Optimization is a ubiquitous modeling tool and is often deployed in settings which repeatedly solve similar instances of the same problem. Amortized optimization methods use learning to predict the solutions to problems in these settings, exploiting the shared structure between similar problem instances. These methods have been crucial in variational inference and reinforcement learning and are capable of solving optimization problems many orders of magnitudes times faster than traditional optimization methods that do not use amortization. This tutorial presents an introduction to the amortized optimization foundations behind these advancements and overviews their applications in variational inference, sparse coding, gradient-based meta-learning, control, reinforcement learning, convex optimization, optimal transport, and deep equilibrium networks. The source code for this tutorial is available at https://github.com/facebookresearch/amortized-optimization-tutorial.

翻译：优化是一种无处不在的建模工具，常被部署在需要反复求解同一问题类似实例的场景中。摊销优化方法利用学习机制预测这些问题实例的解，通过挖掘相似问题实例之间的共享结构来实现。这些方法在变分推理和强化学习中至关重要，且能够比不使用摊销技术的传统优化方法快多个数量级地求解优化问题。本教程介绍了支撑这些进展的摊销优化基础理论，并概述了其在变分推理、稀疏编码、基于梯度的元学习、控制、强化学习、凸优化、最优传输和深度均衡网络中的应用。本教程的源代码可从https://github.com/facebookresearch/amortized-optimization-tutorial获取。