cuopt-numerical-optimization-api-c

cuOpt Numerical Optimization — C API

Solve LP, MILP, and QP problems via the cuOpt C API. The same library, headers, build pattern, and core calls (cuOptCreate*Problem, cuOptSolve, cuOptGetObjectiveValue) apply across all three; QP extends the API with quadratic-objective creation calls.

Confirm problem type and formulation (variables, objective, constraints, variable types) before coding.

This skill is C only.

API Call Sequence

For LP/MILP, the ordered C entry points are: cuOptCreateRangedProblem (sense CUOPT_MINIMIZE / CUOPT_MAXIMIZE, CSR constraint matrix as row_offsets / col_indices / values, var_types char array using CUOPT_CONTINUOUS / CUOPT_INTEGER macros) → cuOptSolve(problem, settings, &solution) → cuOptGetObjectiveValue(solution, &obj_value) → matching cuOptDestroy* calls. Include <cuopt/linear_programming/cuopt_c.h>. Full ordered code with build instructions in references/examples.md.

QP via C API (beta)

QP uses the same library, include/lib paths, and build pattern as LP/MILP — only the problem-creation call differs (it accepts a quadratic objective). See the cuOpt C headers (cpp/include/cuopt/linear_programming/) for the QP-specific creation/solve calls and the repo docs at docs/cuopt/source/cuopt-c/lp-qp-milp/ for end-to-end QP examples.

QP rules:

• MINIMIZE only (CUOPT_MINIMIZE). To maximize f(x), negate objective coefficients and Q entries.
• Continuous variables only — set CUOPT_CONTINUOUS for every variable; integer QP is not supported.
• Q should be PSD for a convex problem.

Dual values (LP / QP)

cuOptGetDualSolution and cuOptGetReducedCosts return duals and reduced costs for LP and QP. They are not returned for a problem with quadratic constraints (the arrays are filled with NaN), so read them only when all constraints are linear. See assets/lp_duals for the call sequence.

Debugging (MPS / C)

MPS parsing: Required sections in order: NAME, ROWS, COLUMNS, RHS, (optional) BOUNDS, ENDATA. Integer markers: 'MARKER', 'INTORG', 'INTEND'.

OOM or slow: Check problem size (variables, constraints); use sparse matrix; set time limit and gap tolerance.

Examples

• examples.md — LP/MILP with build instructions
• assets/README.md — Build commands for all reference code below
• lp_basic — Simple LP: create problem, solve, get solution
• lp_duals — Dual values and reduced costs
• lp_warmstart — PDLP warmstart (see README)
• milp_basic — Simple MILP with integer variable
• milp_production_planning — Production planning with resource constraints
• mps_solver — Solve from MPS file via cuOptReadProblem

For CLI (MPS files), use cuopt_cli and product docs.

Escalate

For contribution or build-from-source, use product or repo documentation.