Online Optimization Using Zeroth Order Oracles

Iman Shames; Daniel Selvaratnam; Jonathan H. Manton

doi:10.1109/LCSYS.2019.2921593

Online Optimization Using Zeroth Order Oracles

Iman Shames^*
, Daniel Selvaratnam
, Jonathan H. Manton

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

Abstract

This letter considers the iterative numerical optimization of time-varying cost functions where no gradient information is available at each iteration. In this case, the proposed algorithm estimates a directional derivative by finite differences. The main contributions are the derivation of error bounds for such algorithms and proposal of optimal algorithm parameter values, e.g., step-sizes, for strongly convex cost functions. The algorithm is applied to tackle a source localization problem using a sensing agent where the source actively evades the agent. Numerical examples are provided to illustrate the theoretical results.

Original language	English
Article number	8733040
Pages (from-to)	31-36
Number of pages	6
Journal	IEEE Control Systems Letters
Volume	4
Issue number	1
DOIs	https://doi.org/10.1109/LCSYS.2019.2921593
Publication status	Published - Jan 2020
Externally published	Yes

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title = "Online Optimization Using Zeroth Order Oracles",

abstract = "This letter considers the iterative numerical optimization of time-varying cost functions where no gradient information is available at each iteration. In this case, the proposed algorithm estimates a directional derivative by finite differences. The main contributions are the derivation of error bounds for such algorithms and proposal of optimal algorithm parameter values, e.g., step-sizes, for strongly convex cost functions. The algorithm is applied to tackle a source localization problem using a sensing agent where the source actively evades the agent. Numerical examples are provided to illustrate the theoretical results.",

keywords = "Optimization algorithms, autonomous systems, optimization",

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N2 - This letter considers the iterative numerical optimization of time-varying cost functions where no gradient information is available at each iteration. In this case, the proposed algorithm estimates a directional derivative by finite differences. The main contributions are the derivation of error bounds for such algorithms and proposal of optimal algorithm parameter values, e.g., step-sizes, for strongly convex cost functions. The algorithm is applied to tackle a source localization problem using a sensing agent where the source actively evades the agent. Numerical examples are provided to illustrate the theoretical results.

AB - This letter considers the iterative numerical optimization of time-varying cost functions where no gradient information is available at each iteration. In this case, the proposed algorithm estimates a directional derivative by finite differences. The main contributions are the derivation of error bounds for such algorithms and proposal of optimal algorithm parameter values, e.g., step-sizes, for strongly convex cost functions. The algorithm is applied to tackle a source localization problem using a sensing agent where the source actively evades the agent. Numerical examples are provided to illustrate the theoretical results.

KW - Optimization algorithms

KW - autonomous systems

KW - optimization

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Online Optimization Using Zeroth Order Oracles

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