Skip to main content

Obtain Semi-definite Matrix Eigenvalue Based on LANCZOS Algorithm

  • Chapter
  • 2209 Accesses

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 148))

Abstract

Orthogonal projection lanczos algorithm is the effective way to solve the complex structural vibration, vibration frequency and vibration mode. Its idea is to translate high-level vibration problem into low-level to solve the vibration problem without losing eigenvalue. In this paper, a simple and convenient method of computing is presented using Orthogonal Projection lanczos algorithm to solve semi-definite matrix generalized eigenvalue problems and re-solve the eigenvalue.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhang, H.X.: Automobile design. China Machine Press, Beijing (1999)

    Google Scholar 

  2. Qin, X., Jiang, H.: A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters. Journal of Parallel and Distributed Computing 65(8), 885–900 (2005)

    Article  MATH  Google Scholar 

  3. Huang, K.: Advanced Engineering Mathematics. People Railway Publishing House, Beijing (1999)

    Google Scholar 

  4. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing, SAC 2002, pp. 603–607. ACM Press, Madrid (2002)

    Google Scholar 

  5. Shi, W.Y., Guo, Y.F., Xue, X.Y.: Matrix-based Kernel Principal Component Analysis for Large-scale Data Set. In: International Joint Conference on Neural Networks (2009)

    Google Scholar 

  6. Reyes-Sierra, M., Coello, C.A.C.: Fitness inheritance in multi-objective

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Shao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag GmbH Berlin Heidelberg

About this chapter

Cite this chapter

Shao, H., Wang, Z., Xu, W. (2012). Obtain Semi-definite Matrix Eigenvalue Based on LANCZOS Algorithm. In: Jin, D., Lin, S. (eds) Advances in Electronic Commerce, Web Application and Communication. Advances in Intelligent and Soft Computing, vol 148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28655-1_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28655-1_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28654-4

  • Online ISBN: 978-3-642-28655-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics