MA 391: Spectral Algorithms

Credits: 3:0


Part I - Applications of Spectral Algotihms: Best-Fit Subspaces, Mixture models, Probabilistic Clustering,Recursive Clustering, Optimization via low-rank approximation.

Part II - Algorithms: Matrix Approximation via Random Sampling, Adaptive Sampling Methods, Extensions of SVD to tensors.


Suggested books :

  1. Ravindran Kannan and Santosh Vempala, Spectral Algorithms ,Foundations and Trends in Theoretical Computer Science, 4:3-4, now Publishers.

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E-mail: chairman.math[at]iisc[dot]ac[dot]in