Agent skill · NVIDIA
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
What it needs
About 3k tokens when loaded.
What this skill does
cuOpt 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/pythonapi.md C / C++ User is embedding in a C/C++ application references/capi.md CLI User is solving from MPS files on the command line references/cliapi.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 ci xi) All continuous LP Linear Some integer or binary MILP Has squared (xx) or cross (xy) 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 xx or xy terms (portfolio optimization, least squares, regularized regression). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/cuopt-numerical-optimization-api