Paper Info
Title
Stochastic Approximation Trackers for Model-Based Search
Abstract
In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems.
Year
DOI
Venue
2019
10.1109/ALLERTON.2019.8919816
2019 57TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)
Field
DocType
ISSN
BitTorrent tracker,Mathematical optimization,Global optimization,Computer science,Stochastic process,Heuristics,Linear programming,Probability density function,Stochastic approximation,Manifold
Conference
2474-0195
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ajin George Joseph132.19
Shalabh Bhatnagar280287.78