## Just relax: Convex programming methods for subset selection and sparse approximation (2004)

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@TECHREPORT{Tropp04justrelax:,

author = {Joel A. Tropp},

title = {Just relax: Convex programming methods for subset selection and sparse approximation},

institution = {},

year = {2004}

}

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### Abstract

Abstract. Subset selection and sparse approximation problems request a good approximation of an input signal using a linear combination of elementary signals, yet they stipulate that the approximation may only involve a few of the elementary signals. This class of problems arises throughout electrical engineering, applied mathematics and statistics, but small theoretical progress has been made over the last fifty years. Subset selection and sparse approximation both admit natural convex relaxations, but the literature contains few results on the behavior of these relaxations for general input signals. This report demonstrates that the solution of the convex program frequently coincides with the solution of the original approximation problem. The proofs depend essentially on geometric properties of the ensemble of elementary signals. The results are powerful because sparse approximation problems are combinatorial, while convex programs can be solved in polynomial time with standard software. Comparable new results for a greedy algorithm, Orthogonal Matching Pursuit, are also stated. This report should have a major practical impact because the theory applies immediately to many real-world signal processing problems. 1.

### Citations

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75 |
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Citation Context ...utions to both problems. ⋆ ⋆ ⋆ ⋆ ⋆ Sparse approximation has been studied for nearly a century, and it has numerous applications. Temlyakov [Tem02] locates the first example in a 1907 paper of Schmidt =-=[Sch07]-=-. In the 1950s, statisticians launched an extensive investigation of another sparse approximation problem called subset selection in regression [Mil02]. Later, approximation theorists began a systemat... |

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63 |
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58 |
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