cuOpt LP/MILP — CLI

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 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

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.

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 — Build/run for sample MPS files
• lp_simple — Minimal LP (PROD_X, PROD_Y, two constraints)
• lp_production — Production planning: chairs + tables, wood/labor
• milp_facility — 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.