An Efficient Winner Approximation for a Series of Combinatorial Auctions

  • Naoki Fukuta
  • Takayuki Ito
Part of the Communications in Computer and Information Science book series (CCIS, volume 67)


In this paper, we show an analysis about approximated winner determination algorithms for iteratively conducted combinatorial auctions. Our algorithms are designed to effectively reuse last-cycle solutions to speed up the initial approximation performance on the next cycle. Experimental results show that our proposed algorithms outperform existing algorithms when a large number of similar bids are contained through iterations. Also, we show an enhanced algorithm effectively avoids undesirable reuses of the last solutions in the algorithm without serious computational overheads.


Combinatorial Auction Auction Mechanism Active Queue Management Winner Determination Winner Determination Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial Auctions. The MIT Press, Cambridge (2006)Google Scholar
  2. 2.
    Sandholm, T., Suri, S., Gilpin, A., Levine, D.: Cabob: A fast optimal algorithm for winner determination in combinatorial auctions. Management Science 51(3), 374–390 (2005)CrossRefGoogle Scholar
  3. 3.
    Sandholm, T.: Expressive commerce and its application to sourcing: How we conducted $35 billion of generalized combinatorial auctions. AI Magazine 28(3), 45–58 (2007)MathSciNetGoogle Scholar
  4. 4.
    McMillan, J.: Selling spectrum rights. The Journal of Economic Perspectives (1994)Google Scholar
  5. 5.
    Fukuta, N., Ito, T.: Periodical resource allocation using approximated combinatorial auctions. In: Proc. of The 2007 WIC/IEEE/ACM International Conference on Intelligent Agent Technology (IAT 2007), pp. 434–441 (2007)Google Scholar
  6. 6.
    Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions: Optimal and approximate approarches. In: Proc. of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 548–553 (1999)Google Scholar
  7. 7.
    Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in rapid, approximately efficient combinatorial auctions. Journal of the ACM 49, 577–602 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Zurel, E., Nisan, N.: An efficient approximate allocation algorithm for combinatorial auctions. In: Proc. of the Third ACM Conference on Electronic Commerce (EC 2001), pp. 125–136 (2001)Google Scholar
  9. 9.
    Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Proc. of the Proc. of 17th National Conference on Artificial Intelligence (AAAI 2000), pp. 22–29 (2000)Google Scholar
  10. 10.
    Thomadakis, M.E., Liu, J.C.: On the efficient scheduling of non-periodic tasks in hard real-time systems. In: Proc. of IEEE Real-Time Systems Symp., pp. 148–151 (1999)Google Scholar
  11. 11.
    Andrew, L.L., Hanly, S.V., Mukhtar, R.G.: Active queue management for fair resource allocation in wireless networks. IEEE Transactions on Mobile Computing, 231–246 (2008)Google Scholar
  12. 12.
    Xie, T., Qin, X.: Security-aware resource allocation for real-time parallel jobs on homogeneous and heterogeneous clusters. IEEE Transactions on Parallel and Distributed Systems 19(5), 682–697 (2008)CrossRefGoogle Scholar
  13. 13.
    Xiao, L., Chen, S., Zhang, X.: Adaptive memory allocations in clusters to handle unexpectedly large data-intensive jobs. IEEE Transactions on Parallel and Distributed Systems 15(7), 577–592 (2004)CrossRefGoogle Scholar
  14. 14.
    Fukuta, N., Ito, T.: Towards better approximation of winner determination for combinatorial auctions with large number of bids. In: Proc. of The 2006 WIC/IEEE/ACM International Conference on Intelligent Agent Technology(IAT 2006), pp. 618–621 (2006)Google Scholar
  15. 15.
    Fukuta, N., Ito, T.: Fine-grained efficient resource allocation using approximated combinatorial auctions–a parallel greedy winner approximation for large-scale problems. Web Intelligence and Agent Systems: An International Journal 7(1), 43–63 (2009)Google Scholar
  16. 16.
    Fukuta, N., Ito, T.: Performance analysis about parallel greedy approximation on combinatorial auctions. In: Bui, T.D., Ho, T.V., Ha, Q.T. (eds.) PRIMA 2008. LNCS (LNAI), vol. 5357, pp. 173–184. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Fukuta, N., Ito, T.: Fast partial reallocation in combinatorial auctions for iterative resource allocation. In: Proc. of 10th Pacific Rim International Workshop on Multi-Agents (PRIMA2007), pp. 196–207 (2007)Google Scholar
  18. 18.
    Fukuta, N., Ito, T.: Short-time approximation on combinatorial auctions – a comparison on approximated winner determination algorithms. In: Proc. of The 3rd International Workshop on Data Engineering Issues in E-Commerce and Services (DEECS 2007) pp. 42–55 (2007)Google Scholar
  19. 19.
    Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proc. of ACM Conference on Electronic Commerce (EC 2000), pp. 66–76 (2000)Google Scholar
  20. 20.
    Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135, 1–54 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Lehmann, D., Müller, R., Sandholm, T.: The winner determination problem. In: Cramton, P., Shoham, Y., Steinberg, R. (eds.) Combinatorial Auctions, pp. 507–538. MIT Press, Cambridge (2006)Google Scholar
  22. 22.
    Boutiler, C., Goldszmidt, M., Sabata, B.: Sequential auctions for the allocation of resources with complementarities. In: Proc. of International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 527–534 (1999)Google Scholar
  23. 23.
    Koenig, S., Tovey, C., Zheng, X., Sungur, I.: Sequential bundle-bid single-sale auction algorithms for decentralized control. In: Proc. of International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1359–1365 (2007)Google Scholar
  24. 24.
    Parkes, D.C., Cavallo, R., Elprin, N., Juda, A., Lahaie, S., Lubin, B., Michael, L., Shneidman, J., Sultan, H.: Ice: An iterative combinatorial exchange. In: The Proc. 6th ACM Conf. on Electronic Commerce, EC 2005 (2005)Google Scholar
  25. 25.
    de Vries, S., Vohra, R.V.: Combinatorial auctions: A survey. International Transactions in Operational Research 15(3), 284–309 (2003)Google Scholar
  26. 26.
    Dobzinski, S., Schapira, M.: An improved approximation algorithm for combinatorial auctions with submodular bidders. In: Proc. of the seventeenth annual ACM-SIAM symposium on Discrete algorithm (SODA 2006), pp. 1064–1073. ACM Press, New York (2006)CrossRefGoogle Scholar
  27. 27.
    Lavi, R., Swamy, C.: Truthful and near-optimal mechanism design via linear programming. In: 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), pp. 595–604 (2005)Google Scholar
  28. 28.
    Guo, Y., Lim, A., Rodrigues, B., Zhu, Y.: A non-exact approach and experiment studies on the combinatorial auction problem. In: Proc. of the 38th Hawaii International Conference on System Sciences (HICSS 2005), p. 82.1 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Naoki Fukuta
    • 1
  • Takayuki Ito
    • 2
    • 3
  1. 1.Shizuoka UniversityHamamatsu ShizuokaJapan
  2. 2.Nagoya Institute of TechnologyNagoyaJapan
  3. 3.Massachusetts Institute of TechnologyCambridgeU.S.A.

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