Fixed-sparsity matrix approximation from matrix-vector products
CoRR(2024)
摘要
We study the problem of approximating a matrix 𝐀 with a matrix
that has a fixed sparsity pattern (e.g., diagonal, banded, etc.), when
𝐀 is accessed only by matrix-vector products. We describe a simple
randomized algorithm that returns an approximation with the given sparsity
pattern with Frobenius-norm error at most (1+ε) times the best
possible error. When each row of the desired sparsity pattern has at most s
nonzero entries, this algorithm requires O(s/ε) non-adaptive
matrix-vector products with 𝐀. We also prove a matching lower-bound,
showing that, for any sparsity pattern with Θ(s) nonzeros per row and
column, any algorithm achieving (1+ϵ) approximation requires
Ω(s/ε) matrix-vector products in the worst case. We thus
resolve the matrix-vector product query complexity of the problem up to
constant factors, even for the well-studied case of diagonal approximation, for
which no previous lower bounds were known.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要