Experimental algorithms

More about the book

This collection addresses various challenges and advancements in algorithm design and computational models. It explores parallelism in processors, examining the support needed for optimal algorithm development. The transition from theoretical concepts to practical applications is highlighted through the experience of a VP of Engineering. Several studies focus on optimization, including experimental comparisons of derivative-free algorithms and buffer management with stochastic packet arrivals. The text also delves into computational models for flash memory devices and presents fast, accurate bounds on linear programming. Innovative algorithms are discussed, such as batch dynamic single-source shortest-path algorithms and a heuristic for fair resource placement. The measurement of geometric graph similarity and strong connectivity algorithms for large graphs are also covered. Additional topics include Pareto paths, self-organizing data structures for compression, and scheduling trains in dense corridors. The efficiency of broadword computing and techniques for network congestion games are examined, alongside empirical evaluations of graph partitioning and algorithms for clique relaxations. The collection concludes with algorithms for assessing protein robustness, multi-level modularity clustering, and a novel bulk-insertion sort method, alongside a fast algorithm for graph isomorphism testing and elevation maxima computatio