A Study on Conditions for Sparse Solution Recovery in Compressive Sensing
| dc.contributor.author | Eydelzon, Anatoly | en_US |
| dc.date.accessioned | 2018-06-18T17:58:14Z | en_US |
| dc.date.available | 2018-06-18T17:58:14Z | en_US |
| dc.date.issued | 2007-08 | en_US |
| dc.date.note | August 2007 (Revised on May 2008) | en_US |
| dc.description | This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/22283 | en_US |
| dc.description.abstract | It is well-known by now that under suitable conditions L1 minimization can recover sparse solutions to under-determined linear systems of equations. More precisely, by solving the convex optimization problem min{||x||1 : Αx = b}, where A is an m by n measurement matrix with m < n, one can obtain the sparsest solution x* to Ax = b provided that the measurement matrix A has certain properties and the sparsity level k of x is suffciently small. This fact has led to active research in the area of compressive sensing and other applications. The central question for this problem is the following. Given a type of measurements, a signal's length n and sparsity level k, what is the minimum measurement size m that ensures recovery? Or equivalently, given a type of measurements, a signal length n and a measurement size m, what is the maximum recoverable sparsity level k? The above fundamental question has been answered, with varying degrees of precision, by a number of researchers for a number of different random or semi-random measurement matrices. However, all the existing results still involve unknown constants of some kind and thus are unable to provide precise answers to specific situations. For example, let A be an m by n partial DCT matrix with n = 107 and m = 5 x 105 (n/m = 20). Can we provide a reasonably good estimate on the maximum recoverable sparsity k? In this research, we attempt to provide a more precise answer to the central question raised above. By studying new suffcient conditions for exact recovery of sparse solutions, we propose a new technique to estimate recoverable sparsity for different kinds of deterministic, random and semi-random matrices. We will present empirical evidence to show the practical success of our approach, though further research is still needed to formally establish its effectiveness. | en_US |
| dc.format.extent | 88 pp | en_US |
| dc.identifier.citation | Eydelzon, Anatoly. "A Study on Conditions for Sparse Solution Recovery in Compressive Sensing." (2007) <a href="https://hdl.handle.net/1911/102076">https://hdl.handle.net/1911/102076</a>. | en_US |
| dc.identifier.digital | TR07-12 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/102076 | en_US |
| dc.language.iso | eng | en_US |
| dc.title | A Study on Conditions for Sparse Solution Recovery in Compressive Sensing | en_US |
| dc.type | Technical report | en_US |
| dc.type.dcmi | Text | en_US |
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