Update solution object to include the LP solver (#822)

This PR replaced `solved_by_pdlp` with `solved_by` in `optimization_problem_solution_t` and all associated objects, such that now it is possible to retrieve which method was used for solving the LP when running in concurrent mode. This also fix a typo in the `CUOPT_TERMINATION_STATUS` and updates the B&B logs to display the method used for solving the root relaxation.

## Issue
Closes #787

Authors:
  - Nicolas L. Guidotti (https://github.com/nguidotti)
  - Ishika Roy (https://github.com/Iroy30)

Approvers:
  - Ramakrishnap (https://github.com/rgsl888prabhu)
  - Nicolas Blin (https://github.com/Kh4ster)

URL: #822

Author: nguidotti

cpp/include/cuopt/linear_programming/constants.h

@@ -130,6 +130,7 @@
 #define CUOPT_METHOD_PDLP         1
 #define CUOPT_METHOD_DUAL_SIMPLEX 2
 #define CUOPT_METHOD_BARRIER      3
+#define CUOPT_METHOD_UNSET        4
 
 /* @brief PDLP precision mode constants */
 #define CUOPT_PDLP_DEFAULT_PRECISION -1

cpp/include/cuopt/linear_programming/cpu_optimization_problem_solution.hpp

@@ -46,7 +46,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
       l2_dual_residual_(std::numeric_limits<f_t>::signaling_NaN()),
       gap_(std::numeric_limits<f_t>::signaling_NaN()),
       num_iterations_(0),
-      solved_by_pdlp_(false)
+      solved_by_(Unset)
   {
   }
 
@@ -65,7 +65,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
                     f_t l2_dual_residual,
                     f_t gap,
                     i_t num_iterations,
-                    bool solved_by_pdlp)
+                    method_t solved_by)
     : primal_solution_(std::move(primal_solution)),
       dual_solution_(std::move(dual_solution)),
       reduced_cost_(std::move(reduced_cost)),
@@ -78,7 +78,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
       l2_dual_residual_(l2_dual_residual),
       gap_(gap),
       num_iterations_(num_iterations),
-      solved_by_pdlp_(solved_by_pdlp)
+      solved_by_(solved_by)
   {
   }
 
@@ -97,7 +97,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
                     f_t l2_dual_residual,
                     f_t gap,
                     i_t num_iterations,
-                    bool solved_by_pdlp,
+                    method_t solved_by,
                     cpu_pdlp_warm_start_data_t<i_t, f_t>&& warmstart_data)
     : primal_solution_(std::move(primal_solution)),
       dual_solution_(std::move(dual_solution)),
@@ -111,7 +111,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
       l2_dual_residual_(l2_dual_residual),
       gap_(gap),
       num_iterations_(num_iterations),
-      solved_by_pdlp_(solved_by_pdlp),
+      solved_by_(solved_by),
       pdlp_warm_start_data_(std::move(warmstart_data))
   {
   }
@@ -149,7 +149,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
 
   i_t get_num_iterations(i_t = 0) const override { return num_iterations_; }
 
-  bool is_solved_by_pdlp(i_t = 0) const override { return solved_by_pdlp_; }
+  method_t solved_by(i_t = 0) const override { return solved_by_; }
 
   const pdlp_warm_start_data_t<i_t, f_t>& get_pdlp_warm_start_data() const override
   {
@@ -266,7 +266,7 @@ class cpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
   f_t l2_dual_residual_;
   f_t gap_;
   i_t num_iterations_;
-  bool solved_by_pdlp_;
+  method_t solved_by_;
 
   // PDLP warm start data (embedded struct, CPU-backed using std::vector)
   cpu_pdlp_warm_start_data_t<i_t, f_t> pdlp_warm_start_data_;

cpp/include/cuopt/linear_programming/optimization_problem_solution.hpp

@@ -122,9 +122,9 @@ class gpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
     return solution_.get_additional_termination_information(id).number_of_steps_taken;
   }
 
-  bool is_solved_by_pdlp(i_t id = 0) const override
+  method_t solved_by(i_t id = 0) const override
   {
-    return solution_.get_additional_termination_information(id).solved_by_pdlp;
+    return solution_.get_additional_termination_information(id).solved_by;
   }
 
   const pdlp_warm_start_data_t<i_t, f_t>& get_pdlp_warm_start_data() const override
@@ -338,7 +338,7 @@ class gpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
                                                            get_l2_dual_residual(),
                                                            get_gap(),
                                                            get_num_iterations(),
-                                                           is_solved_by_pdlp(),
+                                                           solved_by(),
                                                            std::move(cpu_ws));
     }
 
@@ -353,7 +353,7 @@ class gpu_lp_solution_t : public lp_solution_interface_t<i_t, f_t> {
                                                          get_l2_dual_residual(),
                                                          get_gap(),
                                                          get_num_iterations(),
-                                                         is_solved_by_pdlp());
+                                                         solved_by());
   }
 
   /**

cpp/include/cuopt/linear_programming/optimization_problem_solution_interface.hpp

@@ -291,10 +291,10 @@ class lp_solution_interface_t : public optimization_problem_solution_interface_t
   virtual i_t get_num_iterations(i_t id = 0) const = 0;
 
   /**
-   * @brief Check if solved by PDLP
-   * @return true if solved by PDLP
+   * @brief Method used for solving the LP.
+   * @return the method used for solving the LP.
    */
-  virtual bool is_solved_by_pdlp(i_t id = 0) const = 0;
+  virtual method_t solved_by(i_t id = 0) const = 0;
 
   /**
    * @brief Get PDLP warm start data (GPU solutions only)

cpp/include/cuopt/linear_programming/pdlp/solver_settings.hpp

@@ -50,19 +50,34 @@ enum pdlp_solver_mode_t : int {
  * @brief Enum representing the different methods that can be used to solve the
  * linear programming problem.
  *
- * Concurrent: Use both PDLP and DualSimplex in parallel.
+ * Concurrent: Use PDLP, Barrier and DualSimplex in parallel.
  * PDLP: Use the PDLP method.
  * DualSimplex: Use the dual simplex method.
+ * Barrier: Use the barrier method
+ * Unset: The value was not set.
  *
  * @note Default method is Concurrent.
  */
 enum method_t : int {
   Concurrent  = CUOPT_METHOD_CONCURRENT,
   PDLP        = CUOPT_METHOD_PDLP,
   DualSimplex = CUOPT_METHOD_DUAL_SIMPLEX,
-  Barrier     = CUOPT_METHOD_BARRIER
+  Barrier     = CUOPT_METHOD_BARRIER,
+  Unset       = CUOPT_METHOD_UNSET
 };
 
+/// Returns the corresponding string from the enum `method_t`.
+inline std::string method_to_string(method_t method)
+{
+  switch (method) {
+    case method_t::DualSimplex: return "Dual Simplex";
+    case method_t::PDLP: return "PDLP";
+    case method_t::Barrier: return "Barrier";
+    case method_t::Concurrent: return "Concurrent";
+    default: return "Unset";
+  }
+}
+
 /**
  * @brief Enum representing the PDLP precision modes.
  *

cpp/include/cuopt/linear_programming/pdlp/solver_solution.hpp

@@ -10,6 +10,7 @@
 #include <cuopt/linear_programming/constants.h>
 #include <cuopt/error.hpp>
 #include <cuopt/linear_programming/pdlp/pdlp_warm_start_data.hpp>
+#include <cuopt/linear_programming/pdlp/solver_settings.hpp>
 #include <cuopt/linear_programming/utilities/internals.hpp>
 
 #include <rmm/cuda_stream_view.hpp>
@@ -89,8 +90,8 @@ class optimization_problem_solution_t : public base_solution_t {
     /** Solve time in seconds */
     double solve_time{std::numeric_limits<double>::signaling_NaN()};
 
-    /** Whether the problem was solved by PDLP or Dual Simplex */
-    bool solved_by_pdlp{false};
+    /** Whether the problem was solved by PDLP, Barrier or Dual Simplex */
+    method_t solved_by = method_t::Unset;
   };
 
   /**

cpp/include/cuopt/linear_programming/utilities/cython_types.hpp

@@ -83,7 +83,7 @@ struct linear_programming_ret_t {
   double gap_{};
   int nb_iterations_{};
   double solve_time_{};
-  bool solved_by_pdlp_{};
+  linear_programming::method_t solved_by_{};
 
   bool is_gpu() const { return std::holds_alternative<gpu_solutions_t>(solutions_); }
 };

cpp/src/branch_and_bound/branch_and_bound.cpp

@@ -1972,18 +1972,21 @@ lp_status_t branch_and_bound_t<i_t, f_t>::solve_root_relaxation(
       set_uninitialized_steepest_edge_norms<i_t, f_t>(original_lp_, basic_list, edge_norms);
       user_objective = root_crossover_soln_.user_objective;
       iter           = root_crossover_soln_.iterations;
-      solver_name    = "Barrier/PDLP and Crossover";
+      solver_name    = method_to_string(root_relax_solved_by);
+
     } else {
-      root_status    = root_status_future.get();
-      user_objective = root_relax_soln_.user_objective;
-      iter           = root_relax_soln_.iterations;
-      solver_name    = "Dual Simplex";
+      root_status          = root_status_future.get();
+      user_objective       = root_relax_soln_.user_objective;
+      iter                 = root_relax_soln_.iterations;
+      root_relax_solved_by = DualSimplex;
+      solver_name          = "Dual Simplex";
     }
   } else {
-    root_status    = root_status_future.get();
-    user_objective = root_relax_soln_.user_objective;
-    iter           = root_relax_soln_.iterations;
-    solver_name    = "Dual Simplex";
+    root_status          = root_status_future.get();
+    user_objective       = root_relax_soln_.user_objective;
+    iter                 = root_relax_soln_.iterations;
+    root_relax_solved_by = DualSimplex;
+    solver_name          = "Dual Simplex";
   }
 
   settings_.log.printf("\n");

cpp/src/branch_and_bound/branch_and_bound.hpp

@@ -30,6 +30,8 @@
 #include <utilities/work_limit_context.hpp>
 #include <utilities/work_unit_scheduler.hpp>
 
+#include <cuopt/linear_programming/pdlp/solver_settings.hpp>
+
 #include <omp.h>
 
 #include <atomic>
@@ -89,7 +91,8 @@ class branch_and_bound_t {
                                     const std::vector<f_t>& reduced_costs,
                                     f_t objective,
                                     f_t user_objective,
-                                    i_t iterations)
+                                    i_t iterations,
+                                    method_t method)
   {
     if (!is_root_solution_set) {
       root_crossover_soln_.x              = primal;
@@ -99,6 +102,7 @@ class branch_and_bound_t {
       root_crossover_soln_.objective      = objective;
       root_crossover_soln_.user_objective = user_objective;
       root_crossover_soln_.iterations     = iterations;
+      root_relax_solved_by                = method;
       root_crossover_solution_set_.store(true, std::memory_order_release);
     }
   }
@@ -218,6 +222,7 @@ class branch_and_bound_t {
   f_t root_objective_;
   lp_solution_t<i_t, f_t> root_relax_soln_;
   lp_solution_t<i_t, f_t> root_crossover_soln_;
+  method_t root_relax_solved_by{Unset};
   std::vector<f_t> edge_norms_;
   std::atomic<bool> root_crossover_solution_set_{false};
   omp_atomic_t<f_t> root_lp_current_lower_bound_;

cpp/src/dual_simplex/crossover.cpp

@@ -612,7 +612,7 @@ i_t dual_push(const lp_problem_t<i_t, f_t>& lp,
       return TIME_LIMIT_RETURN;
     }
     if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) {
-      settings.log.printf("Concurrent halt\n");
+      if (!settings.inside_mip) { settings.log.printf("Concurrent halt\n"); }
       return CONCURRENT_HALT_RETURN;
     }
   }
@@ -989,7 +989,7 @@ i_t primal_push(const lp_problem_t<i_t, f_t>& lp,
       return TIME_LIMIT_RETURN;
     }
     if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) {
-      settings.log.printf("Concurrent halt\n");
+      if (!settings.inside_mip) { settings.log.printf("Concurrent halt\n"); }
       return CONCURRENT_HALT_RETURN;
     }
   }
@@ -1353,7 +1353,7 @@ crossover_status_t crossover(const lp_problem_t<i_t, f_t>& lp,
     return crossover_status_t::TIME_LIMIT;
   }
   if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) {
-    settings.log.printf("Concurrent halt\n");
+    if (!settings.inside_mip) { settings.log.printf("Concurrent halt\n"); }
     return crossover_status_t::CONCURRENT_LIMIT;
   }
 
@@ -1415,7 +1415,7 @@ crossover_status_t crossover(const lp_problem_t<i_t, f_t>& lp,
       return crossover_status_t::TIME_LIMIT;
     }
     if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) {
-      settings.log.printf("Concurrent halt\n");
+      if (!settings.inside_mip) { settings.log.printf("Concurrent halt\n"); }
       return crossover_status_t::CONCURRENT_LIMIT;
     }
     primal_infeas = primal_infeasibility(lp, settings, vstatus, solution.x);
@@ -1577,7 +1577,7 @@ crossover_status_t crossover(const lp_problem_t<i_t, f_t>& lp,
           return crossover_status_t::TIME_LIMIT;
         }
         if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) {
-          settings.log.printf("Concurrent halt\n");
+          if (!settings.inside_mip) { settings.log.printf("Concurrent halt\n"); }
           return crossover_status_t::CONCURRENT_LIMIT;
         }
         solution.iterations += iter;