From Minimum Cut to Submodular Minimization: Leveraging the Decomposable Structure
||Friday, October 28, 2016
||10:30am - 11:30am
||D344 LSRC, Duke
Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work aims to obtain very practical algorithms for minimizing functions that are sums of "simple" functions. This class of functions provides an important bridge between functions with a rich combinatorial structure -- notably, the cut function of agraph -- and general submodular functions, and it brings along powerful combinatorial structure reminiscent of graphs as well as a fair bit of modeling power that greatly expands its applications. In this talk, we describe recent progress on designing very efficient algorithms for minimizing decomposable functions and understanding their combinatorial structure.
This talk is based on joint work with Huy Nguyen (Northeastern University) and Laszlo Vegh (London School of Economics).
Alina Ene is an assistant professor in the Computer Science department at Boston University. Her research interests include the design and analysis of algorithms, the mathematical aspects of combinatorial optimization topics such as submodularity and graphs, and their applications to machine learning. Prior to joining BU, she was an Assistant Professor at the University of Warwick, a Faculty Fellow at the Alan Turing Institute for Data Science, and a postdoc in the Center for Computational Intractability at Princeton University. Alina obtained her PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2013 under the supervision of Chandra Chekuri.