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 consider an optimization problem with strongly convex objective and linearinequalities constraints. To be able to deal with a large number of constraintswe provide a penalty reformulation of the problem. As penalty functions we usea version of the one-sided Huber losses. The smoothness properties of thesefunctions allow us to choose time-varying penalty parameters in such a way thatthe incremental procedure with the diminishing step-size converges to the exactsolution with the rate $O(1/{\sqrt k})$. To the best of our knowledge, wepresent the first result on the convergence rate for the penalty-based gradientmethod, in which the penalty parameters vary with time.

Convergence Rate of a Penalty Method for Strongly Convex Problems with Linear Constraints