Lecture

Gradient Descent: Proximal Operator and Step-Size Strategies

Description

This lecture covers the concepts of proximal operator, gradient descent, and step-size strategies in the context of minimizing risk functions. It explains the derivation of the gradient-descent algorithm, the use of constant step-sizes, and the transition to iteration-dependent step-sizes. The lecture also discusses the convergence analysis under different step-size conditions, such as constant and vanishing step-sizes, and their impact on the convergence rate of the algorithm.

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.