Amortized Optimization

Amortized optimization leverages machine learning to accelerate the repeated solution of similar optimization problems by learning a mapping from problem parameters to solutions. Current research focuses on applying this technique across diverse fields, employing neural networks (including specialized architectures like parameterized convex minorants) and imitation learning to improve prediction accuracy and efficiency. This approach significantly reduces computational costs compared to traditional methods, impacting areas such as trajectory optimization for robotics, generative modeling, and active learning, where rapid, repeated optimization is crucial.