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Program Committee




APPROX

Niv Buchbinder
Deeparnab Chakrabarty
Siu On Chan
Shuchi Chawla
Eden Chlamtac
Nikhil Devanur (chair)
Alina Ene
Konstantinos Georgiu
Telikepalli Kavitha
Ken Ichi Kawarabayashi
Jochen Koenemann
Amit Kumar
Konstantin Makarychev
Debmalya Panigrahi
Thomas Rothvoss
Barna Saha
Bruce Shepherd
Aravind Srinvasan
David Williamson

RANDOM

Louigi Addario-Berry
Nayantara Bhatnagar
Amin Coja-Oghlan
David Galvin
Valentine Kabanets
Michael Molloy
Cris Moore (chair)
Assaf Naor
Krzysztof Onak
Dana Ron
Alex Russell
Dominik Scheder
Devavrat Shah
Perla Sousi
Mario Szegedy
Amnon Ta-Shma
Thomas Vidick

Program Chairs


APPROX
Nikhil Devanur,
Microsoft Research, Redmond
email:
nikdev@microsoft.com

RANDOM
Cris Moore,
Santa Fe Institute
email:
moore@santafe.edu

Workshop Chairs


José Rolim,
U. of Geneva
e-mail: jose.rolim@unige.ch
Klaus Jansen,
U. of Kiel
e-mail: kj@informatik.uni-kiel.de

Organizing Comitee Chair


Carme Alvarez,
Universitat Politècnica de Catalunya
email:
alvarez@lsi.upc.edu
Important dates
Submission deadline
April 17, 2014
5PM PDT

Notification to authors
June 6, 2014

Camera ready
June 20, 2014
Call for papers
PDF Format

Plain Text

Uri Feige, Weizmann Institute

Preprocessing of NP-hard problems


Abstract: Consider a setting in which an input instance for an NP-hard optimization problem is supplied in two steps. In the first step, one gets to see some partial information about the input instance, referred to as a "preview". Based on this preview, a preprocessing algorithm can spend arbitrary (e.g., exponential) time in preparing some polynomial size "advice" string. In the second step, one gets to see the full input instance. Thereafter, a polynomial time algorithm attempts to solve the instance, and may use for this purpose the advice string prepared by the preprocessing algorithm. For various NP-hard optimization problems we present natural preview functions whose study appears to be well motivated. In certain cases we can prove that preprocessing leads to improved approximation ratios, and in certain cases we can prove limitations on how much preprocessing can help. Many natural questions within this framework remain open, and can serve as fertile ground for future research.

Colin McDiarmid, University of Oxford

Random graphs from a minor-closed class


Abstract: Consider a minor-closed class of graphs, such as the class of planar graphs. What can we say in general about a typical n-vertex graph in the class, for large n? For example, how likely is it to be connected? To answer such questions we need to be able to estimate the number a_n of such graphs. As a first step we want to know that there is a growth constant, that is (a_n/n!)^{1/n} tends to a limit. We can say more if the class is smooth, that is a_n/na_{n-1} tends to a limit. It is known that if a minor-closed class is addable (has 2-connected excluded minors) then it is smooth: we shall see for example that the leaf-free graphs in such a class also form a smooth class.