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

 Promsinchai, Porntip and Petrot, Narin 



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.


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 Promsinchai, Porntip, Petrot, Narin