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

Significant attention has been given to minimizing a penalized least squarescriterion for estimating sparse solutions to large linear systems of equations.The penalty is responsible for inducing sparsity and the natural choice is theso-called $l_0$ norm. In this paper we develop a Momentumized IterativeShrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convexcriterion and prove its convergence to a local minimizer. Simulations on largedata sets show superior performance of the proposed method to other methods.

MIST: L0 Sparse Linear Regression with Momentum