Weighted Sum Scalarization

Weighted sum scalarization is a technique used to simplify multi-objective optimization problems by combining multiple objective functions into a single weighted sum. Current research focuses on improving the efficiency and effectiveness of scalarization methods, particularly exploring variations like Tchebycheff scalarization and its smooth counterparts, to better approximate the Pareto frontier and handle high-dimensional objective spaces. This research is significant because it addresses the computational challenges of multi-objective optimization in diverse fields like reinforcement learning, recommender systems, and multi-task learning, leading to more efficient and effective algorithms for these applications.