-

 

 

 




Optimization Online





 

Randomized Derivative-Free Optimization of Noisy Convex Functions

Ruobing Chen(ruobing.chen***at***us.bosch.com)
Stefan Wild(wild***at***mcs.anl.gov)

Abstract: We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise-adjusted smoothing step-sizes that minimize the least-squares error between the true directional derivative of a noisy function and its finite difference approximation. We provide a convergence rate analysis of STARS for solving convex problems with additive or multiplicative noise. Experimental results show that (1) STARS exhibits noise-invariant behavior with respect to different levels of stochastic noise; (2) the practical performance of STARS in terms of solution accuracy and convergence rate is significantly better than that indicated by the theoretical result; and (3) STARS outperforms a selection of randomized zero-order methods on both additive and multiplicative-noisy functions.

Keywords: derivative-free optimization, randomized search, random noisy, convex function

Category 1: Nonlinear Optimization (Unconstrained Optimization )

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 07/13/2015
Entry Accepted: 07/13/2015
Entry Last Modified: 07/13/2015

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society