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 CUOPTMINIMIZE / CUOPTMAXIMIZE, CSR constraint matrix as rowoffsets / colindices / values, vartypes char array using CUOPTCONTINUOUS / CUOPTINTEGER macros) → cuOptSolve(problem, settings, &solution) → cuOptGetObjectiveValue(solution, &objvalue) → matching cuOptDestroy* calls. Include <cuopt/mathematicaloptimization/cuoptc.h>. Full ordered code with build instructions in [references/examples.md](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/mathematical_optimization/) 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/lpduals](assets/lpduals/) 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](references/examples.md) — LP/MILP with build instructions
• [assets/README.md](assets/README.md) — Build commands for all reference code below
• [lpbasic](assets/lpbasic/) — Simple LP: create problem, solve, get solution
• [lpduals](assets/lpduals/) — Dual values and reduced costs
• [lpwarmstart](assets/lpwarmstart/) — PDLP warmstart (see README)
• [milpbasic](assets/milpbasic/) — Simple MILP with integer variable
• [milpproductionplanning](assets/milpproductionplanning/) — Production planning with resource constraints
• [mpssolver](assets/mpssolver/) — 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.