We introduce a novel Bayesian approach to global optimiza- tion using Gaussian processes. We frame the optimization of both noisy and noiseless functions as sequential decision problems, and introduce myopic and non-myopic solutions to them. Here our solutions can be tai- lored to exactly the degree of confidence we require of them. The use of Gaussian processes allows us to benefit from the incorporation of prior knowledge about our objective function, and also from any derivative observations. Using this latter fact, we introduce an innovative method to combat conditioning problems. Our algorithm demonstrates a signif- icant improvement over its competitors in overall performance across a wide range of canonical test problems.
CITATION STYLE
Osborne, M. A., Garnett, R., & Roberts, S. J. (2009). Gaussian Processes for Global Optimization. In 3rd International Conference on Learning and Intelligent Optimization LION3 (pp. 1–15). Retrieved from http://www.robots.ox.ac.uk/~mosb/OsborneGarnettRobertsGPGO.pdf
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