Title:
Subexponential lower bounds for randomized pivoting rules for the simplex algorithm
Subexponential lower bounds for randomized pivoting rules for the simplex algorithm
No Thumbnail Available
Author(s)
Hansen, Thomas Dueholm
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
The simplex algorithm is among the most widely used algorithms for solving linear programs in practice. Most deterministic pivoting rules are known, however, to need an exponential number of steps to solve some linear programs (Klee-Minty examples). No non-polynomial lower bounds on the expected number of steps were known, prior to this work, for randomized pivoting rules. We provide the first subexponential (i.e., of the form 2^(Omega(n^alpha)), for some alpha>0) lower bounds for the two most natural, and most studied, randomized pivoting rules suggested to date. Joint work with Oliver Friedmann and Uri Zwick.
Sponsor
Date Issued
2011-11-11
Extent
62:08 minutes
Resource Type
Moving Image
Resource Subtype
Lecture