Date Thesis Awarded


Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)




David Phillips

Committee Members

Michael Lewis

Rex K. Kincaid

Virginia Torczon


This thesis investigates various computational approaches to the Maximum Cut problem. It is generally believed that Maximum Cut cannot be solved exactly in polynomial time, so we approach the problem using various heuristics and approximation algorithms. We introduce a rank-penalization heuristic that generates feasible solutions to Maximum Cut. Numerical results show that these solutions are comparable to those given by the Goemans-Williamson randomized algorithm. We also implement a branch and bound algorithm using a branching scheme based on optimal dual variables for the Maximum Cut semidefinite programming relaxation. In our test cases, the dual branching scheme performed consistently better than randomized or largest-degree branching schemes.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.


Thesis is part of Honors ETD pilot project, 2008-2013. Migrated from Dspace in 2016.

On-Campus Access Only