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 Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The tensor rank decomposition problem consists of recovering the unique setof parameters representing a robustly identifiable low-rank tensor when thecoordinate representation of the tensor is presented as input. A conditionnumber for this problem measuring the sensitivity of the parameters to aninfinitesimal change to the tensor is introduced and analyzed. It isdemonstrated that the absolute condition number coincides with the inverse ofthe least singular value of Terracini's matrix. Several basic properties ofthis condition number are investigated.

A condition number for the tensor rank decomposition