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

Topological data analysis (TDA) provides a growing body of tools forcomputing geometric and topological information about spaces from a finitesample of points. We present a new adaptive algorithm for finding provablydense samples of points on real algebraic varieties given a set of definingpolynomials. The algorithm utilizes methods from numerical algebraic geometryto give formal guarantees about the density of the sampling and it also employsgeometric heuristics to reduce the size of the sample. As TDA methods consumesignificant computational resources that scale poorly in the number of samplepoints, our sampling minimization makes applying TDA methods more feasible. Weprovide a software package that implements the algorithm and also demonstratethe implementation with several examples.

Sampling real algebraic varieties for topological data analysis