mlsanityData to 7 Oct 2026

Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage

arXiv 1401.0463 ↗First posted 2 Jan 2014eess.SY cs.SYcited by 15

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Abstract (arXiv)

This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero. We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error. Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2014.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 0; 2024: 3; 2025: 1.

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