Abstract
We implement the algorithm for the max-min resource sharing problem described in [7], using a new line search technique for determining a suitable step length. Our line search technique uses a modified potential function that is less costly to evaluate, thus heuristically simplifying the computation. Observations concerning the quality of the dual solution and oscillating behavior of the algorithm are made. First numerical observations are briefly discussed. In particular we study a certain class of linear programs, namely the computational bottleneck of an algorithm from [8] for solving strip packing with an approach from [10, 13]. For these, we obtain practical running times. Our implementation is able to solve instances for small accuracy parameters ε for which the methods proposed in theory are out of practical interest. More precisely, the technique from improves the known runtime bound of O(M 6ln 2(Mn/(at))+M 5 n/t+ln (Mn/(at))) to the more favourable bound O(M(ε − − 3(ε − − 2+ln M)+M(ε − − 2+ln M))), where n denotes the number of items, M the number of distinct item widths, a the width of the narrowest item and t is a desired additive tolerance. Keywords: Algorithm Engineering, Implementation, Testing, Evaluation and Fine-tuning, Mathematical Programming.
Research of the authors was supported in part by EU Project CRESCCO, Critical Resource Sharing for Cooperation in Complex Systems, IST-2001-33135 and by DFG Project, Entwicklung und Analyse von Approximativen Algorithmen für Gemischte und Verallgemeinerte Packungs- und Überdeckungsprobleme, JA 612/10-1.
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Aizatulin, M., Diedrich, F., Jansen, K.: Experimental Results in Approximation of Max-Min Resouce Sharing, http://www.informatik.uni-kiel.de/textasciitildefdi/
Bienstock, D.: Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice. Kluwer, Dordrecht (2002)
Charikar, M., Chekuri, C., Goel, A., Guha, S., Plotkin, S.: Approximating a finite metric by a small number of tree metrics. In: Proceedings 39th IEEE Symposium on Foundations of Computer Science, FOCS 1998, pp. 379–388 (1998)
Garg, N., Könemann, J.: Fast and simpler algorithms for multicommodity flow and other fractional packing problems. In: Proceedings 39th IEEE Symposium on Foundations of Computer Science, FOCS 1998, pp. 300–309 (1998)
Grigoriadis, M.D., Khachiyan, L.G.: Fast approximation schemes for convex programs with many blocks and coupling constraints. SIAM Journal on Optimization 4, 86–107 (1994)
Grigoriadis, M.D., Khachiyan, L.G.: Coordination complexity of parallel price-directive decomposition. Mathematics of Operations Research 2, 321–340 (1996)
Grigoriadis, M.D., Khachiyan, L.G., Porkolab, L., Villavicencio, J.: Approximate max-min resource sharing for structured concave optimization. SIAM Journal on Optimization 41, 1081–1091 (2001)
Jansen, K.: Approximation algorithms for min-max and max-min resource sharing problems and applications. In: Bampis, E., Jansen, K., Kenyon, C. (eds.) Efficient Approximation and Online Algorithms. LNCS, vol. 3484, pp. 156–202. Springer, Heidelberg (2006)
Jansen, K., Zhang, H.: Approximation algorithms for general packing problems with modified logarithmic potential function. In: Proceedings 2nd IFIP International Conference on Theoretical Computer Science, TCS 2002, pp. 255–266. Kluwer, Dordrecht (2002)
Karmarkar, N., Karp, R.M.: An efficient approximation scheme for the one-dimensional bin-packing problem. In: Proceedings 23rd IEEE Symposium on Foundations of Computer Science, FOCS 1982, pp. 312–320 (1982)
Kenyon, C., Rémila, E.: Approximate strip packing. Mathematics of Operations Research 25, 645–656 (2000)
Könemann, J.: Fast combinatorial algorithms for packing and covering problems, Diploma Thesis, Max-Planck-Institute for Computer Science Saarbrücken (2000)
Korte, B., Vygen, J.: Combinatorial Optimization: Theory and Algorithms. In: Algorithms and Combinatorics, vol. 2, Springer, Heidelberg (2000)
Lu, Q., Zhang, H.: Implementation of Approximation Algorithms for the Multicast Congestion Problem. In: Nikoletseas, S.E. (ed.) WEA 2005. LNCS, vol. 3503, pp. 152–164. Springer, Heidelberg (2005)
Plotkin, S.A., Shmoys, D.B., Tardos, E.: Fast approximation algorithms for fractional packing and covering problems. Mathematics of Operations Research 20, 257–301 (1995)
Villavicencio, J., Grigoriadis, M.D.: Approximate Lagrangian decomposition with a modified Karmarkar logarithmic potential. In: Pardalos, P., Hearn, D.W., Hager, W.W. (eds.) Network Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 450, pp. 471–485. Springer, Heidelberg (1997)
Young, N.E.: Randomized rounding without solving the linear program. In: Young, N.E. (ed.) Proceedings 6th ACM-SIAM Symposium on Discrete Algorithms, SODA 1995, pp. 170–178 (1995)
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Aizatulin, M., Diedrich, F., Jansen, K. (2006). Implementation of Approximation Algorithms for the Max-Min Resource Sharing Problem. In: Àlvarez, C., Serna, M. (eds) Experimental Algorithms. WEA 2006. Lecture Notes in Computer Science, vol 4007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11764298_19
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DOI: https://doi.org/10.1007/11764298_19
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