Visit Reykjavik ICALP 2008
35th International Colloquium on
Automata, Languages and Programming


July 6 - 13, 2008
Reykjavik - Iceland


TITLES AND ABSTRACTS OF THE INVITED TALKS

Ran Canetti (IBM T.J. Watson Research Center and MIT, USA), Composable Formal Security Analysis: Juggling Soundness, Simplicity and Efficiency


Bruno Courcelle (Labri, Universitè Bordeaux, France), Graph Structure and Monadic Second-order Logic: Language Theoretical Aspects


Javier Esparza (Technische Universität München, Germany), Newtonian Program Analysis

Abstract: In this talk I'll look at a sequential program as a system of equations of the form

X1 = f1(X1, ..., Xn)
...
Xn = fn(X1, ..., Xn)

where the fi's are polynomials, and sum and product correspond to choice and sequential composition. (If you're familiar with process algebras, this should ring a bell.) I'll argue that static program analysis is the art of solving these equations over different interpretations, depending on the information one is interested in.

40 years of research on static analysis have not produced much theory on generic methods for solving these equations, i.e., on methods that work for any interpretation. The ones around are based on Knaster-Tarski's and Kleene's theorems. Unfortunately, these methods rarely terminate for infinite domains, and in metric interpretations their convergence is often hopelessly slow. Can we do better?

I'll show that Newton's method, well-known from numerical mathematics, can be generalized to (almost) arbitrary interpretations, and that this generalization provides a unified view of many results of language theory, as well as a bridge between qualitative and quantitative program analysis.

Joint work with Stefan Kiefer and Michael Luttenberger.


Muthu Muthukrishnan (Google, USA), Internet Ad Auctions: Insights and Directions


Peter Winkler (Dartmouth, USA), Optimality and Greed in Dynamic Allocation

Abstract: On-line problems arise often in industry, but even in simple cases it seems impossible to prove that a given algorithm is optimal without assuming some precise input distribution. Rather than settle for a competitive ratio result, we offer a method for proving optimality in dynamic allocation problems which relies on the assumption that "it's right to be greedy." The method is applied to two problems which arose at Lucent Technologies.