YoVDO

Stochastic Primal Dual Splitting Algorithms for Convex and Nonconvex Composite Optimization in Imaging

Offered By: Society for Industrial and Applied Mathematics via YouTube

Tags

Stochastic Optimization Courses Convex Optimization Courses Image Reconstruction Courses Nonconvex Optimization Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Attend a virtual seminar in the Fifth Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS series featuring speaker Xiaoqun Zhang from Shanghai Jiao Tong University. Explore stochastic primal dual splitting algorithms for convex and nonconvex composite optimization in imaging. Delve into two classes of algorithms: the first combines stochastic gradient with primal dual fixed point method (PDFP) for convex linearly composite problems, while the second focuses on Alternating Direction Method of Multipliers (ADMM) for nonconvex composite problems. Learn about the convergence and effectiveness of these algorithms through examples in graphic Lasso, graphics logistic regressions, and image reconstruction. Gain insights into the advantages of SVRG-PDFP for large-scale image reconstruction problems, especially with limited high-performance computing resources. Understand the global convergence and convergence rate of ADMM combined with variance reduction gradient estimators under the Kurdyka-Lojasiewicz (KL) function assumption.

Syllabus

Fifth Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk


Taught by

Society for Industrial and Applied Mathematics

Related Courses

Convex Optimization
Stanford University via edX
FA19: Deterministic Optimization
Georgia Institute of Technology via edX
Applied Optimization For Wireless, Machine Learning, Big Data
Indian Institute of Technology Kanpur via Swayam
Statistical Machine Learning
Eberhard Karls University of Tübingen via YouTube
Convex Optimization
NIOS via YouTube