Agent skill · NVIDIA
cuopt-numerical-optimization-formulation
LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.
What it needs
About 12k tokens when loaded.
What this skill does
Numerical Optimization Formulation Concepts and workflow for going from a problem description to a clear formulation across LP, MILP, and QP. No API code here. What is LP / MILP / QP LP: Linear objective, linear constraints, continuous variables. MILP: Same as LP plus some integer or binary variables (e.g., scheduling, facility location, selection). QP: Quadratic objective (e.g., x², x·y terms — portfolio variance, least squares), linear constraints. QP support in cuOpt is currently in beta. Identifying problem type Property LP MILP QP --- --- --- --- Objective Linear Linear Quadratic (xᵀQx + cᵀx) Constraints Linear Linear Linear + convex quadratic (inequality only) via second-order cones Variables Continuous Mixed: continuous + integer/binary Continuous Sense min or max min or max minimize only (negate to max) Duals / sensitivity Dual values + reduced costs None (integer optima) Dual values + reduced costs If the objective is purely linear, prefer LP/MILP — do not artificially introduce quadratic terms. If any variable is integer or binary, the problem is MILP regardless of the rest. Post-solve sensitivity (LP / QP only). Continuous LP and QP solutions expose dual values (the marginal objective change per unit a binding constraint is relaxed: where to invest to improve the outcome) and reduced costs (for a variable the optimizer left at zero, how far it must improve to enter the solution: a near-miss). MILP solutions have no duals — integer optima are not continuous, so there are none to return. Duals are also unavailable when the model includes quadratic constraints — the second-order cone path returns primal values only. See the language-specific API skills for how to retrieve them after a solve. Required formulation questions Ask these if not already clear: 1. Decision variables — What are they? Bounds? 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/cuopt-numerical-optimization-formulation