Hard Problems Some problems are hard to solve. No polynomial time algorithm is known. E.g., NP-hard problems such as machine scheduling, bin packing,
SIGCOMM 2003 Making Intra-Domain Routing Robust to Changing and Uncertain Traffic Demands: Understanding Fundamental Tradeoffs David Applegate Edith Cohen.
Future directions in computer science research John Hopcroft Department of Computer Science Cornell University CINVESTAV-IPN Dec 2,2013.
Why almost all k-colorable graphs are easy A. Coja-Oghlan, M. Krivelevich, D. Vilenchik.
Genome Rearrangements CIS 667 April 13, 2004. Genome Rearrangements We have seen how differences in genes at the sequence level can be used to infer evolutionary.
Virtual Computing Environment for Future Combat Systems.
The Use of Semidefinite Programming in Approximation Algorithms Uriel Feige The Weizmann Institute.
Rectangle Visibility Graphs: Characterization, Construction, Compaction Ileana Streinu (Smith) Sue Whitesides (McGill U.)
Dynamic Spectrum Management: Optimization, game and equilibrium Tom Luo (Yinyu Ye) December 18, WINE 2008.
Truthful Randomized Mechanisms for Combinatorial Auctions Speaker: Shahar Dobzinski Joint work with Noam Nisan and Michael Schapira.
NP-completeness Algorithms and Networks. Algorithms and Networks: NP-completeness2 Today Complexity of computational problems Formal notion of computations.
Pseudorandom Bit Generation Artur Gadomski Piero Giammarino Henrik Goldman Massimo Giulio Caterino.
