Constrained Optimization

Constrained optimization is closer to most real-world problems because we rarely seek just the best solution; we seek the best solution that also respects certain requirements. Budget limits, energy constraints, capacity bounds, fairness conditions, or physical rules are common examples. In machine learning, constrained optimization becomes important in contexts involving fairness, resource limitations, and safety requirements. It brings optimization out of idealized mathematics and into operational reality.