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

This paper shows that the OSGA algorithm -- which uses first-orderinformation to solve convex optimization problems with optimal complexity --can be used to efficiently solve arbitrary bound-constrained convexoptimization problems. This is done by constructing an explicit method as wellas an inexact scheme for solving the bound-constrained rational subproblemrequired by OSGA. This leads to an efficient implementation of OSGA onlarge-scale problems in applications arising signal and image processing,machine learning and statistics. Numerical experiments demonstrate thepromising performance of OSGA on such problems. ions to show the efficiency ofthe proposed scheme. A software package implementing OSGA for bound-constrainedconvex problems is available.

An optimal subgradient algorithm for large-scale bound-constrained convex optimization