cuopt-numerical-optimization-api-python
Installation
SKILL.md
cuOpt Numerical Optimization Skill (Python)
Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver. The Python API surface (Problem, SolverSettings, solve) is shared across all three problem classes — only the objective form and a few rules change.
Before You Start
Use a formulation summary (parameters, constraints, decisions, objective) if available; otherwise ask for decision variables, objective, and constraints. Then confirm problem type (LP / MILP / QP — see below) and variable types.
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) |