NDLI: Gradient-Based Adaptive Stochastic Search for Non-Differentiable Optimization

Content Provider	IEEE Xplore Digital Library
Author	Enlu Zhou Jiaqiao Hu
Copyright Year	1963
Abstract	In this paper, we propose a stochastic search algorithm for solving general optimization problems with little structure. The algorithm iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized distribution model over the solution space. The basic idea is to convert the original (possibly non-differentiable) problem into a differentiable optimization problem on the parameter space of the parameterized sampling distribution, and then use a direct gradient search method to find improved sampling distributions. Thus, the algorithm combines the robustness feature of stochastic search from considering a population of candidate solutions with the relative fast convergence speed of classical gradient methods by exploiting local differentiable structures. We analyze the convergence and converge rate properties of the proposed algorithm, and carry out numerical study to illustrate its performance.
Sponsorship	IEEE Control Systems Society
Starting Page	1818
Ending Page	1832
Page Count	15
File Size	713730
File Format	PDF
ISSN	00189286
Volume Number	59
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Stochastic processes Convergence Approximation algorithms Reactive power Approximation methods Aerospace electronics black-box optimization Stochastic approximation stochastic search
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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