Abstract:

Optimization algorithms play a crucial role in the process of handling enormous datasets in the age of big data. Traditional optimization methods often struggle with modern applications, sometimes taking days or even weeks to find a solution. My research program thus consists in developing and providing fast and resource-efficient, large-scale optimization methods to meet the demands of today's challenges. In this talk, I will highlight the increasing need for such methods and present two block decomposable approaches to address this demand. Both of these methods not only speed up solution time, sometimes reducing it from days to mere minutes or seconds, but also make it feasible to tackle problems that might previously be considered computationally intractable. Specifically, I will discuss the Alternating Directions of Method of Multipliers (ADMM) and the Randomized Block Coordinate Descent Method, both of which offer efficient and scalable solutions to large-scale optimization challenges.

Bio:

Dr. Leandro F. Maia is an Assistant Professor in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. His research focuses on the development and analysis of algorithms for large-scale optimization problems, with applications in engineering, machine learning, and data science. He earned his Ph.D. in Industrial and Systems Engineering from Texas A&M University under the supervision of Dr. David H. Gutman. Over the past two years, he has collaborated with Professor Renato Monteiro from Georgia Tech and researchers at Sandia National Laboratories. Before his doctoral studies, he obtained a Master’s degree in Mathematics from the Federal University of Pará (UFPA) and a Bachelor’s degree in Computer Engineering from the Military Institute of Engineering (IME) in Brazil.