External Constraint

External constraints in machine learning and optimization involve incorporating real-world limitations or requirements into model development and decision-making processes. Current research focuses on integrating these constraints, which can be either hard (must be satisfied) or soft (penalized if violated), into various algorithms, including model predictive control and constrained optimization methods like projected gradient descent. This field is crucial for developing robust and reliable AI systems applicable to diverse domains, from robotics and video processing to fairness-aware algorithms and socially beneficial AI design, by bridging the gap between theoretical models and practical applications.