Lecture

Momentum methods and nonlinear CG

Description

This lecture covers gradient descent with memory in R2, introducing momentum methods to improve convergence speed. It then delves into conjugate gradients with momentum, exploring the use of different beta rules and their impact on performance. The instructor also discusses nonlinear CG on manifolds, specifically Riemannian CG, highlighting the rich family of algorithms it yields and the delicate theory behind it.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.