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cuopt-numerical-optimization-api-python

Build and solve LP, MILP, and QP models with NVIDIA cuOpt's Python API.

1.0k|218|Updated Apr 8, 2025
Search Tags:#python#optimization#gpu-accelerated#milp#linear-programming#quadratic-programming#vehicle-routing
One-click install
npx skills add https://github.com/NVIDIA/cuopt --skill cuopt-numerical-optimization-api-python-nvidia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuopt-numerical-optimization-api-python
Source: https://github.com/NVIDIA/cuopt/tree/main/skills/cuopt-numerical-optimization-api-python
Command: npx skills add https://github.com/NVIDIA/cuopt --skill cuopt-numerical-optimization-api-python-nvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

This Skill helps you formulate and solve linear, mixed-integer, and (beta) quadratic optimization models using NVIDIA cuOpt’s GPU-accelerated Python API.

Core Features & Use Cases

  • LP (Linear Programming): optimize a linear objective with continuous decision variables under linear constraints (e.g., production/resource allocation with fractional quantities).
  • MILP (Mixed-Integer Linear Programming): optimize a linear objective with integer/binary decision variables under linear constraints (e.g., facility location, scheduling, assignment, lot sizing).
  • QP (Quadratic Programming, beta): optimize quadratic objectives with continuous variables only (e.g., portfolio variance minimization, least-squares, quadratic cost/risk terms).
  • Practical modeling guidance: choose LP vs MILP vs QP correctly, set variable types from problem wording, and handle solver status checks using required PascalCase status names.

Quick Start

Ask: "Solve my optimization problem with cuOpt’s Python API as an LP, MILP, or QP—here are the objective, decision variables, bounds, and constraints."

Frequently Asked Questions about cuopt-numerical-optimization-api-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I solve linear programming and MILP models in Python using GPU acceleration?▼

To solve linear programming (LP) and mixed-integer linear programming (MILP) models in Python, you can build a cuOpt Problem, define variable types, set linear objectives and constraints, and execute problem.solve with SolverSettings for GPU acceleration.

Can I use quadratic programming for portfolio optimization and least-squares problems?▼

Yes, you can apply quadratic programming (QP) to portfolio variance minimization and least-squares problems by defining quadratic objectives with continuous variables, though QP support is currently in beta.

When do I need mixed-integer linear programming instead of standard linear programming?▼

You need mixed-integer linear programming (MILP) instead of standard linear programming (LP) when your model requires integer or binary decision variables for scenarios like facility location, scheduling, or assignment tasks.

How do I check the solver status after running an optimization problem in Python?▼

After running your optimization problem, you validate the results by checking the solver status using the required PascalCase problem.Status.name attribute provided by the cuOpt Python API.

What are the limitations of using beta quadratic programming with continuous variables?▼

The limitation of beta quadratic programming (QP) is that it supports continuous variables only, meaning it cannot handle integer or binary decision variables in quadratic objectives like portfolio optimization models.

Does cuOpt's Python API support vehicle routing and production planning optimization?▼

Yes, cuOpt's Python API applies to vehicle routing, production planning, scheduling, and resource allocation by formulating and solving linear, mixed-integer, and quadratic optimization models.

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