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

Consider the linear equation $\mathbf{A}\mathbf{x}=\mathbf{y}$, where$\mathbf{A}$ is a $k\times N$-matrix, $\mathbf{x}\in\mathcal{K}\subset\mathbb{R}^N$ and $\mathbf{y}\in\mathbb{R}^M$ a given vector. When$\mathcal{K}$ is a convex set and $M\not= N$ this is a typical ill-posed,linear inverse problem with convex constraints. Here we propose a new way tosolve this problem when $\mathcal{K} = \prod_j[a_j,b_j]$. It consists ofregarding $\mathbf{A}\mathbf{x}=\mathbf{y}$ as the constraint of a convexminimization problem, in which the objective (cost) function is the dual of amoment generating function. This leads to a nice minimization problem and someinteresting comparison results. More importantly, the method provides asolution that lies in the interior of the constraint set $\mathcal{K}$. We alsoanalyze the dependence of the solution on the data and relate it to the LeChatellier principle.

Ill-posed linear inverse problems with box constraints: A new convex optimization approach