ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Stochastic Approximation Trackers for Model-Based Search

Joseph, AG and Bhatnagar, S (2019) Stochastic Approximation Trackers for Model-Based Search. In: 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019, 24 -27 September 2019, Monticello, IL, USA, USA, pp. 741-748.

[img] PDF
ann_all_con_com-con-com-all_2019.pdf - Published Version
Restricted to Registered users only

Download (614kB) | Request a copy
Official URL: http://dx.doi.org/10.1109/ALLERTON.2019.8919816

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.

Item Type: Conference Paper
Publication: 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
Publisher: IEEE
Additional Information: Copyright of this article belongs to IEEE
Keywords: Benchmarking; Global optimization; Heuristic methods; Stochastic control systems; Stochastic systems, Bench-mark problems; Competitive performance; Derivative-free; Deterministic optimization; High-quality solutions; Model-based search; Sequential algorithm; Stochastic approximations, Stochastic models
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 10 Feb 2020 11:45
Last Modified: 10 Feb 2020 11:45
URI: http://eprints.iisc.ac.in/id/eprint/64454

Actions (login required)

View Item View Item