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Fast algorithms for matrix completion and compressed sensing

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

Compressed sensing and matrix completion are techniques by which simplicity in data can be exploited for more efficient data 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 been extensively studies over the last 8 years. In this talk we present new algorithms that balances low per iteration complexity with fast asymptotic convergence, allowing solutions to much larger problem sizes. These algorithms 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.

This talk is part of the Applied and Computational Analysis series.

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