Agent skill · NVIDIA
cuopt-multi-objective-exploration
Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
What it needs
About 10k tokens when loaded.
What this skill does
Multi-Objective Exploration cuOpt optimizes one objective per solve. Many real problems have several objectives that pull against each other — cost vs. service level, return vs. risk, makespan vs. overtime, distance vs. vehicle count. A single solve answers "what's optimal for one particular weighting," but it hides the tradeoff the user actually needs to see. This skill turns a sequence of single-objective cuOpt solves into a Pareto frontier — the set of solutions where you can't improve one objective without giving up another — and gives the discipline to read it. It adds no solver features; it orchestrates the LP / MILP / QP solves already covered by the formulation and API skills. When this applies Reach for this workflow when the problem has two or more objectives with no agreed-upon weighting, signalled by language like: "balance X and Y", "trade off", "as cheap as possible without hurting service" "minimize cost and maximize coverage", "I want options, not one answer" any objective the user is willing to relax in exchange for another If there is a single clear objective (everything else is a hard constraint), this skill does not apply — formulate and solve once. Core idea — one solve is one point on a curve A single optimum encodes one implicit weighting of the objectives. Change the weighting and the optimum moves. The frontier is the curve traced by all the non-dominated optima. A solution A dominates B when A is at least as good on every objective and strictly better on one. Dominated solutions are never worth choosing. The Pareto frontier is exactly the non-dominated set; the user's job is to pick a point on it, and yours is to show them the whole curve plus where the tradeoff is sharpest. Do not collapse a multi-objective problem to a single weighted number and report its optimum as "the answer" — that silently makes the tradeoff decision for the user. Trace the frontier and let them choose. Objectives and constraints are interchangeable. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/cuopt-multi-objective-exploration