Restricted Quasiconvexity Isometry Property for Symmetric $α$-Stable Random Matrices
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915370565107712 |
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| author | Krishnan, Sunder Ram |
| author_facet | Krishnan, Sunder Ram |
| contents | We formulate a generalization of the Restricted Isometry Property (RIP) referred to as the Restricted Quasiconvexity Isometry Property (RQIP) for alpha stable random projections with $0<α<1$. A lower bound on the number of rows for RQIP to hold for random matrices whose entries are drawn from a symmetric $α$-stable ($SαS$) distribution is derived. The proof leverages two key components: a concentration inequality for empirical fractional moments of $SαS$ variables and a covering number bound for sparse $\ell_α$ balls. The resulting sample complexity reflects the polynomial tail behavior of the concentration and reinforces an observation made in the literature that the RIP framework may have to be replaced with other sparse recovery formulations in practice, such as those based on the null space property. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Restricted Quasiconvexity Isometry Property for Symmetric $α$-Stable Random Matrices Krishnan, Sunder Ram Probability Signal Processing We formulate a generalization of the Restricted Isometry Property (RIP) referred to as the Restricted Quasiconvexity Isometry Property (RQIP) for alpha stable random projections with $0<α<1$. A lower bound on the number of rows for RQIP to hold for random matrices whose entries are drawn from a symmetric $α$-stable ($SαS$) distribution is derived. The proof leverages two key components: a concentration inequality for empirical fractional moments of $SαS$ variables and a covering number bound for sparse $\ell_α$ balls. The resulting sample complexity reflects the polynomial tail behavior of the concentration and reinforces an observation made in the literature that the RIP framework may have to be replaced with other sparse recovery formulations in practice, such as those based on the null space property. |
| title | Restricted Quasiconvexity Isometry Property for Symmetric $α$-Stable Random Matrices |
| topic | Probability Signal Processing |
| url | https://arxiv.org/abs/2507.02649 |