cuopt-numerical-optimization-api-cli

cuOpt Numerical Optimization — CLI

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).

Basic usage

# 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

Common options

cuopt_cli --help
 
# Time limit (seconds)
cuopt_cli

MPS format (required sections, in order)

1. NAME — problem name
2. ROWS — N (objective), L/G/E (constraints)
3. COLUMNS — variable names, row names, coefficients
4. RHS — right-hand side values
5. BOUNDS (optional) — LO, UP, FX, BV, LI, UI
6. ENDATA

Integer variables: use 'MARKER' 'INTORG' before and 'MARKER' 'INTEND' after the integer columns.

QP via CLI (beta)

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:

• MINIMIZE only. For maximization, negate the objective coefficients (and Q entries) in the MPS file.
• Continuous variables only — do not mix integer markers with quadratic objectives.

Troubleshooting

• Failed to parse MPS — Check ENDATA, section order (NAME, ROWS, COLUMNS, RHS, [BOUNDS], ENDATA), integer markers.
• Infeasible — Check constraint directions (L/G/E) and RHS values.

Examples

• assets/README.md [blocked] — Build/run for sample MPS files
• lp_simple [blocked] — Minimal LP (PROD_X, PROD_Y, two constraints)
• lp_production [blocked] — Production planning: chairs + tables, wood/labor
• milp_facility [blocked] — Facility location with binary open/close

Getting the CLI

CLI is included with the Python package (cuopt). Install via pip or conda; then run cuopt_cli --help to verify.