Title
Log-Concave Polynomials, Entropy, and a Deterministic Approximation Algorithm for Counting Bases of Matroids
Abstract
We give a deterministic polynomial time 2^O(r)-approximation algorithm for the number of bases of a given matroid of rank r and the number of common bases of any two matroids of rank r. To the best of our knowledge, this is the first nontrivial deterministic approximation algorithm that works for arbitrary matroids. Based on a lower bound of Azar, Broder, and Frieze this is almost the best possible assuming oracle access to independent sets of the matroid. There are two main ingredients in our result: For the first, we build upon recent results of Adiprasito, Huh, and Katz and Huh and Wang on combinatorial hodge theory to derive a connection between matroids and log-concave polynomials. We expect that several new applications in approximation algorithms will be derived from this connection in future. Formally, we prove that the multivariate generating polynomial of the bases of any matroid is log-concave as a function over the positive orthant. For the second ingredient, we develop a general framework for approximate counting in discrete problems, based on convex optimization. The connection goes through subadditivity of the entropy. For matroids, we prove that an approximate superadditivity of the entropy holds by relying on the log-concavity of the corresponding polynomials.
Year
DOI
Venue
2018
10.1109/FOCS.2018.00013
2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)
Keywords
DocType
Volume
matroid,deterministic counting,entropy,log concave polynomial
Conference
abs/1807.00929
ISSN
ISBN
Citations 
1523-8288
978-1-5386-4231-3
3
PageRank 
References 
Authors
0.42
15
3
Name
Order
Citations
PageRank
Nima Anari17914.83
Shayan Oveis Gharan232326.63
Cynthia Vinzant37911.85