From cuopt-numerical-optimization-api-cli
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cuopt-numerical-optimization-api-cli:cuopt-numerical-optimization-api-cliThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
npx claudepluginhub coin-or/cuopt --plugin cuopt-numerical-optimization-api-cliLP, 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.
Provides guidelines for using NVIDIA cuOpt SDK: routing, LP/MILP/QP, installation, and server deployment. Activates when users need help calling cuOpt APIs or setting up the solver.