Solve quadratic programming problems from the command line using cuOpt's CLI.
npx skills add https://github.com/NVIDIA/skills --skill cuopt-qp-api-cli
Please help me install this Agent Skill. Skill: cuopt-qp-api-cli Source: https://github.com/NVIDIA/skills/tree/main/skills/cuopt/cuopt-qp-api-cli Command: npx skills add https://github.com/NVIDIA/skills --skill cuopt-qp-api-cli
Quadratic programming problems can be solved from the command line using cuOpt's CLI, enabling rapid experimentation and automation without a GUI.
Run cuopt_cli on a QP-formatted input file (e.g., problem.mps) from the command line.
High-intent search queries and answers about installing and using this skill.
Solve quadratic programming problems by running the cuOpt CLI on a QP-formatted input file, such as an MPS file. This enables rapid automation and experimentation directly from the terminal without a GUI.
Yes, the cuOpt CLI supports quadratic objectives, allowing you to extend linear programming workflows to include quadratic terms. You can consult the CLI help and docs for specific formats and flags.
Solving QP instances from the terminal requires installing the cuOpt package and the cuopt_cli. You can then run the CLI on your input files with optional time limits for execution control.
Yes, the CLI supports solving QP instances from MPS-like input files with optional time limits. This helps manage execution time during prototyping and deployment contexts.
Use the LP/MILP CLI as an alternative workflow when dealing with strictly linear programming problems. The QP CLI is specifically designed for formulations that include quadratic terms.
yo-stevenSolve LP, MILP, and beta QP problems from MPS files via cuopt_cli.
sayalinvidiaSolve LP, MILP, and QP problems via the cuOpt C API.
sayalinvidiaSolve LP, MILP, and QP problems with NVIDIA cuOpt's Python API.
jkitchinClassify Python optimization problems and map them to solvers.
