Transformed $\ell_p$ Minimization Model and Sparse Signal Recovery
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866915935825166336 |
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| author | Li, Ziwei Chen, Wengu Ge, Huanmin Yang, Dachun |
| author_facet | Li, Ziwei Chen, Wengu Ge, Huanmin Yang, Dachun |
| contents | In this article, we introduce a minimization model via a non-convex transformed $\ell_p$ (TLp) penalty function with two parameters $a\in(0,\infty)$ and $p\in(0,1]$, where the case $p=1$ is known and was established by S. Zhang and J. Xin. Using the sparse convex-combination technique, we establish the exact and the stable sparse signal recovery based on the restricted isometry property (RIP). We apply a modified iteratively re-weighted least squares method and the difference of convex functions algorithm (DCA) to give the IRLSTLp algorithm for unconstrained TLp minimization and prove some convergence results. Finally, we conduct some numerical experiments to show the robustness of the IRLSTLp and the flexibility of the TLp minimization model. The novelty of these results lies in three aspects: (i) We introduce the concept of the relaxation degree RD$_P$ of a separable penalty function $P$ to quantitatively measure how closely $P$ approaches $\ell_0$, whose significance also lies in revealing the functional relationship of the parameters involved to keep a high performance of a multi-parameter minimization model. (ii) We introduce the TLp penalty, which includes two aforementioned adjustable parameters, offering more flexibility and stronger sparsity-promotion capability of the TLp minimization model, compared with the $\ell_p$ and the TL1 minimization models. (iii) The obtained RIP upper bound for signal recovery via TLp minimization can reduce, when $p\in(0,1]$ and as $a\to \infty$, to the sharp RIP bound obtained by R. Zhang and S. Li and, especially, can recover, when $p=1$, the well-known sharp bound $δ_{2s}<\frac{\sqrt{2}}{2}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09722 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformed $\ell_p$ Minimization Model and Sparse Signal Recovery Li, Ziwei Chen, Wengu Ge, Huanmin Yang, Dachun Functional Analysis Information Theory Classical Analysis and ODEs Primary 94A12, Secondary 90C26, 90C90, 94A15 In this article, we introduce a minimization model via a non-convex transformed $\ell_p$ (TLp) penalty function with two parameters $a\in(0,\infty)$ and $p\in(0,1]$, where the case $p=1$ is known and was established by S. Zhang and J. Xin. Using the sparse convex-combination technique, we establish the exact and the stable sparse signal recovery based on the restricted isometry property (RIP). We apply a modified iteratively re-weighted least squares method and the difference of convex functions algorithm (DCA) to give the IRLSTLp algorithm for unconstrained TLp minimization and prove some convergence results. Finally, we conduct some numerical experiments to show the robustness of the IRLSTLp and the flexibility of the TLp minimization model. The novelty of these results lies in three aspects: (i) We introduce the concept of the relaxation degree RD$_P$ of a separable penalty function $P$ to quantitatively measure how closely $P$ approaches $\ell_0$, whose significance also lies in revealing the functional relationship of the parameters involved to keep a high performance of a multi-parameter minimization model. (ii) We introduce the TLp penalty, which includes two aforementioned adjustable parameters, offering more flexibility and stronger sparsity-promotion capability of the TLp minimization model, compared with the $\ell_p$ and the TL1 minimization models. (iii) The obtained RIP upper bound for signal recovery via TLp minimization can reduce, when $p\in(0,1]$ and as $a\to \infty$, to the sharp RIP bound obtained by R. Zhang and S. Li and, especially, can recover, when $p=1$, the well-known sharp bound $δ_{2s}<\frac{\sqrt{2}}{2}$. |
| title | Transformed $\ell_p$ Minimization Model and Sparse Signal Recovery |
| topic | Functional Analysis Information Theory Classical Analysis and ODEs Primary 94A12, Secondary 90C26, 90C90, 94A15 |
| url | https://arxiv.org/abs/2603.09722 |