Algorithms and Data Structures

Prof. Kurt Mehlhorn was appointed a Fellow of the ACM (1999) "for important contributions in complexity theory and in the design, analysis, and practice of combinatorial and geometric algorithms." A Professor of Computer Science at Saarland University since 1975, and a director of the Max-Planck-Institut fur Informatik in Saarbrucken, he has coauthored over 250 refereed papers/articles, in collaboration with 200 researchers. Other awards include the Leibniz Award of the German Research Foundation in 1986 and the Konrad Zuse Medal of the German Society for Informatics in 1995. Prof. Peter Sanders is a Professor of Computer Science at the University of Karlsruhe. A leading researcher in the area of theoretical and experimental algorithm analysis, in particular related to efficient algorithms for parallel processing and communication in networks, his responsibilities include organizing the European Symposium on Algorithms in Karlsruhe in 2008. The authors have considerable experience teaching on the topic of algorithms and working on related industrial projects.

"This is another mainstream textbook on algorithms and data structures, mainly intended for undergraduate students and professionals ... . The two-layer index table is also detailed and helpful. I do enjoy reading the informative sections of historical notes and further findings at the end of each chapter. ... This book is very well written, with the help of ... clear figures and tables, as well as many interesting and inspiring examples." Zhizhang Shen, Zentralblatt MATH, Vol. 1146, 2008 "... the book develops the basic fundamental principles underlying their design and analysis without sacrificing depth or rigor. The authors' insight, knowledge and active research on algorithms and data structures provide a very solid approach to the book. I particularly liked their "as informally as possible and as formally as necessary" writing style, and I enjoyed a lot their decision to not only discuss classical results, but to broaden the view to alternative implementations, memory hierarchies and libraries, which transmits novelty and increases interest... I think that this book will be a superb addition particularly useful for teachers of undergraduate courses, to graduate students in Computer Science, and to researchers that work, or intend to work, with algorithms." Jordi Petit, Computer Science Review 3, 2009 "Mehlhorn and Sanders write well, and the well-organized presentation reflects their experience and interest in the various topics... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. [...] This text is intended for undergraduate computer science (CS) majors, and focuses on algorithm analysis. ... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. Finally, the book contains interesting tidbits that are not readily available elsewhere." M. G. Murphy, ACM Computing Reviews, October 2008 "A 'Toolbox' should be portable, practical, and useful. This book is all these, covering a nice swath of the classic CS algorithms but addressing them in a way that is accessible to the student and practitioner. Furthermore, it manages to incorporate interesting examples as well as subtle examples of wit compressed into its 300 pages. Although it is not tied to any one language or library, it provides practical references to efficient open-source implementations of many of the algorithms and data structures; these should be the first refuge of the commercial developer. I can easily recommend this book as an intermediate undergraduate text, a refresher for those of us who only dimly remember our intermediate undergraduate courses, and as a reference for the professional development craftsman." Hal C. Elrod, SIGACT News Book Review Column 42(4) 2011

• Appetizer: Integer Arithmetics.- Representing Sequences by Arrays and Linked Lists.- Hash Tables and Associative Arrays.- Sorting and Selection.- Priority Queues.- Sorted Sequences.- Graph Representation.- Graph Traversal.- Shortest Paths.- Minimum Spanning Trees.- Generic Approaches to Optimization.