cuOpt LP/MILP Skill

Model and solve linear and mixed-integer linear programs using NVIDIA cuOpt's GPU-accelerated solver.

Before You Start

Use a formulation summary (parameters, constraints, decisions, objective) if available; otherwise ask for decision variables, objective, and constraints. Then confirm variable types (see below) and interface (Python API recommended).

Choosing LP vs MILP

Prefer LP (all continuous variables) when the problem allows it. LP solves faster and has stronger optimality guarantees. Use MILP only when the problem logically requires whole numbers or yes/no decisions.

Problem types that need extra care: Multi-period planning and goal programming are easy to misinterpret. Double-check that rates and constraints apply to the right time period or priority level (AGENTS.md: verify understanding before code).

• Use LP when every quantity can meaningfully be fractional: flows, proportions, rates, dollars, hours, tonnes of material, etc.
• Use MILP when the problem mentions counts of discrete entities, yes/no choices, or either/or decisions (e.g. open a facility or not, assign a person to a shift, number of trucks).

Integer vs continuous from wording