{"title":"论克罗内克结构矩阵的限制等势特性","authors":"Yanbin He, Geethu Joseph","doi":"arxiv-2409.08699","DOIUrl":null,"url":null,"abstract":"In this work, we study the restricted isometry property (RIP) of\nKronecker-structured matrices, formed by the Kronecker product of two factor\nmatrices. Previously, only upper and lower bounds on the restricted isometry\nconstant (RIC) in terms of the RICs of the factor matrices were known. We\nderive a probabilistic measurement bound for the $s$th-order RIC. We show that\nthe Kronecker product of two sub-Gaussian matrices satisfies RIP with high\nprobability if the minimum number of rows among two matrices is $\\mathcal{O}(s\n\\ln \\max\\{N_1, N_2\\})$. Here, $s$ is the sparsity level, and $N_1$ and $N_2$\nare the number of columns in the matrices. We also present improved measurement\nbounds for the recovery of Kronecker-structured sparse vectors using\nKronecker-structured measurement matrices. Finally, our analysis is further\nextended to the Kronecker product of more than two matrices.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":"14 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Restricted Isometry Property of Kronecker-structured Matrices\",\"authors\":\"Yanbin He, Geethu Joseph\",\"doi\":\"arxiv-2409.08699\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we study the restricted isometry property (RIP) of\\nKronecker-structured matrices, formed by the Kronecker product of two factor\\nmatrices. Previously, only upper and lower bounds on the restricted isometry\\nconstant (RIC) in terms of the RICs of the factor matrices were known. We\\nderive a probabilistic measurement bound for the $s$th-order RIC. We show that\\nthe Kronecker product of two sub-Gaussian matrices satisfies RIP with high\\nprobability if the minimum number of rows among two matrices is $\\\\mathcal{O}(s\\n\\\\ln \\\\max\\\\{N_1, N_2\\\\})$. Here, $s$ is the sparsity level, and $N_1$ and $N_2$\\nare the number of columns in the matrices. We also present improved measurement\\nbounds for the recovery of Kronecker-structured sparse vectors using\\nKronecker-structured measurement matrices. Finally, our analysis is further\\nextended to the Kronecker product of more than two matrices.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08699\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Restricted Isometry Property of Kronecker-structured Matrices
In this work, we study the restricted isometry property (RIP) of
Kronecker-structured matrices, formed by the Kronecker product of two factor
matrices. Previously, only upper and lower bounds on the restricted isometry
constant (RIC) in terms of the RICs of the factor matrices were known. We
derive a probabilistic measurement bound for the $s$th-order RIC. We show that
the Kronecker product of two sub-Gaussian matrices satisfies RIP with high
probability if the minimum number of rows among two matrices is $\mathcal{O}(s
\ln \max\{N_1, N_2\})$. Here, $s$ is the sparsity level, and $N_1$ and $N_2$
are the number of columns in the matrices. We also present improved measurement
bounds for the recovery of Kronecker-structured sparse vectors using
Kronecker-structured measurement matrices. Finally, our analysis is further
extended to the Kronecker product of more than two matrices.