The ACM Journal of Experimental Algorithmics

Please Note: The articles in this volume now appear in the ACM Digital Library. You can find the table of contents, together with browsing and indexing services, at the ACM Digital Library, Journal of Experimental Algorithmics, Volume 7.

Special Issue Articles

Article 1:
Heuristics on Lattice Basis Reduction in Practice, by Werner Backes, Max Planck Institut für Informatik, and Susanne Wetzel, Stevens Institute of Technology

Article 2:
Parallelizing Local Search for CNF Satisfiability Using Vectorization and PVM, by Kazuo Iwama, Daisuke Kawai, Shuichi Miyazaki, Yasuo Okabe, and Jun Umemoto, Kyoto University

Article 3:
An Experimental Study of Online Scheduling Algorithms, by Susanne Albers, Universität Freiburg, and Bianca Schröder, Carnegie-Mellon University

Article 4:
Implementation of O(n m log n) Weighted Matchings in General Graphs: The Power of Data Structures, by Kurt Mehlhorn and Guido Schäfer, Max Planck Institut für Informatik

Article 5:
Implementing HEAPSORT with (n logn - 0.9n) and QUICKSORT with (n logn + 0.2n) Comparisons, by Stefan Edelkamp and Patrick Stiegeler, Universität Freiburg

Article 6:
Implementation of Approximation Algorithms for Weighted and Unweighted Edge-Disjoint Paths in Bidirected Trees, by Thomas Erlebach, Eidgenössische Technische Hochschule Zürich, and Klaus Jansen, Universität Kiel

Article 7:
Portable List Ranking: An Experimental Study, by Isabelle Guérin-Lassous, INRIA Rocquencourt, and Jens Gustedt, INRIA Lorraine

Article 8:
Planar Point Location For Large Data Sets: To Seek Or Not To Seek, by Jan Vahrenhold and Klaus H. Hinrichs, Universität Münster

Article 9:
Efficient Sorting Using Registers and Caches, by Rajiv Wickremesinghe, Lars Arge, Jeffrey Chase, and Jeffrey S. Vitter, Duke University

Regular Articles:

Article 10:
Finding the Chromatic Number by Means of Critical Graphs, by Francine Herrmann, Université de Metz, and Alain Hertz, Ecole Polytechnique de Montréal

Article 11:
Solving a "Hard" Problem to Approximate an "Easy" One: Heuristics for Maximum Matchings and Maximum Traveling Salesman Problems, by Sándor Fekete, TU Braunschweig, Henk Meijer, Queen's University, André Rohe, Universität Bonn, and Walter Tietze, TU Berlin

Article 12:
Relational Concept Learning by Cooperative Evolution, by Filippo Neri, Universitá del Piemonte Orientale "A. Avogadro"

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