The Dynamics of Matrix Momentum

  • Magnus Rattray
  • David Saad
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


We analyse the matrix momentum algorithm, which provides an efficient approximation to on-line Newton’s method, by extending a recent statistical mechanics framework to include second order algorithms. We study the efficacy of this method when the Hessian is available and also consider a practical implementation which uses a single example estimate of the Hessian. The method is shown to provide excellent asymptotic performance, although the single example implementation is sensitive to the choice of training parameters. We conjecture that matrix momentum could provide efficient matrix inversion for other second order algorithms.


Gradient Descent Hide Node Generalization Error Asymptotic Performance Order Algorithm 
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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Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Magnus Rattray
    • 1
  • David Saad
    • 1
  1. 1.Neural Computing Research GroupAston UniversityBirminghamUK

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