Abstract
In 2009, Röglin and Teng showed that the smoothed number of Pareto optimal solutions of linear multi-criteria optimization problems is polynomially bounded in the number n of variables and the maximum density φ of the semi-random input model for any fixed number of objective functions. Their bound is, however, not very practical because the exponents grow exponentially in the number d + 1 of objective functions. In a recent breakthrough, Moitra and O’Donnell improved this bound significantly to \(O \big ( n^{2d} \phi^{d(d+1)/2}\big )\).
An “intriguing problem”, which Moitra and O’Donnell formulate in their paper, is how much further this bound can be improved. The previous lower bounds do not exclude the possibility of a polynomial upper bound whose degree does not depend on d. In this paper we resolve this question by constructing a class of instances with Ω( (n φ)(d − logd) ·(1 − Θ1/φ) ) Pareto optimal solutions in expectation. For the bi-criteria case we present a higher lower bound of Ω( n 2 φ 1 − Θ1/φ ), which almost matches the known upper bound of O( n 2 φ).
A part of this work was done at Maastricht University and was supported by a Veni grant from the Netherlands Organisation for Scientific Research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beier, R., Röglin, H., Vöcking, B.: The smoothed number of pareto optimal solutions in bicriteria integer optimization. In: Fischetti, M., Williamson, D.P. (eds.) IPCO 2007. LNCS, vol. 4513, pp. 53–67. Springer, Heidelberg (2007)
Beier, R., Vöcking, B.: Random knapsack in expected polynomial time. Journal of Computer and System Sciences 69(3), 306–329 (2004)
Ehrgott, M.: Multicriteria Optimization, 2nd edn. Springer, Heidelberg (2005)
Moitra, A., O’Donnell, R.: Pareto optimal solutions for smoothed analysts. Technical report, CoRR, abs/1011.2249 (2010), http://arxiv.org/abs/1011.2249 ; To appear in Proc. of the 43rd Annual ACM Symposium on Theory of Computing (STOC) (2011)
Röglin, H., Teng, S.-H.: Smoothed analysis of multiobjective optimization. In: Proc. of the 50th Ann. IEEE Symp. on Foundations of Computer Science (FOCS), pp. 681–690 (2009)
Spielman, D.A., Teng, S.-H.: Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. Journal of the ACM 51(3), 385–463 (2004)
Spielman, D.A., Teng, S.-H.: Smoothed analysis: an attempt to explain the behavior of algorithms in practice. Communications of the ACM 52(10), 76–84 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brunsch, T., Röglin, H. (2011). Lower Bounds for the Smoothed Number of Pareto Optimal Solutions. In: Ogihara, M., Tarui, J. (eds) Theory and Applications of Models of Computation. TAMC 2011. Lecture Notes in Computer Science, vol 6648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20877-5_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-20877-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20876-8
Online ISBN: 978-3-642-20877-5
eBook Packages: Computer ScienceComputer Science (R0)