2 citations · 8 across the 10 of their papers we have counts for
10 papers
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…
Contraction methods for continuous optimization
Xiaopeng Luo, Xin Xu
Motivated by the grid search method and Bayesian optimization, we introduce the concept of contractibility and its applications in model-based optimization. First, a basic framewor…