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 minimizing a smooth function subject to a summation constraintover its variables. By exploiting a connection between the greedy 2-coordinateupdate for this problem and equality-constrained steepest descent in the1-norm, we give a convergence rate for greedy selection under a proximalPolyak-Lojasiewicz assumption that is faster than random selection andindependent of the problem dimension $n$. We then consider minimizing with botha summation constraint and bound constraints, as arises in the support vectormachine dual problem. Existing greedy rules for this setting either guaranteetrivial progress only or require $O(n^2)$ time to compute. We show that bound-and summation-constrained steepest descent in the L1-norm guarantees moreprogress per iteration than previous rules and can be computed in only $O(n\log n)$ time.

Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm