cuopt-numerical-optimization-api-python

Solve LP, MILP, and QP problems with NVIDIA cuOpt's Python API.

Updated Apr 16, 2026

Search Tags:#python #optimization #solver #milp #lp #qp #cuopt

Ready to run

npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cuopt-numerical-optimization-api-python-sayalinvidia

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cuopt-numerical-optimization-api-python
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/cuopt-numerical-optimization-api-python
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cuopt-numerical-optimization-api-python-sayalinvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Model and solve LP, MILP, and QP optimization problems using NVIDIA cuOpt's GPU-accelerated Python API, enabling fast, scalable decision-making across operations research tasks.

Core Features & Use Cases

Unified Python API to define LP, MILP, and QP problems with variables, constraints, and objective functions.
Supports continuous and integer variables, standard solver settings, status checking, and solution extraction (ObjValue, variable values, and duals for LP/MILP).
Typical use cases include portfolio optimization, production planning, facility location, resource allocation, scheduling, and other optimization problems in finance and operations.

Quick Start

Create a cuOpt Problem, add variables and constraints, set an objective (MAXIMIZE or MINIMIZE), and call solve with SolverSettings to obtain the optimal solution.

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 MILP and LP optimization problems in Python?▼

To solve MILP and LP optimization problems in Python, you can define variables, constraints, and objective functions using the cuOpt Python API, then call solve with SolverSettings to obtain optimal variable values and objective results.

What types of optimization problems does the cuOpt Python API support?▼

The cuOpt Python API supports solving Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Quadratic Programming (QP) problems, accommodating both continuous and integer variables for portfolio, production, and scheduling scenarios.

Can I extract dual values when solving LP and MILP problems with GPU acceleration?▼

Yes, solving LP and MILP problems with the cuOpt Python API allows you to extract dual values, alongside standard solver results like objective values and variable values, for comprehensive operations research analysis.

How do I set up a quadratic programming problem for portfolio optimization in Python?▼

To set up a quadratic programming problem for portfolio optimization in Python, create a cuOpt Problem instance, add your continuous variables and linear constraints, define a QP objective, and execute the solve command.

Is there a GPU-accelerated Python solver for large-scale production planning and scheduling?▼

Yes, the cuOpt Python API is a GPU-accelerated solver designed for large-scale production planning, facility location, and scheduling tasks, enabling fast and scalable decision-making across operations research workflows.