2 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
Derivative-free global minimization for a class of multiple minima problems
Xiaopeng Luo, Xin Xu, Daoyi Dong
We prove that the finite-difference based derivative-free descent (FD-DFD) methods have a capability to find the global minima for a class of multiple minima problems. Our main res…
A New Accelerated Stochastic Gradient Method with Momentum
Liang Liu, Xiaopeng Luo
In this paper, we propose a novel accelerated stochastic gradient method with momentum, which momentum is the weighted average of previous gradients. The weights decays inverse pro…
Asymptotic proximal point methods: finding the global minima with linear convergence for a class of multiple minima problems
Xiaopeng Luo, Xin Xu, Herschel A. Rabitz
We propose and analyze asymptotic proximal point (APP) methods to find the global minimizer for a class of nonconvex, nonsmooth, or even discontinuous multiple minima functions. Th…
Can speed up the convergence rate of stochastic gradient methods to by a gradient averaging strategy?
Xin Xu, Xiaopeng Luo
In this paper we consider the question of whether it is possible to apply a gradient averaging strategy to improve on the sublinear convergence rates without any increase in storag…
Stochastic gradient-free descents
Xiaopeng Luo, Xin Xu
In this paper we propose stochastic gradient-free methods and accelerated methods with momentum for solving stochastic optimization problems. All these methods rely on stochastic d…