Global Cardinality Constraints Make Approximating Some Max-2-CSPs Harder

Assuming the Unique Games Conjecture, we show that existing approximation algorithms for some Boolean Max-2-CSPs with cardinality constraints are optimal. In particular, we prove that Max-Cut with cardinality constraints is UG-hard to approximate within ~~0.858, and that Max-2-Sat with cardinality constraints is UG-hard to approximate within ~~0.929. In both cases, the previous best hardness results were the same as the hardness of the corresponding unconstrained Max-2-CSP (~~0.878 for Max-Cut, and ~~0.940 for Max-2-Sat). The hardness for Max-2-Sat applies to monotone Max-2-Sat instances, meaning that we also obtain tight inapproximability for the Max-k-Vertex-Cover problem

The BG News September 13, 1994

The BGSU campus student newspaper September 13, 1994. Volume 77 - Issue 14https://scholarworks.bgsu.edu/bg-news/6725/thumbnail.jp

The BG News January 28, 1992

The BGSU campus student newspaper January 28, 1992. Volume 74 - Issue 83https://scholarworks.bgsu.edu/bg-news/6319/thumbnail.jp

The BG News January 28, 1992

The BGSU campus student newspaper January 28, 1992. Volume 74 - Issue 83https://scholarworks.bgsu.edu/bg-news/6319/thumbnail.jp

The BG News February 20, 1987

The BGSU campus student newspaper February 20, 1987. Volume 69 - Issue 84https://scholarworks.bgsu.edu/bg-news/5623/thumbnail.jp

BGSU 1991-1992-1993 Undergraduate Catalog

Bowling Green State University undergraduate catalog for 1991-1992-1993.https://scholarworks.bgsu.edu/catalogs/1014/thumbnail.jp

Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges

Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context