LP Duals and Reduced Costs
Retrieve dual values (shadow prices) and reduced costs after solving an LP.
Problem: Minimize 3x + 2y + 5z subject to x + y + z = 4, 2x + y + z = 5, x, y, z ≥ 0.
Run: python model.py
/cuopt-numerical-optimization-api
@e0cd22dLP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
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Use this Skill: https://skilld.dev/gh/nvidia/skills/cuopt-numerical-optimization-api
This session only. Nothing lands on disk.
≈57 tokens on demand. Your agent reads this file only when SKILL.md points to it.
Source: SKILL.md on GitHub
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The skill provides a comprehensive environment for modeling and solving optimization problems using the NVIDIA cuOpt library. It includes well-documented examples and reference implementations for Python, C, and CLI interfaces. Security analysis identified a remote data download from a reputable academic source and standard data ingestion patterns for optimization files, neither of which present malicious risk.
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Provenance
Signed by skilld at e0cd22d. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.
Last checked against GitHub yesterday.
Activeupdated 2 months ago
Capability
- version
- 26.10.00
Other metadata
- metadata
{ "author": "NVIDIA cuOpt Team", "tags": [ "cuopt", "linear-programming", "milp", "qp", "python", "c-api", "cli" ] }