Gradient Descent: Proximal Operator and Step-Size Strategies

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 2 of 3

Newton Method: Convergence and Quadratic Care

Covers the Newton method and its convergence properties near the optimal point.

Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Uniform Integrability and Convergence

Explores uniform integrability, convergence theorems, and the importance of bounded sequences in understanding the convergence of random variables.

Descent Methods and Newton Method with Line Search

Covers the comparison between descent methods and Newton's method, modifications, line search, and convergence analysis.

Double Descent Curves: Overparametrization

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Coordinate Descent: Optimization Strategies

Explores coordinate descent optimization strategies, emphasizing simplicity in optimization through one-coordinate updates and discussing the implications of different approaches.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Gradient Descent

Covers the algorithm of gradient descent, aiming to minimize a function by iteratively moving in the direction of the steepest decrease.

Optimization Techniques: Gradient Method Overview

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.