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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this paper we combine the stochastic variance reduced gradient (SVRG)method [17] with the primal dual fixed point method (PDFP) proposed in [7] tosolve a sum of two convex functions and one of which is linearly composite.This type of problems are typically arisen in sparse signal and imagereconstruction. The proposed SVRG-PDFP can be seen as a generalization ofProx-SVRG [37] originally designed for the minimization of a sum of two convexfunctions. Based on some standard assumptions, we propose two variants, one isfor strongly convex objective function and the other is for general convexcases. Convergence analysis shows that the convergence rate of SVRG-PDFP isO(1/k) (here k is the iteration number) for general convex objective functionand linear for k strongly convex case. Numerical examples on machine learningand CT image reconstruction are provided to show the effectiveness of thealgorithms.

A Stochastic Variance Reduced Primal Dual Fixed Point Method For Linearly Constrained Separable Optimization