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 study the problem of estimating precision matrices in Gaussiandistributions that are multivariate totally positive of order two($\mathrm{MTP}_2$). The precision matrix in such a distribution is an M-matrix.This problem can be formulated as a sign-constrained log-determinant program.Current algorithms are designed using the block coordinate descent method orthe proximal point algorithm, which becomes computationally challenging inhigh-dimensional cases due to the requirement to solve numerous nonnegativequadratic programs or large-scale linear systems. To address this issue, wepropose a novel algorithm based on the two-metric projection method,incorporating a carefully designed search direction and variable partitioningscheme. Our algorithm substantially reduces computational complexity, and itstheoretical convergence is established. Experimental results on synthetic andreal-world datasets demonstrate that our proposed algorithm provides asignificant improvement in computational efficiency compared to thestate-of-the-art methods.

Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity