Decremental $(1+\epsilon)$-Approximate Maximum Eigenvector: Dynamic Power Method
<|reference_start|>Decremental $(1+\epsilon)$-Approximate Maximum Eigenvector: Dynamic Power Method: We present a dynamic algorithm for maintaining $(1+\epsilon)$-approximate maximum eigenvector and eigenvalue of a positive semi-definite matrix $A$ undergoing \emph{decreasing} updates, i.e., updates which may only decrease eigenvalues. Given a vector $v$ updating $A\gets A-vv^{\top}$, our algorithm takes $\tilde{O}(\mathrm{nnz}(v))$ amortized update time, i.e., polylogarithmic per non-zeros in the update vector.   Our technique is based on a novel analysis of the influential power method in the dynamic setting. The two previous sets of techniques have the following drawbacks (1) algebraic techniques can maintain exact solutions but their update time is at least polynomial per non-zeros, and (2) sketching techniques admit polylogarithmic update time but suffer from a crude additive approximation.   Our algorithm exploits an oblivious adversary. Interestingly, we show that any algorithm with polylogarithmic update time per non-zeros that works against an adaptive adversary and satisfies an additional natural property would imply a breakthrough for checking psd-ness of matrices in $\tilde{O}(n^{2})$ time, instead of $O(n^{\omega})$ time.<|reference_end|>
@article{adil2024decremental,
    title={Decremental $(1+\epsilon)$-Approximate Maximum Eigenvector: Dynamic
  Power Method},
    author={Deeksha Adil and Thatchaphol Saranurak},
    journal={arXiv preprint arXiv:2402.17929},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17929},
    primaryClass={cs.DS}
}