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SUMMARY:Conjugate gradient iterative hard thresholding for compressed sens
 ing and matrix completion - Tanner\, J (University of Oxford)
DTSTART:20140210T110000Z
DTEND:20140210T114500Z
UID:TALK50754@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Jeffrey D. Blanchard (Grinnell College)\, Ke Wei (
 University of Oxford) \n\nCompressed sensing and matrix completion are tec
 hniques by which simplicity in data can be exploited for more efficient da
 ta acquisition. For instance\, if a matrix is known to be (approximately) 
 low rank then it can be recovered from few of its entries. The design and 
 analysis of computationally efficient algorithms for these problems has be
 en extensively studies over the last 8 years. In this talk we present a ne
 w algorithm that balances low per iteration complexity with fast asymptoti
 c convergence. This algorithm has been shown to have faster recovery time 
 than any other known algorithm in the area\, both for small scale problems
  and massively parallel GPU implementations. The new algorithm adapts the 
 classical nonlinear conjugate gradient algorithm and shows the efficacy of
  a linear algebra perspective to compressed sensing and matrix completion.
 \n
LOCATION:Seminar Room 1\, Newton Institute
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