Performance Analysis about Parallel Greedy Approximation on Combinatorial Auctions

  • Naoki Fukuta
  • Takayuki Ito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)


Combinatorial auctions provide suitable mechanisms for efficient allocation of resources to self-interested agents. Considering ubiquitous computing scenarios, the ability to complete an auction within a fine-grained time period without loss of allocation efficiency is in strong demand. Furthermore, to achieve such scenarios, it is very important to handle a large number of bids in an auction. Recently, we proposed an algorithm to obtain sufficient quality of winners in very short time. However, it is demanded to analyze which factor is mainly affected to obtain such a good performance. Also it is demanded to clarify the actual implementation-level performance of the algorithm compared to a major commercial-level generic problem solver. In this paper, we show our parallel greedy updating approach contributes its better performance. Furthermore, we show our approach has a certain advantage compared to a latest commercial-level implementation of generic LP solver through various experiments.


Simulated Annealing Combinatorial Auction Winner Determination Winner Determination Problem Sequential Auction 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial Auctions. MIT Press, Cambridge (2006)zbMATHGoogle 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)CrossRefzbMATHGoogle Scholar
  3. 3.
    McMillan, J.: Selling spectrum rights. The Journal of Economic Perspectives (1994)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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 (PRIMA 2007), pp. 196–207 (2007)Google Scholar
  8. 8.
    Fukuta, N., Ito, T.: Toward a large scale e-market: A greedy and local search based winner determination. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS, vol. 4570, pp. 354–363. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in rapid, approximately efficient combinatorial auctions. Journal of the ACM 49, 577–602 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Proc. of the AAAI 2000, pp. 22–29 (2000)Google Scholar
  11. 11.
    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
  12. 12.
    de Vries, S., Vohra, R.V.: Combinatorial auctions: A survey. International Transactions in Operational Research 15(3), 284–309 (2003)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proc. of EC 2000, pp. 66–76 (2000)Google Scholar
  14. 14.
    Avasarala, V., Polavarapu, H., Mullen, T.: An approximate algorithm for resource allocation using combinatorial auctions. In: Proc. of the 2006 WIC/IEEE/ACM International Conference on Intelligent Agent Technology (IAT 2006), pp. 571–578 (2006)Google Scholar
  15. 15.
    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
  16. 16.
    Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135, 1–54 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kastner, R., Hsieh, C., Potkonjak, M., Sarrafzadeh, M.: On the sensitivity of incremental algorithms for combinatorial auctions. In: Proc. International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS 2002), pp. 81–88 (2002)Google Scholar
  18. 18.
    Guo, Y., Lim, A., Rodrigues, B., Zhu, Y.: A non-exact approach and experiment studies on the combinatorial auction problem. In: Proc. of HICSS 2005, p. 82.1 (2005)Google Scholar
  19. 19.
    Dobzinski, S., Schapira, M.: An improved approximation algorithm for combinatorial auctions with submodular bidders. In: SODA 2006: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm, pp. 1064–1073. ACM Press, New York (2006)CrossRefGoogle Scholar
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Naoki Fukuta
    • 1
  • Takayuki Ito
    • 2
    • 3
  1. 1.Shizuoka UniversityHamamatsuJapan
  2. 2.Nagoya Institute of TechnologyGokiso-choJapan
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations