Please use this identifier to cite or link to this item: https://doi.org/10.1109/WSC.2012.6465039
Title: Optimization via gradient oriented polar random search
Authors: Li, H.
Lee, L.H. 
Chew, E.P. 
Issue Date: 2012
Source: Li, H.,Lee, L.H.,Chew, E.P. (2012). Optimization via gradient oriented polar random search. Proceedings - Winter Simulation Conference : -. ScholarBank@NUS Repository. https://doi.org/10.1109/WSC.2012.6465039
Abstract: Search algorithms are often used for optimization problems where its mathematical formulation is difficult to be analyzed, e.g., simulation optimization. In literature, search algorithms are either driven by gradient or based on random sampling within specified neighborhood, but both methods have limitation as gradient search can be easily trapped at a local optimum and random sampling loses efficiency by not utilizing local information such as gradient direction that might be available. A combination of the two is believed to overcome both disadvantages. However, the main difficulty is how to incorporate and control randomness in a direction instead of a point. Thus, this paper makes use of a polar coordinate representation in any high dimension to randomly generate directions where the concentration can be explicitly controlled, based on which a brand new Gradient Oriented Polar Random Search (GO-POLARS) is designed and proved to satisfy the conditions for strong local convergence. © 2012 IEEE.
Source Title: Proceedings - Winter Simulation Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72367
ISBN: 9781467347792
ISSN: 08917736
DOI: 10.1109/WSC.2012.6465039
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 13, 2017

Page view(s)

29
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.