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In this paper, we present two variants of DCA (Different of Convex functionsAlgorithm) to solve the constrained sum of differentiable function andcomposite functions minimization problem, with the aim of increasing theconvergence speed of DCA. In the first variant, DCA-Like, we introduce a newtechnique to iteratively modify the decomposition of the objective function.This successive decomposition could lead to a better majorization andconsequently a better convergence speed than the basic DCA. We then incorporatethe Nesterov's acceleration technique into DCA-Like to give rise to the secondvariant, named Accelerated DCA-Like. The convergence properties and theconvergence rate under Kudyka-Lojasiewicz assumption of both variants arerigorously studied. As an application, we investigate our algorithms for thet-distributed stochastic neighbor embedding. Numerical experiments on severalbenchmark datasets illustrate the efficiency of our algorithms.

A DCA-Like Algorithm and its Accelerated Version with Application in Data Visualization