P
Pritam Ranjan
Researcher at Indian Institute of Management Indore
Publications - 69
Citations - 1173
Pritam Ranjan is an academic researcher from Indian Institute of Management Indore. The author has contributed to research in topics: Gaussian process & Computer experiment. The author has an hindex of 14, co-authored 64 publications receiving 1038 citations. Previous affiliations of Pritam Ranjan include Simon Fraser University & Acadia University.
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Sequential Experiment Design for Contour Estimation From Complex Computer Codes
TL;DR: A sequential methodology for estimating a contour from a complex computer code using a stochastic process model as a surrogate for the computer simulator is developed and applied to exploration of a contours for a network queuing system.
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GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs
TL;DR: This paper implements a slightly modified version of the model proposed by Ranjan et al. (2011) in the R package GPfit, with a novel parameterization of the spatial correlation function and a clustering based multi-start gradient based optimization algorithm that yield robust optimization that is typically faster than the genetic algorithm based approach.
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A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data
TL;DR: In this paper, the authors propose a lower bound on the nugget that minimizes the over-smoothing and an iterative regularization approach to construct a predictor that further improves the inter...
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A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data
TL;DR: A lower bound on the nugget is proposed that minimizes the over-smoothing and an iterative regularization approach to construct a predictor that further improves the interpolation accuracy is proposed.
Journal ArticleDOI
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization
Robert B. Gramacy,Genetha Anne Gray,Sébastien Le Digabel,Herbert K. H. Lee,Pritam Ranjan,Garth N. Wells,Stefan M. Wild +6 more
TL;DR: In this article, a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework is proposed to solve the problem of constrained black-box optimization.