## SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm

Publication
, Journal Article

Babadi, B; Kalouptsidis, N; Tarokh, V

January 6, 2009

We develop a Recursive $\mathcal{L}_1$-Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an Expectation-Maximization type algorithm. Simulation studies in the context of channel estimation, employing multi-path wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.

### Duke Scholars

## Publication Date

January 6, 2009

### Citation

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Chicago

ICMJE

MLA

NLM

Babadi, B., Kalouptsidis, N., & Tarokh, V. (2009). SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least
Squares Algorithm.

Babadi, Behtash, Nicholas Kalouptsidis, and Vahid Tarokh. “SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least
Squares Algorithm,” January 6, 2009.

Babadi B, Kalouptsidis N, Tarokh V. SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least
Squares Algorithm. 2009 Jan 6;

Babadi, Behtash, et al.

*SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm*. Jan. 2009.Babadi B, Kalouptsidis N, Tarokh V. SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least
Squares Algorithm. 2009 Jan 6;

## Publication Date

January 6, 2009