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 Plus

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

Zendy Tools

Zendy Open

We introduce a randomized algorithm for computing the minimal-norm solutionto an underdetermined system of linear equations. Given an arbitrary full-rankm x n matrix A with m<n, any m x 1 vector b, and any positive real numberepsilon less than 1, the procedure computes an n x 1 vector x approximating torelative precision epsilon or better the n x 1 vector p of minimal Euclideannorm satisfying Ap=b. The algorithm typically requires O(mnlog(sqrt(n)/epsilon) + m**3) floating-point operations, generally less than theO(m**2 n) required by the classical schemes based on QR-decompositions orbidiagonalization. We present several numerical examples illustrating theperformance of the algorithm.

A fast algorithm for computing minimal-norm solutions to underdetermined systems of linear equations