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

Nonconvex optimization problems arise in different research fields and arouselots of attention in signal processing, statistics and machine learning. Inthis work, we explore the accelerated proximal gradient method and some of itsvariants which have been shown to converge under nonconvex context recently. Weshow that a novel variant proposed here, which exploits adaptive momentum andblock coordinate update with specific update rules, further improves theperformance of a broad class of nonconvex problems. In applications to sparselinear regression with regularizations like Lasso, grouped Lasso, capped$\ell_1$ and SCAP, the proposed scheme enjoys provable local linearconvergence, with experimental justification.

Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics