// LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from MPS via command line.
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
After solving a non-trivial problem, detect generalizable learnings and propose skill updates so future interactions benefit automatically. Always active — applies to every interaction.
Install cuOpt for Python, C, or as a server (pip, conda, Docker) — system requirements, install commands, and verification. Use when the user wants to install or verify cuOpt for any user-facing interface. For building cuOpt from source or contributing to cuOpt, see cuopt-developer.
LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.
Solve Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Quadratic Programming (QP, beta) with the Python API. Use when the user asks about optimization with linear or quadratic objectives, linear constraints, integer variables, scheduling, resource allocation, facility location, production planning, portfolio optimization, or least squares.
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
| name | cuopt-numerical-optimization-api-cli |
| version | 26.08.00 |
| description | LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from MPS via command line. |
Solve LP, MILP, and QP problems from MPS files via cuopt_cli. The same command, options, and MPS workflow apply across all three; QP uses the standard MPS quadratic-objective extension.
Confirm problem type and formulation (variables, objective, constraints, variable types) before coding.
This skill is CLI only (MPS input).
# Solve LP or MILP from MPS file
cuopt_cli problem.mps
# With options
cuopt_cli problem.mps --time-limit 120 --mip-relative-tolerance 0.01
cuopt_cli --help
# Time limit (seconds)
cuopt_cli problem.mps --time-limit 120
# MIP gap tolerance (stop when within X% of optimal)
cuopt_cli problem.mps --mip-relative-tolerance 0.001
# MIP absolute tolerance
cuopt_cli problem.mps --mip-absolute-tolerance 0.0001
# Presolve, iteration limit, method
cuopt_cli problem.mps --presolve --iteration-limit 10000 --method 1
Integer variables: use 'MARKER' 'INTORG' before and 'MARKER' 'INTEND' after the integer columns.
Quadratic objectives extend the standard MPS workflow — same cuopt_cli command, same options. Check cuopt_cli --help for QP-specific flags and the repo docs at docs/cuopt/source/cuopt-cli/ for the quadratic-objective MPS format.
QP rules:
CLI is included with the Python package (cuopt). Install via pip or conda; then run cuopt_cli --help to verify.