Bayesian Guided Pattern Search for Robust Local Optimization

Computer experiments: a review

“…Consequently they call the approach the 'treed Gaussian process' model. Taddy et al (2009) coupled the treed GP model with pattern search methods for function optimisation. The treed GP model helps to highlight promising regions of the input space that have not been properly explored by previous function evaluations; pattern search is used to rapidly converge to an optimum near the best evaluation to date.…”

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

“…The new point becomes the current point in the next step of algorithm, if the PS finds out the point in the mesh that improves the objective function at the current point. PS method is very successful for optimization problem such as, Bound constrained minimization and Globaly Convergent Augmented Lagrangian algorithm [24].…”

Variable selection via a multi-stage strategy

“…Previous research relevant to sequential design in computer experiments includes Gramacy and Lee (2009), where data points are sampled in a way to minimize the standard deviation in predicted output or minimize the expected square error averaging over the input space, and Santner et al (2003) and Taddy et al (2009), where sequential sampling is performed by taking additional data points maximizing the expected value of an objective function.…”