Numerical experiments on stochastic block proximal-gradient type method for convex constrained optimization involving coordinatewise separable problems


 Promsinchai, Porntip and Petrot, Narin 


Abstract

carpathian_2019_35_3_371_378_abstract

In this paper, we consider convex constrained optimization problems with composite objective functions over the set of a minimizer of another function. The main aim is to test numerically a new algorithm, namely a stochastic block coordinate proximal-gradient algorithm with penalization, by comparing both the number of iterations and CPU times between this introduced algorithm and the other well-known types of block coordinate descent algorithm for finding solutions of the randomly generated optimization problems with a \ell_{1}-regularization term.

 

Additional Information

Author(s)

 Promsinchai, Porntip, Petrot, Narin