TCS+ talk: Wednesday, March 20th, Aleksandar Nikolov, University of Toronto
The next TCS+ talk will take place this coming Wednesday, March 20th at 1:00 PM Eastern Time (10:00 AM Pacific Time, 18:00 Central European Time, 17:00 UTC). Aleksandar Nikolov from University of Toronto will speak about “Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems” (abstract below).
Please make sure you reserve a spot for your group to join us live by signing up on the online form. As usual, for more information about the TCS+ online seminar series and the upcoming talks, or to suggest a possible topic or speaker, please see the website.
Abstract: Semidefinite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rounding method of Goemans and Williamson has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications. Despite the fact that this approach yields tight approximation guarantees for some problems, like Max Cut, for many others, like Max Sat, Max DiCut, and constraint satisfaction problems with global constraints, the tight approximation ratio is still unknown. One of the main reasons for this is the fact that very few techniques for rounding semi-definite relaxations are known.
In this work, we present a new general and simple method for rounding semi-definite programs, based on Brownian motion. Our approach is inspired by recent results in algorithmic discrepancy theory. We develop and present tools for analyzing our new rounding algorithms, utilizing mathematical machinery from the theory of Brownian motion, complex analysis, and partial differential equations. Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max Cut, Max 2-Sat, and Max DiCut, and derive new algorithms that are competitive with the best known results. We further show that our algorithms can be used, together with the Sum of Squares hierarchy, to approximate constraint satisfaction problems subject to multiple global cardinality constraints.
Join work with Sepehr Abbasi-Zadeh, Nikhil Bansal, Guru Guruganesh, Roy Schwartz, and Mohit Singh