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Abstract: This paper presents heuristic approximation algorithms and methods to find lower bounds to approximate the Minimum Linear Arrangement problem and evaluates and compares experimentaly their behaviour when they are applied to sparse graphs. The algorithms presented and analyzed belong to the families of Successive Augmentation algorithms (also called greedy algorithms), local search algorithms (Hillclimbing, Metropolis and Simulated Annealing) and Spectral Sequencing. The empirical results are... (Update)
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BibTeX entry: (Update)
J. Petit i Silvestre. Approximation Heuristics and Benchmarkings for the MinLA Problem. In R. Battiti and A. Bertossi, editors, Alex '98 --- Building bridges between theory and applications, http://www.lsi.upc.es/žjpetit/MinLA/Benchmark, February 1998. Universit `a di Trento. http://citeseer.ist.psu.edu/article/petit98approximation.html More
@misc{ petit98approximation,
author = "J. Petit and i Silvestre",
title = "Approximation Heuristics and Benchmarkings for the MinLA Problem",
text = "J. Petit i Silvestre. Approximation Heuristics and Benchmarkings for the
MinLA Problem. In R. Battiti and A. Bertossi, editors, Alex '98 --- Building
bridges between theory and applications, http://www.lsi.upc.es/žjpetit/MinLA/Benchmark,
February 1998. Universit `a di Trento.",
year = "1998",
url = "citeseer.ist.psu.edu/article/petit98approximation.html" }
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