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

Finding a point in the intersection of a collection of closed convex sets,that is the convex feasibility problem, represents the main modeling strategyfor many computational problems. In this paper we analyze new stochasticreformulations of the convex feasibility problem in order to facilitate thedevelopment of new algorithmic schemes. We also analyze the conditioningproblem parameters using certain (linear) regularity assumptions on theindividual convex sets. Then, we introduce a general random projectionalgorithmic framework, which extends to the random settings many existingprojection schemes, designed for the general convex feasibility problem. Ourgeneral random projection algorithm allows to project simultaneously on severalsets, thus providing great flexibility in matching the implementation of thealgorithm on the parallel architecture at hand. Based on the conditioningparameters, besides the asymptotic convergence results, we also derive explicitsublinear and linear convergence rates for this general algorithmic framework.

Randomized projection methods for convex feasibility problems: conditioning and convergence rates