Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers
The work introduces a novel, efficient algorithm to determine optimal penalization weights for approximate solvers in constrained optimization problems solved via QUBO formulations. It combines analytical bounds and sampling to find a minimal penalty constant M that guarantees a desired success probability, offering provable performance improvements over existing heuristics.