Giuseppe F. Italiano (University of Rome "Tor Vergata", Italy)
Experimental Study of Resilient Algorithms and Data Structures
Joint work with Umberto Ferraro-Petrillo and Irene Finocchi
Large and inexpensive memory devices may suffer from faults, where some bits may arbitrarily flip and corrupt the values of the affected memory cells. The appearance of such faults may seriously compromise the correctness and performance of computations. In recent years, many algorithms for computing in the presence of memory faults have been introduced in the literature: in particular, an algorithm or a data structure is called resilient if it is able to work correctly on the set of uncorrupted values. In this paper we contribute carefully engineered implementations of recent resilient algorithms and data structures and perform an extensive experimental study using a combination of several fault parameters and different fault injection strategies.
Panos M. Pardalos (University of Florida, USA)
Computational Challenges with Cliques, Quasi-Cliques and Clique Partitions in Graphs
During the last decade, many problems in social, biological, and financial networks require finding cliques, or quasi-cliques. Cliques or clique partitions have also been used as clustering or classification data mining tools in data sets represented by networks. These networks can be very large and often massive and therefore external (or semi-external) memory algorithms are needed. We discuss applications, algorithms and computational approaches for all types of problems.