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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider coordinate descent methods on convex quadratic problems, in whichexact line searches are performed at each iteration. (This algorithm isidentical to Gauss-Seidel on the equivalent symmetric positive definite linearsystem.) We describe a class of convex quadratic problems for which therandom-permutations version of cyclic coordinate descent (RPCD) outperforms thestandard cyclic coordinate descent (CCD) approach, yielding convergencebehavior similar to the fully-random variant (RCD). A convergence analysis isdeveloped to explain the empirical observations.

Analyzing Random Permutations for Cyclic Coordinate Descent