Tight bounds for the subspace sketch problem with applications
In the subspace sketch problem one is given an n × d matrix A with O(log(nd)) bit entries, and would like to compress it in an arbitrary way to build a small space data structure Qp, so that for any given x ∊ ℝd, with probability at least 2/3, one has Qp(x) = (1 ± ∊ )|| Ax||p, where p ≥ 0 and the ra...
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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/10356/153688 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In the subspace sketch problem one is given an n × d matrix A with O(log(nd)) bit entries, and would like to compress it in an arbitrary way to build a small space data structure Qp, so that for any given x ∊ ℝd, with probability at least 2/3, one has Qp(x) = (1 ± ∊ )|| Ax||p, where p ≥ 0 and the randomness is over the construction of Qp. The central question is, how many bits are necessary to store Qp? This problem has applications to the communication of approximating the number of nonzeros in a matrix product, the size of coresets in projective clustering, the memory of streaming algorithms for regression in the row-update model, and embedding subspaces of Lp in functional analysis. A major open question is the dependence on the approximation factor ∊. We show if p ≥ 0 is not a positive even integer and d = Ω (log(1/∊ )), then Ω (∊-2d) bits are necessary. On the other hand, if p is a positive even integer, then there is an upper bound of O(dp log(nd)) bits independent of \varepsilon. Our results are optimal up to logarithmic factors. As corollaries of our main lower bound, we obtain new lower bounds for a wide range of applications, including the above, which in many cases are optimal. |
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