YoVDO

A Stochastic Newton Algorithm for Distributed Convex Optimization

Offered By: Simons Institute via YouTube

Tags

Convex Optimization Courses Algorithm Design Courses Distributed Computing Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 35-minute lecture on a novel stochastic Newton algorithm for distributed convex optimization presented by Brian Bullins from Purdue University at the Simons Institute. Delve into the proposed method for homogeneous distributed stochastic convex optimization, where machines calculate stochastic gradients and Hessian-vector products of the same population objective. Learn how this algorithm reduces communication rounds without compromising performance, particularly for quasi-self-concordant objectives like logistic regression. Examine the convergence guarantees and empirical evidence supporting the effectiveness of this approach in optimization and algorithm design.

Syllabus

A Stochastic Newton Algorithm for Distributed Convex Optimization


Taught by

Simons Institute

Related Courses

Natural Language Processing
Columbia University via Coursera
Intro to Algorithms
Udacity
Conception et mise en œuvre d'algorithmes.
École Polytechnique via Coursera
Paradigms of Computer Programming
Université catholique de Louvain via edX
Data Structures and Algorithm Design Part I | 数据结构与算法设计(上)
Tsinghua University via edX