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Add support for sparsity exploitation for SOCs of large dimensionality #1468

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01f3469
Add support for sparsity exploitation for SOCP of large dimensionality
yuwenchen95 Jun 24, 2026
9c8965e
Merge branch 'main' into sparse-socp
yuwenchen95 Jun 29, 2026
4ed64ef
Stricter IR tolerance for SOCP with sparse socs
yuwenchen95 Jun 29, 2026
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1 change: 1 addition & 0 deletions cpp/include/cuopt/mathematical_optimization/constants.h
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,7 @@
#define CUOPT_ORDERING "ordering"
#define CUOPT_BARRIER_DUAL_INITIAL_POINT "barrier_dual_initial_point"
#define CUOPT_BARRIER_ITERATIVE_REFINEMENT "barrier_iterative_refinement"
#define CUOPT_BARRIER_SOC_THRESHOLD "barrier_soc_threshold"
#define CUOPT_BARRIER_STEP_SCALE "barrier_step_scale"
#define CUOPT_ELIMINATE_DENSE_COLUMNS "eliminate_dense_columns"
#define CUOPT_CUDSS_DETERMINISTIC "cudss_deterministic"
Expand Down
1 change: 1 addition & 0 deletions cpp/include/cuopt/mathematical_optimization/pdlp/solver_settings.hpp
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Expand Up @@ -283,6 +283,7 @@ class pdlp_solver_settings_t {
bool eliminate_dense_columns{true};
pdlp_precision_t pdlp_precision{pdlp_precision_t::DefaultPrecision};
bool barrier_iterative_refinement{true};
i_t barrier_soc_threshold{5};
f_t barrier_step_scale{0.9};
bool save_best_primal_so_far{false};
/**
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501 changes: 403 additions & 98 deletions cpp/src/barrier/barrier.cu
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26 changes: 16 additions & 10 deletions cpp/src/barrier/iterative_refinement.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,6 @@

#include <algorithm>
#include <cmath>
#include <cstdio>
#include <limits>
#include <vector>

Expand Down Expand Up @@ -89,7 +88,8 @@ f_t vector_norm2(const rmm::device_uvector<f_t>& x)
template <typename i_t, typename f_t, typename T>
f_t iterative_refinement_simple(T& op,
const rmm::device_uvector<f_t>& b,
rmm::device_uvector<f_t>& x)
rmm::device_uvector<f_t>& x,
f_t tol = 1e-8)
{
rmm::device_uvector<f_t> x_sav(x, x.stream());

Expand All @@ -106,7 +106,7 @@ f_t iterative_refinement_simple(T& op,
}
rmm::device_uvector<f_t> delta_x(x.size(), op.data_.handle_ptr->get_stream());
i_t iter = 0;
while (error > 1e-8 && iter < 30) {
while (error > tol && iter < 30) {
thrust::fill(op.data_.handle_ptr->get_thrust_policy(),
delta_x.data(),
delta_x.data() + delta_x.size(),
Expand Down Expand Up @@ -155,15 +155,15 @@ f_t iterative_refinement_simple(T& op,
template <typename i_t, typename f_t, typename T>
f_t iterative_refinement_gmres(T& op,
const rmm::device_uvector<f_t>& b,
rmm::device_uvector<f_t>& x)
rmm::device_uvector<f_t>& x,
f_t tol = 1e-8)
{
// Parameters
// Ideally, we do not need to restart here. But having restarts helps as a checkpoint to get
// better solutions in case of true residual is far from the measured residual and true residuals
// are not converging after some point
const int max_restarts = 3;
const int m = 10; // Krylov space dimension
const f_t tol = 1e-8;

rmm::device_uvector<f_t> r(x.size(), x.stream());
rmm::device_uvector<f_t> x_sav(x, x.stream());
Expand All @@ -189,7 +189,7 @@ f_t iterative_refinement_gmres(T& op,

f_t norm_r = vector_norm_inf<f_t>(r);
if (show_info) { CUOPT_LOG_INFO("GMRES IR: initial residual = %e, |b| = %e", norm_r, bnorm); }
if (norm_r <= 1e-8) { return norm_r; }
if (norm_r <= tol) { return norm_r; }

f_t residual = norm_r;
f_t best_residual = norm_r;
Expand Down Expand Up @@ -393,13 +393,16 @@ f_t iterative_refinement_gmres(T& op,
}

template <typename i_t, typename f_t, typename T>
f_t iterative_refinement(T& op, const dense_vector_t<i_t, f_t>& b, dense_vector_t<i_t, f_t>& x)
f_t iterative_refinement(T& op,
const dense_vector_t<i_t, f_t>& b,
dense_vector_t<i_t, f_t>& x,
f_t tol = 1e-8)
{
rmm::device_uvector<f_t> d_b(b.size(), op.data_.handle_ptr->get_stream());
raft::copy(d_b.data(), b.data(), b.size(), op.data_.handle_ptr->get_stream());
rmm::device_uvector<f_t> d_x(x.size(), op.data_.handle_ptr->get_stream());
raft::copy(d_x.data(), x.data(), x.size(), op.data_.handle_ptr->get_stream());
auto err = iterative_refinement_gmres<i_t, f_t, T>(op, d_b, d_x);
auto err = iterative_refinement_gmres<i_t, f_t, T>(op, d_b, d_x, tol);

raft::copy(x.data(), d_x.data(), x.size(), op.data_.handle_ptr->get_stream());

Expand All @@ -408,9 +411,12 @@ f_t iterative_refinement(T& op, const dense_vector_t<i_t, f_t>& b, dense_vector_
}

template <typename i_t, typename f_t, typename T>
f_t iterative_refinement(T& op, const rmm::device_uvector<f_t>& b, rmm::device_uvector<f_t>& x)
f_t iterative_refinement(T& op,
const rmm::device_uvector<f_t>& b,
rmm::device_uvector<f_t>& x,
f_t tol = 1e-8)
{
return iterative_refinement_gmres<i_t, f_t, T>(op, b, x);
return iterative_refinement_gmres<i_t, f_t, T>(op, b, x, tol);
}

} // namespace cuopt::mathematical_optimization::barrier
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