 4 deletions cpp/src/mip/solve.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -125,10 +125,12 @@ mip_solution_t<i_t, f_t> run_mip(detail::problem_t<i_t, f_t>& problem,
running_mip);

cuopt_func_call(auto saved_problem = scaled_problem);
if (settings.mip_scaling) { scaling.scale_problem(); }
if (settings.initial_solutions.size() > 0) {
for (const auto& initial_solution : settings.initial_solutions) {
scaling.scale_primal(*initial_solution);
if (settings.mip_scaling) {
scaling.scale_problem();
if (settings.initial_solutions.size() > 0) {
for (const auto& initial_solution : settings.initial_solutions) {
scaling.scale_primal(*initial_solution);
}
}
}
// only call preprocess on scaled problem, so we can compute feasibility on the original problem
Expand Down
24 changes: 24 additions & 0 deletions cpp/src/mip/solver.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,9 @@
#include "local_search/rounding/simple_rounding.cuh"
#include "solver.cuh"

#include <linear_programming/pdlp.cuh>
#include <linear_programming/solve.cuh>

#include <dual_simplex/branch_and_bound.hpp>
#include <dual_simplex/simplex_solver_settings.hpp>
#include <dual_simplex/solve.hpp>
Expand Down Expand Up @@ -124,6 +127,27 @@ solution_t<i_t, f_t> mip_solver_t<i_t, f_t>::run_solver()
return sol;
}

// if the problem was reduced to a LP: run concurrent LP
if (context.problem_ptr->n_integer_vars == 0) {
CUOPT_LOG_INFO("Problem reduced to a LP, running concurrent LP");
pdlp_solver_settings_t<i_t, f_t> settings{};
settings.time_limit = timer_.remaining_time();
settings.method = method_t::Concurrent;

auto opt_sol = solve_lp_with_method<i_t, f_t>(
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@hlinsen hlinsen Jul 29, 2025

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Calling LP with solve_lp_with_method instead of solve_lp means we would not get the initial LP problem checks and presolve being added in #234.
Should we use it instead? the rest looks good to me!

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@Kh4ster Kh4ster Jul 30, 2025

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I also think we should be calling solve_lp and pass down the corresponding settings to use concurrent unless I missed something

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@aliceb-nv aliceb-nv Jul 30, 2025

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Problem checks should be superfluous as they're already performed on the presolved problem. Additionally, the checks in solve_lp are only done on the original problem structure (which has already been checked at the time of calling solve_mip)
solve_lp requires passing either a mps_data_model or the original optimization_problem_t instead of the presolved problem, I used solve_lp_with_method to avoid making changes to solve_lp. Do you think I should add an overload instead?

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@hlinsen hlinsen Jul 30, 2025

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I think another overload would over complexify things at this point. I prefer sticking with this approach. What we could do is move the try/catch and presolve in solve_lp_with_method in some other PR.

*context.problem_ptr->original_problem_ptr, *context.problem_ptr, settings);

solution_t<i_t, f_t> sol(*context.problem_ptr);
sol.copy_new_assignment(host_copy(opt_sol.get_primal_solution()));
if (opt_sol.get_termination_status() == pdlp_termination_status_t::Optimal ||
opt_sol.get_termination_status() == pdlp_termination_status_t::PrimalInfeasible ||
opt_sol.get_termination_status() == pdlp_termination_status_t::DualInfeasible) {
sol.set_problem_fully_reduced();
}
context.problem_ptr->post_process_solution(sol);
return sol;
}

namespace dual_simplex = cuopt::linear_programming::dual_simplex;
std::future<dual_simplex::mip_status_t> branch_and_bound_status_future;
dual_simplex::user_problem_t<i_t, f_t> branch_and_bound_problem;
Expand Down
7 changes: 7 additions & 0 deletions cpp/tests/mip/empty_fixed_problems_test.cu
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Expand Up @@ -78,4 +78,11 @@ TEST(mip_solve, empty_max_problem_with_objective_test)
EXPECT_NEAR(obj_val, 11, 1e-5);
}

TEST(mip_solve, mip_presolved_to_lp)
{
auto [termination_status, obj_val, lb] = test_mps_file("mip/mip-presolved-to-lp.mps", 5, false);
EXPECT_EQ(termination_status, mip_termination_status_t::Optimal);
EXPECT_NEAR(obj_val, 0, 1e-5);
}

} // namespace cuopt::linear_programming::test
32 changes: 32 additions & 0 deletions datasets/mip/mip-presolved-to-lp.mps
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Original file line number Diff line number Diff line change
@@ -0,0 +1,32 @@
NAME LP_PROBLEM
ROWS
N OBJ
E R001
E R002
E R003
L R004
L R005
L R006
L R007
COLUMNS
X001 OBJ 1.000000
X001 R004 -1.000000
X001 R006 -1.000000
X002 OBJ 1.000000
X002 R005 -1.000000
X002 R007 -1.000000
X003 R001 1.000000
X003 R004 1.000000
X003 R006 -1.000000
X004 R002 1.000000
X004 R005 1.000000
X004 R007 -1.000000
X005 R001 -1.000000
X005 R002 -1.000000
X005 R003 1.000000
RHS
BOUNDS
LO BND1 X005 0.000000
UP BND1 X005 1.000000
BV BND1 X005
ENDATA