Abstract
This paper concentrates on low-cost heuristics for profile reduction. Low-cost methods for profile reduction are mainly heuristic in nature and based on graph-theoretic concepts. The contribution of this paper is twofold. Firstly, the paper includes a section involving a numerical examination of the current state-of-art metaheuristic and graph-theoretic methods for matrix profile reduction. With the support of extensive experiments, this paper shows that the metaheuristic-based algorithm is capable of reducing the profile of some matrices where the other algorithms do not perform well, but on average, the profile reduction obtained is similar for these algorithms, whereas the metaheuristic-based algorithm takes seven orders of magnitude more running time. These high execution times make the metaheuristic-based algorithm a noncontender for sparse matrix factorization and related problems. Secondly, this paper experimentally evaluates a hybrid algorithm based on the MPG and NSloan heuristics. This paper also evaluates the new hybrid heuristic for profile reduction when applied to matrices arising from two application areas against the most promising low-cost heuristics for solving the problem. The results obtained on a set of standard benchmark matrices show that the new hybrid heuristic method does not compare favorably with existing low-cost heuristics for profile reduction when applied to large-scale matrices.
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Gonzaga de Oliveira, S.L., Osthoff, C., Henderson Guedes de Oliveira, L.N. (2019). An Experimental Analysis of Heuristics for Profile Reduction. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_3
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