Randomization Techniques for Large-Scale Optimization

The interest in large-scale optimization methods has recently grown remarkably due to
their application in diverse practical problems including machine learning, statistics and
data science. Recent works have shown that the introduction of randomization techniques
such as random shuffling in optimization algorithms would result in more efficient and
accurate results. Yet, the mathematical justification of this is still lacking which servers as
an obstruction on understanding the randomization techniques and developing new and
efficient numerical methods.
This project aims to introduce randomization techniques into the operator splitting
algorithms for the more general nonconvex problems (such as the proximal gradient
methods and the Douglas-Rachford algorithm). In particular, it will focus on how to use
randomization techniques to improve current splitting algorithms for nonconvex
optimization problems.

Xiaoyu Li

The University of New South Wales

Xiaoyu Li is a fourth-year undergraduate student at the University of New South Wales majoring in mathematics and computer science. His research focus lies between applied mathematics and computer science, with interests in mathematical optimization and theoretical machine learning. He is working towards attaining a doctorate in mathematics and hopes to be qualified enough to become a mathematician someday, which would be a great honor.

You may be interested in

David Adams

David Adams

Can Machines Learn like Fish? An Application of Simulated Multi-Timescale Nexting
Jamie Bell

Jamie Bell

Curvature and Topology
Matthew Hanna

Matthew Hanna

Optimisation and Theoretical Implications of Probability Inequalities
Michael Nefiodovas

Michael Nefiodovas

Optimal Control in Stochastic Hydrodynamic Models: Rowing Across the Indian Ocean
Contact Us

We're not around right now. But you can send us an email and we'll get back to you, asap.

Not readable? Change text.