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This research aims to develop efficient greedy algorithms for sparse signal recovery in under-determined systems.

On a class of greedy sparse recovery algorithms

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Novel greedy algorithms enhance sparse signal recovery accuracy, indicating improvements over classical methods.

This research aims to develop efficient greedy algorithms for sparse signal recovery in under-determined systems.
Proposed a novel greedy approach based on characterizing solutions in the sparse space.
Developed an l2-based orthogonal matching pursuit algorithm that improves recovery accuracy.
Derived an l1-based algorithm, Alg_GL1, showing better performance compared to the classical basis pursuit.
The proposed OMP-type algorithm demonstrated higher recovery accuracy than classical OMP algorithms.
Alg_GL1 significantly outperformed the classical basis pursuit in numerical simulations.
The new algorithms showed robustness against numerical instability and measurement disturbances.

Li et al. (2026) studied this question.

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