  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.
