English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study $k$-GenEV, the problem of finding the top $k$ generalizedeigenvectors, and $k$-CCA, the problem of finding the top $k$ vectors incanonical-correlation analysis. We propose algorithms $\mathtt{LazyEV}$ and$\mathtt{LazyCCA}$ to solve the two problems with running times linearlydependent on the input size and on $k$.  Furthermore, our algorithms are DOUBLY-ACCELERATED: our running times dependonly on the square root of the matrix condition number, and on the square rootof the eigengap. This is the first such result for both $k$-GenEV or $k$-CCA.We also provide the first gap-free results, which provide running times thatdepend on $1/\sqrt{\varepsilon}$ rather than the eigengap.

Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition