Stochastic DCA with Variance Reduction and Applications in Machine Learning

We design stochastic Difference-of-Convex-functions Algorithms (DCA) for solving a class of structured Difference-of-Convex-functions (DC) problems. As the standard DCA requires the full information of (sub)gradients which could be expensive in large-scale settings, stochastic approaches rely upon stochastic information instead. However, stochastic estimations generate additional variance terms making stochastic algorithms unstable. Therefore, we integrate some novel variance reduction techniques including SVRG and SAGA into our design. The almost sure convergence to critical points of the proposed algorithms is established and the …

applications learning machine machine learning stochastic variance