A stochastically evolving non-local search and solutions to inverse problems with sparse data

Venugopal, Mamatha and Vasu, Ram Mohan and Roy, Debasish (2016) A stochastically evolving non-local search and solutions to inverse problems with sparse data. In: PROBABILISTIC ENGINEERING MECHANICS, 46 . pp. 37-47.

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Abstract

Building on a martingale approach to global optimization, a powerful stochastic search scheme for the global optimum of cost functions is proposed using change of measures on the states that evolve as diffusion processes and splitting of the state-space along the lines of a Bayesian game. To begin with, the efficacy of the optimizer, when contrasted with one of the most efficient existing schemes, is assessed against a family of No-hard benchmark problems. Then, using both simulated and experimental data, potentialities of the new proposal are further explored in the context of an inverse problem of significance in photoacoustic imaging, wherein the superior''reconstruction features of a global search vis-a-vis the commonly adopted local or quasi-local schemes are brought into relief. (C) 2016 Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	PROBABILISTIC ENGINEERING MECHANICS
Additional Information:	Copy right for this article belongs to the ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	31 Jan 2017 05:31
Last Modified:	31 Jan 2017 05:31
URI:	http://eprints.iisc.ac.in/id/eprint/56133

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