Lecture

Optimization for Machine Learning: Faster Gradient Descent

Description

This lecture covers the concept of faster gradient descent in optimization for machine learning, focusing on projected gradient descent. It discusses the possibility of exponential error decrease, strongly convex functions, and the application of projected gradient descent in constrained optimization problems. The lecture also explores the properties of projection, Lipschitz convex functions, and the efficiency of gradient descent over closed and convex sets.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace
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.
Ontological neighbourhood
Related lectures (73)
Optimization for Machine Learning: Faster Gradient Descent
Explores faster gradient descent in optimization for machine learning, focusing on projected gradient descent and the properties of convex functions.
Optimization for Machine Learning: Frank-Wolfe Algorithm
Covers the Frank-Wolfe algorithm, a sparse and feasible optimization method with examples and convergence analysis.
Convex Optimization
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Convex Optimization: Gradient Descent
Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Convex Functions
Covers the properties and operations of convex functions.
Show more