Skip to main content

Vector Assignment Problems: A General Framework

  • Conference paper
  • First Online:
Algorithms — ESA 2002 (ESA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2461))

Included in the following conference series:

Abstract

We present a general framework for vector assignment problems. In such problems one aims at assigning n input vectors to m machines such that the value of a given target function is minimized. While previous approaches concentrated on simple target functions such as max-max, the general approach presented here enables us to design a PTAS for a wide class of target functions. In particular we are able to deal with non-monotone target functions and asymmetric settings where the cost functions per machine may be different for different machines. This is done by combining a graph-based technique and a new technique of preprocessing the input vectors.

Research supported in part by the Israel Science Foundation (grant no. 250/01)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. N. Alon, Y. Azar, G. Woeginger, and T. Yadid. Approximation schemes for scheduling. In Proc. 8th ACM-SIAM Symp. on Discrete Algorithms, pages 493–500, 1997.

    Google Scholar 

  2. A.K. Chandra and C. K. Wong. Worst-case analysis of a placement algorithm related to storage allocation. SIAM Journal on Computing, 4(3):249–263, 1975.

    Article  MATH  MathSciNet  Google Scholar 

  3. C. Chekuri and S. Khanna. On multi-dimensional packing problems. In Proceedings of the Tenth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 185–194, 1999.

    Google Scholar 

  4. R.A. Cody and E. G. Coffman, Jr. Record allocation for minimizing expected retrieval costs on drum-like storage devices. J. Assoc. Comput. Mach., 23(1):103–115, 1976.

    MATH  MathSciNet  Google Scholar 

  5. E.G. Coffman, Jr. and George S. Lueker. Approximation algorithms for extensible bin packing. In Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 586–588, 2001.

    Google Scholar 

  6. J. Csirik, H. Kellerer, and G. Woeginger. The exact lpt-bound for maximizing the minimum completion time. Operations Research Letters, 11:281–287, 1992.

    Article  MATH  MathSciNet  Google Scholar 

  7. W. F. de la Vega and G. S. Lueker. Bin packing can be solved within 1 + ∈ in linear time. Combinatorica, 1(4):349–355, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  8. P. Dell’Olmo, H. Kellerer, M. G. Speranza, and Zs. Tuza. A 13/12 approximation algorithm for bin packing with extendable bins. Information Processing Letters, 65(5):229–233, 1998.

    Article  MathSciNet  Google Scholar 

  9. P. Dell’Olmo and M. G. Speranza. Approximation algorithms for partitioning small items in unequal bins to minimize the total size. Discrete Applied Mathematics, 94:181–191, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  10. M.R. Garey, R. L. Graham, D. S. Johnson, and A.C.C. Yao. Resource constrained scheduling as generalized bin packing. Journal of Combinatorial Theory (Series A), 21:257–298, 1976.

    Article  MATH  MathSciNet  Google Scholar 

  11. R. L. Graham. Bounds for certain multiprocessor anomalies. Bell System Technical Journal, 45:1563–1581, 1966.

    Google Scholar 

  12. R. L. Graham. Bounds on multiprocessing timing anomalies. SIAM J. Appl. Math, 17:416–429, 1969.

    Article  MATH  MathSciNet  Google Scholar 

  13. D. S. Hochbaum and D.B. Shmoys. Using dual approximation algorithms for scheduling problems: theoretical and practical results. Journal of the ACM, 34(1):144–162, 1987.

    Article  MathSciNet  Google Scholar 

  14. N. Karmarkar and R. M. Karp. An efficient approximation scheme for the one-dimensional bin-packing problem. In Proc. 23rd Ann. Symp. on Foundations of Computer Science, 1982.

    Google Scholar 

  15. S. Sahni. Algorithms for scheduling independent tasks. Journal of the Association for Computing Machinery, 23:116–127, 1976.

    MATH  MathSciNet  Google Scholar 

  16. G. J. Woeginger. A polynomial time approximation scheme for maximizing the minimum machine completion time. Operations Research Letters, 20:149–154, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  17. G. J. Woeginger. There is no asymptotic PTAS for two-dimensional vector packing. Information Processing Letters, 64(6):293–297, 1997.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Epstein, L., Tassa, T. (2002). Vector Assignment Problems: A General Framework. In: Möhring, R., Raman, R. (eds) Algorithms — ESA 2002. ESA 2002. Lecture Notes in Computer Science, vol 2461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45749-6_42

Download citation

  • DOI: https://doi.org/10.1007/3-540-45749-6_42

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44180-9

  • Online ISBN: 978-3-540-45749-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics