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LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI.
Passed 22 of 22 test cases with the skill, and 16 of 22 without it.
and 3 more in the full report
6 fixed16 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
My model minimizes total hourly wage cost for hospital nursing shifts. Decision variables are the whole number of nurses assigned to each shift, subject to minimum linear coverage requirements. Which problem class is appropriate?
| Checked | Without skill | With skill |
|---|---|---|
| Classify the problem as Mixed-Integer Linear Programming (MILP). | Missed | Passed |
The instructions your model reads when the skill loads.
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver.
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.
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).
x*x or x*y terms (portfolio optimization, least squares, regularized regression).test cases passed, vs 16 / 22 without
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