LP C API Examples#
Example With Data#
This example demonstrates how to use the LP solver in C. More details on the API can be found in C API.
Copy the code below into a file called lp_example.c:
/*
* Simple test program for cuOpt linear programming solver
*/
// Include the cuOpt linear programming solver header
#include <cuopt/linear_programming/cuopt_c.h>
#include <stdio.h>
#include <stdlib.h>
// Convert termination status to string
const char* termination_status_to_string(cuopt_int_t termination_status)
{
switch (termination_status) {
case CUOPT_TERIMINATION_STATUS_OPTIMAL:
return "Optimal";
case CUOPT_TERIMINATION_STATUS_INFEASIBLE:
return "Infeasible";
case CUOPT_TERIMINATION_STATUS_UNBOUNDED:
return "Unbounded";
case CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT:
return "Iteration limit";
case CUOPT_TERIMINATION_STATUS_TIME_LIMIT:
return "Time limit";
case CUOPT_TERIMINATION_STATUS_NUMERICAL_ERROR:
return "Numerical error";
case CUOPT_TERIMINATION_STATUS_PRIMAL_FEASIBLE:
return "Primal feasible";
case CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND:
return "Feasible found";
default:
return "Unknown";
}
}
// Test simple LP problem
cuopt_int_t test_simple_lp()
{
cuOptOptimizationProblem problem = NULL;
cuOptSolverSettings settings = NULL;
cuOptSolution solution = NULL;
/* Solve the following LP:
minimize -0.2*x1 + 0.1*x2
subject to:
3.0*x1 + 4.0*x2 <= 5.4
2.7*x1 + 10.1*x2 <= 4.9
x1, x2 >= 0
*/
cuopt_int_t num_variables = 2;
cuopt_int_t num_constraints = 2;
cuopt_int_t nnz = 4;
// CSR format constraint matrix
// https://docs.nvidia.com/nvpl/latest/sparse/storage_format/sparse_matrix.html#compressed-sparse-row-csr
// From the constraints:
// 3.0*x1 + 4.0*x2 <= 5.4
// 2.7*x1 + 10.1*x2 <= 4.9
cuopt_int_t row_offsets[] = {0, 2, 4};
cuopt_int_t column_indices[] = {0, 1, 0, 1};
cuopt_float_t values[] = {3.0, 4.0, 2.7, 10.1};
// Objective coefficients
// From the objective function: minimize -0.2*x1 + 0.1*x2
// -0.2 is the coefficient of x1
// 0.1 is the coefficient of x2
cuopt_float_t objective_coefficients[] = {-0.2, 0.1};
// Constraint bounds
// From the constraints:
// 3.0*x1 + 4.0*x2 <= 5.4
// 2.7*x1 + 10.1*x2 <= 4.9
cuopt_float_t constraint_upper_bounds[] = {5.4, 4.9};
cuopt_float_t constraint_lower_bounds[] = {-CUOPT_INFINITY, -CUOPT_INFINITY};
// Variable bounds
// From the constraints:
// x1, x2 >= 0
cuopt_float_t var_lower_bounds[] = {0.0, 0.0};
cuopt_float_t var_upper_bounds[] = {CUOPT_INFINITY, CUOPT_INFINITY};
// Variable types (continuous)
// From the constraints:
// x1, x2 >= 0
char variable_types[] = {CUOPT_CONTINUOUS, CUOPT_CONTINUOUS};
cuopt_int_t status;
cuopt_float_t time;
cuopt_int_t termination_status;
cuopt_float_t objective_value;
printf("Creating and solving simple LP problem...\n");
// Create the problem
status = cuOptCreateRangedProblem(num_constraints,
num_variables,
CUOPT_MINIMIZE, // minimize=False
0.0, // objective offset
objective_coefficients,
row_offsets,
column_indices,
values,
constraint_lower_bounds,
constraint_upper_bounds,
var_lower_bounds,
var_upper_bounds,
variable_types,
&problem);
if (status != CUOPT_SUCCESS) {
printf("Error creating problem: %d\n", status);
goto DONE;
}
// Create solver settings
status = cuOptCreateSolverSettings(&settings);
if (status != CUOPT_SUCCESS) {
printf("Error creating solver settings: %d\n", status);
goto DONE;
}
// Set solver parameters
status = cuOptSetFloatParameter(settings, CUOPT_ABSOLUTE_PRIMAL_TOLERANCE, 0.0001);
if (status != CUOPT_SUCCESS) {
printf("Error setting optimality tolerance: %d\n", status);
goto DONE;
}
// Solve the problem
status = cuOptSolve(problem, settings, &solution);
if (status != CUOPT_SUCCESS) {
printf("Error solving problem: %d\n", status);
goto DONE;
}
// Get solution information
status = cuOptGetSolveTime(solution, &time);
if (status != CUOPT_SUCCESS) {
printf("Error getting solve time: %d\n", status);
goto DONE;
}
status = cuOptGetTerminationStatus(solution, &termination_status);
if (status != CUOPT_SUCCESS) {
printf("Error getting termination status: %d\n", status);
goto DONE;
}
status = cuOptGetObjectiveValue(solution, &objective_value);
if (status != CUOPT_SUCCESS) {
printf("Error getting objective value: %d\n", status);
goto DONE;
}
// Print results
printf("\nResults:\n");
printf("--------\n");
printf("Termination status: %s (%d)\n", termination_status_to_string(termination_status), termination_status);
printf("Solve time: %f seconds\n", time);
printf("Objective value: %f\n", objective_value);
// Get and print solution variables
cuopt_float_t* solution_values = (cuopt_float_t*)malloc(num_variables * sizeof(cuopt_float_t));
status = cuOptGetPrimalSolution(solution, solution_values);
if (status != CUOPT_SUCCESS) {
printf("Error getting solution values: %d\n", status);
free(solution_values);
goto DONE;
}
printf("\nPrimal Solution: Solution variables \n");
for (cuopt_int_t i = 0; i < num_variables; i++) {
printf("x%d = %f\n", i + 1, solution_values[i]);
}
free(solution_values);
DONE:
cuOptDestroyProblem(&problem);
cuOptDestroySolverSettings(&settings);
cuOptDestroySolution(&solution);
return status;
}
int main() {
// Run the test
cuopt_int_t status = test_simple_lp();
if (status == CUOPT_SUCCESS) {
printf("\nTest completed successfully!\n");
return 0;
} else {
printf("\nTest failed with status: %d\n", status);
return 1;
}
}
It is necessary to have the path for include and library dirs ready, if you know the paths, please add them to the path variables directly. Otherwise, run the following commands to find the path and assign it to the path variables. The following commands are for Linux and might fail in cases where the cuopt library is not installed or there are multiple cuopt libraries in the system.
If you have built it locally, libcuopt.so will be in the build directory cpp/build and include directoy would be cpp/include.
# Find the cuopt header file and assign to INCLUDE_PATH
INCLUDE_PATH=$(find / -name "cuopt_c.h" -path "*/linear_programming/*" -printf "%h\n" | sed 's/\/linear_programming//' 2>/dev/null)
# Find the libcuopt library and assign to LIBCUOPT_LIBRARY_PATH
LIBCUOPT_LIBRARY_PATH=$(find / -name "libcuopt.so" 2>/dev/null)
Build and run the example
# Build and run the example
gcc -I $INCLUDE_PATH -L $LIBCUOPT_LIBRARY_PATH -o lp_example lp_example.c -lcuopt
./lp_example
You should see the following output:
Output#
Creating and solving simple LP problem...
Solving a problem with 2 constraints 2 variables (0 integers) and 4 nonzeros
Objective offset 0.000000 scaling_factor 1.000000
Running concurrent
Dual simplex finished in 0.00 seconds
Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time
0 +0.00000000e+00 +0.00000000e+00 0.00e+00 0.00e+00 2.00e-01 0.011s
PDLP finished
Concurrent time: 0.013s
Solved with dual simplex
Status: Optimal Objective: -3.60000000e-01 Iterations: 1 Time: 0.013s
Results:
--------
Termination status: Optimal (1)
Solve time: 0.000013 seconds
Objective value: -0.360000
Primal Solution: Solution variables
x1 = 1.800000
x2 = 0.000000
Test completed successfully!
Example With MPS File#
This example demonstrates how to use the cuOpt linear programming solver in C to solve an MPS file.
Copy the code below into a file called lp_example_mps.c:
/*
* Example program for solving MPS files with cuOpt linear programming solver
*/
#include <cuopt/linear_programming/cuopt_c.h>
#include <stdio.h>
#include <stdlib.h>
const char* termination_status_to_string(cuopt_int_t termination_status)
{
switch (termination_status) {
case CUOPT_TERIMINATION_STATUS_OPTIMAL:
return "Optimal";
case CUOPT_TERIMINATION_STATUS_INFEASIBLE:
return "Infeasible";
case CUOPT_TERIMINATION_STATUS_UNBOUNDED:
return "Unbounded";
case CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT:
return "Iteration limit";
case CUOPT_TERIMINATION_STATUS_TIME_LIMIT:
return "Time limit";
case CUOPT_TERIMINATION_STATUS_NUMERICAL_ERROR:
return "Numerical error";
case CUOPT_TERIMINATION_STATUS_PRIMAL_FEASIBLE:
return "Primal feasible";
case CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND:
return "Feasible found";
default:
return "Unknown";
}
}
cuopt_int_t solve_mps_file(const char* filename)
{
cuOptOptimizationProblem problem = NULL;
cuOptSolverSettings settings = NULL;
cuOptSolution solution = NULL;
cuopt_int_t status;
cuopt_float_t time;
cuopt_int_t termination_status;
cuopt_float_t objective_value;
cuopt_int_t num_variables;
cuopt_float_t* solution_values = NULL;
printf("Reading and solving MPS file: %s\n", filename);
// Create the problem from MPS file
status = cuOptReadProblem(filename, &problem);
if (status != CUOPT_SUCCESS) {
printf("Error creating problem from MPS file: %d\n", status);
goto DONE;
}
// Get problem size
status = cuOptGetNumVariables(problem, &num_variables);
if (status != CUOPT_SUCCESS) {
printf("Error getting number of variables: %d\n", status);
goto DONE;
}
// Create solver settings
status = cuOptCreateSolverSettings(&settings);
if (status != CUOPT_SUCCESS) {
printf("Error creating solver settings: %d\n", status);
goto DONE;
}
// Set solver parameters
status = cuOptSetFloatParameter(settings, CUOPT_ABSOLUTE_PRIMAL_TOLERANCE, 0.0001);
if (status != CUOPT_SUCCESS) {
printf("Error setting optimality tolerance: %d\n", status);
goto DONE;
}
// Solve the problem
status = cuOptSolve(problem, settings, &solution);
if (status != CUOPT_SUCCESS) {
printf("Error solving problem: %d\n", status);
goto DONE;
}
// Get solution information
status = cuOptGetSolveTime(solution, &time);
if (status != CUOPT_SUCCESS) {
printf("Error getting solve time: %d\n", status);
goto DONE;
}
status = cuOptGetTerminationStatus(solution, &termination_status);
if (status != CUOPT_SUCCESS) {
printf("Error getting termination status: %d\n", status);
goto DONE;
}
status = cuOptGetObjectiveValue(solution, &objective_value);
if (status != CUOPT_SUCCESS) {
printf("Error getting objective value: %d\n", status);
goto DONE;
}
// Print results
printf("\nResults:\n");
printf("--------\n");
printf("Number of variables: %d\n", num_variables);
printf("Termination status: %s (%d)\n", termination_status_to_string(termination_status), termination_status);
printf("Solve time: %f seconds\n", time);
printf("Objective value: %f\n", objective_value);
// Get and print solution variables
solution_values = (cuopt_float_t*)malloc(num_variables * sizeof(cuopt_float_t));
status = cuOptGetPrimalSolution(solution, solution_values);
if (status != CUOPT_SUCCESS) {
printf("Error getting solution values: %d\n", status);
goto DONE;
}
printf("\nPrimal Solution: First 10 solution variables (or fewer if less exist):\n");
for (cuopt_int_t i = 0; i < (num_variables < 10 ? num_variables : 10); i++) {
printf("x%d = %f\n", i + 1, solution_values[i]);
}
if (num_variables > 10) {
printf("... (showing only first 10 of %d variables)\n", num_variables);
}
DONE:
free(solution_values);
cuOptDestroyProblem(&problem);
cuOptDestroySolverSettings(&settings);
cuOptDestroySolution(&solution);
return status;
}
int main(int argc, char* argv[]) {
if (argc != 2) {
printf("Usage: %s <mps_file_path>\n", argv[0]);
return 1;
}
// Run the solver
cuopt_int_t status = solve_mps_file(argv[1]);
if (status == CUOPT_SUCCESS) {
printf("\nSolver completed successfully!\n");
return 0;
} else {
printf("\nSolver failed with status: %d\n", status);
return 1;
}
}
It is necessary to have the path for include and library dirs ready, if you know the paths, please add them to the path variables directly. Otherwise, run the following commands to find the path and assign it to the path variables. The following commands are for Linux and might fail in cases where the cuopt library is not installed or there are multiple cuopt libraries in the system.
If you have built it locally, libcuopt.so will be in the build directory cpp/build and include directoy would be cpp/include.
# Find the cuopt header file and assign to INCLUDE_PATH
INCLUDE_PATH=$(find / -name "cuopt_c.h" -path "*/linear_programming/*" -printf "%h\n" | sed 's/\/linear_programming//' 2>/dev/null)
# Find the libcuopt library and assign to LIBCUOPT_LIBRARY_PATH
LIBCUOPT_LIBRARY_PATH=$(find / -name "libcuopt.so" 2>/dev/null)
Build and run the example
# Create a MPS file in the current directory
echo "* optimize
* cost = -0.2 * VAR1 + 0.1 * VAR2
* subject to
* 3 * VAR1 + 4 * VAR2 <= 5.4
* 2.7 * VAR1 + 10.1 * VAR2 <= 4.9
NAME good-1
ROWS
N COST
L ROW1
L ROW2
COLUMNS
VAR1 COST -0.2
VAR1 ROW1 3 ROW2 2.7
VAR2 COST 0.1
VAR2 ROW1 4 ROW2 10.1
RHS
RHS1 ROW1 5.4 ROW2 4.9
ENDATA" > sample.mps
# Build and run the example
gcc -I $INCLUDE_PATH -L $LIBCUOPT_LIBRARY_PATH -o lp_example_mps lp_example_mps.c -lcuopt
./lp_example_mps sample.mps
You should see the following output:
Output#
Reading and solving MPS file: sample.mps
Solving a problem with 2 constraints 2 variables (0 integers) and 4 nonzeros
Objective offset 0.000000 scaling_factor 1.000000
Running concurrent
Dual simplex finished in 0.00 seconds
Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time
0 +0.00000000e+00 +0.00000000e+00 0.00e+00 0.00e+00 2.00e-01 0.012s
PDLP finished
Concurrent time: 0.014s
Solved with dual simplex
Status: Optimal Objective: -3.60000000e-01 Iterations: 1 Time: 0.014s
Results:
--------
Number of variables: 2
Termination status: Optimal (1)
Solve time: 0.000014 seconds
Objective value: -0.360000
Primal Solution: First 10 solution variables (or fewer if less exist):
x1 = 1.800000
x2 = 0.000000
Solver completed successfully!