Sublinear-time algorithms for compressive phase retrieval
In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately k-sparse vector x in C n given access to y= |φ x|, where |v| denotes the vector obtained from taking the absolute value of v inCn coordinate-wise. In this paper we present sublinear-time algorithms for...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161218 |
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| Summary: | In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately k-sparse vector x in C n given access to y= |φ x|, where |v| denotes the vector obtained from taking the absolute value of v inCn coordinate-wise. In this paper we present sublinear-time algorithms for a few for-each variants of the compressive phase retrieval problem which are akin to the variants considered for the classical compressive sensing problem in theoretical computer science. Our algorithms use pure combinatorial techniques and near-optimal number of measurements. |
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