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.
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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 |
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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 |
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