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

In this paper, we consider high-dimensional nonconvex square-root-lossregression problems and introduce a proximal majorization-minimization (PMM)algorithm for these problems. Our key idea for making the proposed PMM to beefficient is to develop a sparse semismooth Newton method to solve thecorresponding subproblems. By using the Kurdyka-{\L}ojasiewicz propertyexhibited in the underlining problems, we prove that the PMM algorithmconverges to a d-stationary point. We also analyze the oracle property of theinitial subproblem used in our algorithm. Extensive numerical experiments arepresented to demonstrate the high efficiency of the proposed PMM algorithm.

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems