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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This study investigates the use of continuous-time dynamical systems forsparse signal recovery. The proposed dynamical system is in the form of anonlinear ordinary differential equation (ODE) derived from the gradient flowof the Lasso objective function. The sparse signal recovery process of thisODE-based approach is demonstrated by numerical simulations using the Eulermethod. The state of the continuous-time dynamical system eventually convergesto the equilibrium point corresponding to the minimum of the objectivefunction. To gain insight into the local convergence properties of the system,a linear approximation around the equilibrium point is applied, yielding aclosed-form error evolution ODE. This analysis shows the behavior ofconvergence to the equilibrium point. In addition, a variational optimizationproblem is proposed to optimize a time-dependent regularization parameter inorder to improve both convergence speed and solution quality. The deepunfolded-variational optimization method is introduced as a means of solvingthis optimization problem, and its effectiveness is validated through numericalexperiments.

Ordinary Differential Equation-based Sparse Signal Recovery