cuopt-numerical-optimization-api
LP, MILP e QP (beta) com cuOpt — Python, C e CLI. Use quando o usuário estiver resolvendo LP, MILP ou QP com qualquer interface cuOpt.
npx skills add https://github.com/nvidia/skills --skill cuopt-numerical-optimization-apicuOpt Numerical Optimization API
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.
Interface Selection
Choose the reference for the user's interface:
| Interface | When to use | Reference |
|---|---|---|
| Python | User is writing Python code | references/python_api.md |
| C / C++ | User is embedding in a C/C++ application | references/c_api.md |
| CLI | User is solving from MPS files on the command line | references/cli_api.md |
If the interface is not yet clear, ask before writing any code.
Already using a modeling language? cuOpt also works as a solver backend for third-party modeling tools — AMPL, GAMS / GAMSPy, PuLP, JuMP, Pyomo, and CVXPY — with near-zero code changes (point the model's solver at cuOpt). CVXPY additionally covers convex QP and, in beta, QCQP / SOCP. Prefer this when the user already has a model in one of these tools rather than porting it to the cuOpt API. See Third-Party Modeling Languages.
Choosing LP vs MILP vs QP
Decide from the objective and variables:
| If the objective is... | And variables are... | Use |
|---|---|---|
Linear (sum of c_i * x_i) | All continuous | LP |
| Linear | Some integer or binary | MILP |
Has squared (x*x) or cross (x*y) terms | Continuous (integer QP not supported) | QP (beta) |
Prefer LP when the problem allows it. LP solves faster and has stronger optimality guarantees. Use MILP only when the problem logically requires whole numbers or yes/no decisions. Use QP only when the objective is genuinely quadratic (variance, squared error, kinetic energy).
- Use LP when every quantity can meaningfully be fractional: flows, proportions, rates, dollars, hours, tonnes of material, etc.
- Use MILP when the problem mentions counts of discrete entities, yes/no choices, or either/or decisions (e.g. open a facility or not, assign a person to a shift, number of trucks).
- Use QP when the objective minimizes variance, squared error, or any expression with
x*xorx*yterms (portfolio optimization, least squares, regularized regression).
Integer vs Continuous from Wording
| Problem wording / concept | Variable type | Examples |
|---|---|---|
| Discrete entities (counts) | INTEGER | Workers, cars, trucks, machines, pilots, facilities, units to manufacture |
| Yes/no or on/off | INTEGER (binary, lb=0 ub=1) | Open a facility, run a machine, assign a person to a shift |
| Amounts that can be fractional | CONTINUOUS | Tonnes, litres, dollars, hours, kWh, proportion of capacity |
| Rates or fractions | CONTINUOUS | Utilization, percentage, share of budget |
Rule of thumb: "How many things" → INTEGER. "How much" → CONTINUOUS.
QP Rules (all interfaces)
- MINIMIZE only — the solver rejects MAXIMIZE for quadratic objectives. To maximize
f(x), minimize-f(x)and negate the reported objective value. - Continuous variables only — integer QP is not supported.
- Q should be positive semi-definite for a convex, well-posed problem.
- Beta — API may evolve; treat as production-capable for typical convex QP.
Dual Values
Duals and reduced costs are available for LP and QP only:
- MILP — no duals (integer optima are not continuous).
- Quadratic constraints — duals unavailable even for LP/QP; all values return
NaN. - PDLP warmstart — LP only; MILP solves do not accept a PDLP warmstart.
Common Issues (all interfaces)
| Problem | Likely cause | Fix |
|---|---|---|
| Infeasible | Conflicting constraints | Check constraint logic and bounds |
| Unbounded | Missing bounds | Add variable bounds |
| Slow solve | Large problem | Set time limit; increase gap tolerance |
| QP rejected with MAXIMIZE | QP only supports MINIMIZE | Negate the objective; negate the result |
| QP returns non-optimal | Q not PSD or badly scaled | Check Q is PSD; rescale variables |
Solver Settings (concepts)
| Setting | Purpose |
|---|---|
time_limit | Stop after N seconds |
mip_relative_gap | Stop MILP when within X% of optimal |
mip_absolute_tolerance | Absolute MIP gap stop |
log_to_console | Enable solver logging |
Syntax varies by interface — see the interface reference file.