A framework for optimization under limited information

Tansu Alpcan ()

Journal of Global Optimization, 2013, vol. 55, issue 3, 706 pages

Abstract: In many real world problems, optimisation decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of data points. The scarcity of data may be due to high cost of observation or fast-changing nature of the underlying system. This paper presents a “black-box” optimisation framework that takes into account the information collection, estimation, and optimisation aspects in a holistic and structured manner. Explicitly quantifying the observations at each optimisation step using the entropy measure from information theory, the often nonconvex-objective function to be optimised is modelled and estimated by adopting a Bayesian approach and using Gaussian processes as a state-of-the-art regression method. The resulting iterative scheme allows the decision maker to address the problem by expressing preferences for each aspect quantitatively and concurrently. Copyright Springer Science+Business Media, LLC. 2013

Keywords: Limited information; Active learning; Multi-objective optimization (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-012-9942-z

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