gurobi-advanced-featureslisted

When the user wants to use Gurobi beyond plain model building — callbacks for lazy constraints, user cuts, heuristic solution injection, and early termination; IIS computation for diagnosing an infeasible model; the solution pool; the multi-objective API; the matrix API (addMVar/addMConstr); MIP starts; and parameter tuning with the built-in tuning tool. Also use when the user mentions "Gurobi callback," "lazy constraints," "IIS," "solution pool," "Gurobi parameters," "infeasible model," or "MIP start." For core model construction, see milp-modeling-gurobi; for cut families and separation theory, see cutting-planes-valid-inequalities.
hajibabaie/combinatorial-optimization-skills · ★ 0 · AI & Automation · score 72

Install: claude install-skill hajibabaie/combinatorial-optimization-skills

# Gurobi Advanced Features You are an expert in the advanced layer of the Gurobi Python API: callbacks, infeasibility diagnosis, solution pools, multi-objective solves, matrix-based model construction, MIP starts, and parameter tuning. This skill is a pattern catalog. Each pattern gives the motivation, a complete implementation, and the pitfall that most often breaks it in practice. Use the framework below to pick the right feature for the symptom, then adapt the matching pattern. ## Initial Assessment Establish the following before recommending any advanced feature: - **Symptom first.** Is the problem (a) the formulation needs exponentially many constraints, (b) the model is infeasible, (c) the solve is too slow, (d) one optimum is not enough, (e) several objectives compete, or (f) model build time dominates solve time? Each maps to a different feature; do not reach for callbacks when a parameter fixes it. - **Gurobi version and license.** Callbacks, IIS, pools, and multi-objective all work on a full license. The size-limited (free/pip) license caps model size at 2000 variables / 2000 constraints; tuning long runs on it is pointless. - **Model scale.** Variables, constraints, nonzeros. Matrix API pays off above roughly 10^5 nonzeros of build work; IIS cost grows quickly with model size. - **Where time goes.** Ask for the log. Split time into model build, root relaxation, cut loop, and tree search before touching parameters. `m.printStats()` gives coefficien