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cuopt-lp-milp-api-python

Formulate and solve LP and MILP problems with cuOpt's Python API.

2.8k|332|Updated Feb 25, 2026
Search Tags:#optimization#python-api#integer-programming#milp#linear-programming#lp#cuopt
One-click install
npx skills add https://github.com/NVIDIA/skills --skill cuopt-lp-milp-api-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuopt-lp-milp-api-python
Source: https://github.com/NVIDIA/skills/tree/main/skills/cuopt/cuopt-lp-milp-api-python
Command: npx skills add https://github.com/NVIDIA/skills --skill cuopt-lp-milp-api-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

Formulates and solves Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) problems using cuOpt's GPU-accelerated Python API.

Core Features & Use Cases

  • Formulate problems by creating a cuOpt Problem, adding variables (CONTINUOUS or INTEGER), constraints, and an objective.
  • Solve with SolverSettings, including time limits and mip-relative gaps, and read model status and solution values.
  • Reference models and tutorials live in the assets directory, e.g., minimal LP/MILP examples and production planning models to illustrate common use cases.

Quick Start

Create a cuOpt Problem, add variables and constraints, set an objective, then call solve with a SolverSettings.

Frequently Asked Questions about cuopt-lp-milp-api-python

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

FAQPage Schema
How do I formulate and solve linear programming problems in Python using cuOpt?▼

To solve linear programming problems in Python, create a cuOpt Problem, add variables, constraints, and an objective, then call solve with configurable SolverSettings to get solution values.

Can I solve mixed-integer linear programming models with cuOpt's Python API?▼

Yes, you can solve mixed-integer linear programming models by adding INTEGER variables to your cuOpt Problem and configuring solver settings like mip-relative gaps for optimal solutions.

What types of optimization tasks does cuOpt support for engineering workflows?▼

cuOpt supports optimization tasks such as scheduling, resource allocation, facility location, and production planning in engineering and analytics workflows using linear and mixed-integer programming.

How do I configure solver settings like time limits and gaps for MILP in cuOpt?▼

You can configure MILP solver settings in cuOpt by using SolverSettings to define time limits and mip-relative gaps before calling the solve function on your problem.

Do I need specific Python packages to run cuOpt for LP and MILP problems?▼

Yes, you need Python and cuOpt's linear programming package installed to formulate and solve LP and MILP problems using the GPU-accelerated Python API.

Are there reference models available for production planning with linear programming?▼

Yes, reference models and tutorials, including minimal LP/MILP examples and production planning models, are available in the assets directory to illustrate common use cases.

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