Inexact Accelerated High-order Proximal-point Methods in Convex Programming
Society for Industrial and Applied Mathematics via YouTube
Become an AI & ML Engineer with Cal Poly EPaCE — IBM-Certified Training
Finance Certifications Goldman Sachs & Amazon Teams Trust
Overview
Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Explore a groundbreaking framework for accelerated methods in Convex Programming through this 55-minute talk from the Society for Industrial and Applied Mathematics. Delve into the Bi-Level Unconstrained Minimization (BLUM) approach, which utilizes approximations of high-order proximal points to surpass traditional limits in Complexity Theory. Discover a novel second-order method achieving a convergence rate of O(k^(-4)), outperforming existing methods for functions with Lipschitz continuous Hessian. Examine new techniques with exact auxiliary search procedures, boasting impressive convergence rates of O(k^(-(3p+1)/2)) for proximal operators of order p≥1. Gain insights from Yurii Nesterov of Université Catholique de Louvain, Belgium, as he presents these innovative concepts in convex optimization.
Syllabus
Inexact Accelerated High-order Proximal-point Methods
Taught by
Society for Industrial and Applied Mathematics