Online Stochastic DCA with applications to Principal Component Analysis

Understand

Stochastic algorithms are well-known for their performance in the era of big data.

• In convex optimization, stochastic algorithms have been studied in depth and breadth.
• However, the current body of research on stochastic algorithms for nonsmooth, nonconvex optimization is relatively limited.
• In this paper, we propose new stochastic algorithms based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) - the backbone of nonconvex, nonsmooth optimization.