2 citations · 8 across the 10 of their papers we have counts for
6 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…
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…
Minima distribution for global optimization
Xiaopeng Luo
This paper establishes a strict mathematical relationship between an arbitrary continuous function on a compact set and its global minima, like the well-known first order optimalit…