Lower bounds for the smoothed number of Pareto optimal solutions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6648 LNCS, Issue , 2011, Pages 416-427

  Brunsch, Tobias ^a   Röglin, Heiko ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BI-CRITERIA; FIXED NUMBERS; INPUT MODELS; LOWER BOUNDS; MAXIMUM DENSITY; MULTICRITERIA OPTIMIZATION; OBJECTIVE FUNCTIONS; PARETO OPTIMAL SOLUTIONS; SEMI-RANDOM; UPPER BOUND;

OPTIMAL SYSTEMS;

PARETO PRINCIPLE;

EID: 79955777191     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20877-5_41     Document Type: Conference Paper

References (8)

1. 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)
2. Beier, R., Vöcking, B.: Random knapsack in expected polynomial time. Journal of Computer and System Sciences 69(3), 306-329 (2004)
3. Ehrgott, M.: Multicriteria Optimization, 2nd edn. Springer, Heidelberg (2005)
4. Moitra, A., O'Donnell, R.: Pareto optimal solutions for smoothed analysts. Technical report, CoRR, abs/1011.2249 (2010), http://arxiv.org/abs/ 1011.2249;
5. To appear in Proc. of the 43rd Annual ACM Symposium on Theory of Computing (STOC) (2011)
6. 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)
7. 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)
8. 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)