Designing and analyzing algorithms for optimization problems is a crucial but challenging task that arises in various fields such as business, science, and engineering. Despite the development of various successful optimization algorithms over the past sixty years, many of these algorithms are problem-specific and ad hoc in nature.
In this talk, I will present a unified framework for designing optimization algorithms through the general problem of Convex Integer Optimization, which captures many central challenges in optimization. This framework has resulted in the algorithmic advances for various fundamental optimization problems, including faster algorithms for tractable problems and better approximation algorithms for NP-hard problems. Furthermore, it has revealed new connections between NP-hard and tractable problems which have been studied relatively independently for over half a century. Finally, I will conclude the talk with several future research directions and open problems in optimization and related areas.
Haotian Jiang is a Postdoctoral Researcher at Microsoft Research, Redmond. In December 2022, he obtained his PhD from the Paul G. Allen School of Computer Science & Engineering at University of Washington under the supervision of Yin Tat Lee. He is broadly interested in theoretical computer science and applied mathematics. His primary area of expertise is the design and analysis of algorithms for continuous and discrete optimization problems. His work on optimization has been recognized by a Best Student Paper Award in SODA 2021.
Faculty Host: Sariel Har-Peled
Meeting ID: 848 4399 5218; Password: csillinois