Sunday, 27 May 2018

Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time. (arXiv:1805.09416v1 [cs.LG])

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle points with at most $O(\log d)$ iterations, which is nearly dimension-free. Further, we show that ASGLD and ASG converge to a local minimum with at most $O(\log d/\epsilon^4)$ iterations. Also, ASGLD with full gradients or ASGLD with a slowly linearly increasing batch size converge to a local minimum with iterations bounded by $O(\log d/\epsilon^2)$, which outperforms existing first-order methods.



from cs updates on arXiv.org https://ift.tt/2J4yoIA
//

Related Posts:

0 comments:

Post a Comment