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 paper, we consider the problem of obtaining the best $k$-sparsesolution of $Ax=y$ subject to the constraint that the columns of $A$ areorthogonal. The naive approach for obtaining a solution to this problem hasexponential complexity and there exist $l_1$ regularization methods such asLasso to obtain approximate solutions. In this paper, we show that we canobtain an exact solution to the problem, with much less computational effortcompared to the brute force search when the columns of $A$ are orthogonal.

Sparse solution of overdetermined linear systems when the columns of $A$ are orthogonal