cuopt-numerical-optimization-api-python

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 ( 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). Show more Installs 492 Repository nvidia/skills GitHub Stars 1.2K First Seen May 15, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Warn