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

In this work, we study the problem of finding approximate, with minimumsupport set, solutions to matrix max-plus equations, which we call sparseapproximate solutions. We show how one can obtain such solutions efficientlyand in polynomial time for any $\ell_p$ approximation error. Based on theseresults, we propose a novel method for piecewise-linear fitting of convexmultivariate functions, with optimality guarantees for the model parameters andan approximately minimum number of affine regions.

Sparse Approximate Solutions to Max-Plus Equations with Application to Multivariate Convex Regression