Title
A Smoothing Direct Search Method for Monte Carlo-Based Bound Constrained Composite Nonsmooth Optimization
Abstract
We propose and analyze a smoothing direct search algorithm for finding a minimizer of a nonsmooth nonconvex function over a box constraint set, where the objective function values cannot be computed directly but are approximated by Monte Carlo simulation. In the algorithm, we adjust the stencil size, the sample size, and the smoothing parameter simultaneously so that the stencil size goes to zero faster than the smoothing parameter and the square root of the sample size goes to infinity faster than the reciprocal of the stencil size. We prove that with probability one any accumulation point of the sequence generated by the algorithm is a Clarke stationary point. We report on numerical results from statistics and financial applications.
Year
DOI
Venue
2018
10.1137/17M1116714
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
Field
DocType
sampling methods,direct search algorithm,Monte Carlo simulation,nonsmooth optimization,smoothing functions,Clarke stationarity
Monte Carlo method,Mathematical optimization,Direct search,Composite number,Smoothing,Sampling (statistics),Mathematics
Journal
Volume
Issue
ISSN
40
4
1064-8275
Citations 
PageRank 
References 
0
0.34
12
Authors
4
Name
Order
Citations
PageRank
Xiaojun Chen11298107.51
Carl T. Kelley216223.55
Fengmin Xu31209.89
Zaikun Zhang4141.65