Abstract
The purpose of this special issue of Algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues and standards in algorithmic experimentation were encouraged. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing were also welcome. In this regard, we solicited contributions from all most prominent areas of applied algorithmic research, which include but are not limited to graphs, databases, computational geometry, big data, networking, combinatorial aspects of scientific computing, and computational problems in the natural sciences or engineering.
Highlights
Algorithm engineering is an emerging discipline whose main aim is bridging the gap between classical algorithm design and complexity theory and practical algorithmics and applications
Driven by concrete applications, the algorithm engineering methodology complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during the process of designing of an algorithmic solution, ranging from realistic modeling, design, analysis, and robust and efficient implementations to careful experiments
The main focus is on understanding the behavior of natural strategies for solving the problem that are known as priority schedulings: several results are provided for this category of algorithm, ranging from stabilization properties to approximation guarantees
Summary
Algorithm engineering is an emerging discipline whose main aim is bridging the gap between classical algorithm design and complexity theory and practical algorithmics and applications (we refer the interested reader to [1] for a comprehensive survey).Studies in this field have grown when extreme advancements in the available computing hardware have rendered traditional computational models more and more unrealistic and have led to a constantly increasing demand for solutions to real-world problems that are efficient in theory and practical. Paper [2] studies issues affecting the implementation, testing, and experimentation of algorithms for computational geometry problems.
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