Iterative Algorithms
Duration: 1 hour 8 mins 45 secs
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About this item
| Description: |
Montanari, A (Stanford)
Tuesday 12 January 2010, 09:30-10:30 |
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| Created: | 2010-01-13 14:16 | ||
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| Collection: | Stochastic Processes in Communication Sciences | ||
| Publisher: | Isaac Newton Institute | ||
| Copyright: | Montanari, A | ||
| Language: | eng (English) | ||
| Credits: |
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| Abstract: | The problem of estimating a high dimensional vector from a set of linear observations arises in a number of engineering disciplines. It becomes particularly challenging when the underlying signal has some non-linear structure that needs to be exploited. I will present a new class of iterative algorithms inspired by probabilistic graphical models ideas, that appear to be asymptotically optimal in specific contexts. I will discuss in particular the application to compressed sensing problems. [Joint work with David L. Donoho and Arian Maleki] |
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