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/cuopt-numerical-optimization-api

@e0cd22d
by NVIDIA Corporationnvidia/skills3.5k stars
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LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

Use this Skill: https://skilld.dev/gh/nvidia/skills/cuopt-numerical-optimization-api

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assetscREADME.md

≈428 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Assets — reference C examples

LP/MILP C API reference implementations. Use as reference when building new applications; do not edit in place. Build requires cuOpt installed (include and lib paths set).

Example Type Description
lp_basic LP Simple LP: create problem, solve, get solution
lp_duals LP Dual values and reduced costs
lp_warmstart LP PDLP warmstart (see README)
milp_basic MILP Simple MILP with integer variable
milp_production_planning MILP Production planning with resource constraints
mps_solver LP/MILP Solve from MPS file via cuOptReadProblem

Build and run

Set include and library paths, then build and run.

Using conda: Activate your cuOpt env first (conda activate cuopt), then:

# Paths from active conda env (CONDA_PREFIX is set when env is activated)
export INCLUDE_PATH="${CONDA_PREFIX}/include"
export LIB_PATH="${CONDA_PREFIX}/lib"
export LD_LIBRARY_PATH="${LIB_PATH}:${LD_LIBRARY_PATH}"

# Build and run (from this assets/ directory) — example: lp_basic
gcc -I"${INCLUDE_PATH}" -L"${LIB_PATH}" -o lp_basic/lp_simple lp_basic/lp_simple.c -lcuopt
./lp_basic/lp_simple

For the other examples, use the same pattern (e.g. lp_duals/lp_duals.c → lp_duals/lp_duals). mps_solver takes an MPS file path: ./mps_solver mps_solver/data/sample.mps.

Without conda, set INCLUDE_PATH and LIB_PATH to your cuOpt include and lib directories, then use the same gcc and LD_LIBRARY_PATH as above. Each subdirectory README has a one-line build/run for that example.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    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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    Risk: MEDIUM · 1 issue

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
version
26.10.00
Other metadata
metadata
{
  "author": "NVIDIA cuOpt Team",
  "tags": [
    "cuopt",
    "linear-programming",
    "milp",
    "qp",
    "python",
    "c-api",
    "cli"
  ]
}

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