- About
- Events
- Calendar
- Graduation Information
- Cornell Learning Machines Seminar
- Student Colloquium
- BOOM
- Fall 2024 Colloquium
- Conway-Walker Lecture Series
- Salton 2024 Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University - High School Programming Contests 2024
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- ACSU Research Night
- Cornell Junior Theorists' Workshop 2024
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
- Admissions
- Current Students
- Computer Science Graduate Office Hours
- Advising Guide for Research Students
- Business Card Policy
- Cornell Tech
- Curricular Practical Training
- A & B Exam Scheduling Guidelines
- Fellowship Opportunities
- Field of Computer Science Ph.D. Student Handbook
- Graduate TA Handbook
- Field A Exam Summary Form
- Graduate School Forms
- Instructor / TA Application
- Ph.D. Requirements
- Ph.D. Student Financial Support
- Special Committee Selection
- Travel Funding Opportunities
- Travel Reimbursement Guide
- The Outside Minor Requirement
- Diversity and Inclusion
- Graduation Information
- CS Graduate Minor
- Outreach Opportunities
- Parental Accommodation Policy
- Special Masters
- Student Spotlights
- Contact PhD Office
Hypergraph K-Cut in Randomized Polynomial Time
Abstract: In the hypergraph k-cut problem, the input is a hypergraph along with a constant k and the goal is to find a smallest subset of hyperedges whose removal ensures that the remaining hypergraph has at least k connected components. The graph k-cut problem is solvable in polynomial-time (Goldschmidt and Hochbaum, 1994) while the complexity of the hypergraph k-cut problem is open. In this talk, I will present a randomized polynomial-time algorithm to solve the hypergraph k-cut problem. Along the way, I will also present a random contraction algorithm to compute hypergraph min-cut, thus generalizing the well-known random contraction algorithm for graph min-cut due to Karger.
Based on joint work with Chao Xu and Xilin Yu.
Bio: Karthekeyan Chandrasekaran is an assistant professor in Industrial and Enterprise Systems Engineering and an affiliate assistant professor in Computer Science at UIUC. He received his Ph.D. in Algorithms, Combinatorics and Optimization from Georgia Tech. Prior to joining UIUC, he was a Simons Postdoctoral Fellow in the Theory of Computation group at Harvard University. His research interests are in combinatorial optimization, design and analysis of algorithms, and integer programming.