Solving and benchmarking QUBO problems with Gurobi in Python

State-of-the-art classical optimizer Gurobi for Quadratic Unconstrained Binary Optimization (QUBO) problems. The core gurobipy implementation for QUBO is relatively compact: ```python model = gp.Model() x = model.addMVar(n, vtype=GRB.BINARY) model.setObjective(x @ Q @ x, GRB.MINIMIZE) model.optimize() solution = x.X.astype(int) objective = model.ObjVal ``` Complete workflow in Python. First formulate a graph problem (weighted Max-Cut) as QUBO, solve it with Gurobi, benchmark increasingly large instances, and understand what the solver is doing beyond the optimize() call. Interested in feedback on the modeling, benchmarking methodology, and which additional Gurobi metrics would make the comparison more rigorous. submitted by /u/Future_Ad7567 [link] [留言]