DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

By O. Roustant, David Ginsbourger, Yves Deville. 2012

664 citations recorded by the source corpus.

We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimization, the functionalities of both packages are detailed and demonstrated in two distinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. We then put to the fore the implementation of sequential and parallel optimization strategies relying on the expected improvement criterion on the occasion of DiceOptim’s presentation. An appendix is dedicated to complementary mathematical and computational details.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a2ea5969bdf27bd437cacfbf081b550c220179c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

Simulation optimization: a review of algorithms and applications (2014, neutral): “DiceOptim Global response surface [169] 2012”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, methodology): “Bayesian optimization tune, rBayesianOptimization and DiceOptim (Roustant et al., 2012) are R packages that implement BO with Gaussian processes and for purely numerical search spaces. mlrMBO and its successor mlr3mbo offer to construct a flexible class of BO variants with arbitrary regression…”
A Tutorial on Bayesian Optimization (2018, background): “They are described in detail in Roustant et al. (2012) and are available from CRAN via https://cran.r-project.org/web/packages/DiceOptim/index.html .”
Easy Hyperparameter Search Using Optunity (2014, methodology): “Some packages dedicated to Bayesian approaches include Spearmint (Snoek et al., 2012), DiceKriging (Roustant et al., 2012) and BayesOpt (Martinez-Cantin, 2014).”
The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei (2013, methodology): “The emulation is carried out using the R package DiceKriging (Roustant et al., 2012).”
BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014, methodology): “DiceOptim (Roustant et al., 2012) used R timing system (proc.”
A Review on Global Sensitivity Analysis Methods (2014, neutral): “The technique for estimating the metamodel hyperparam-eters is described in Roustant et al. [70].”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, background): “Detailed calculations and statistical interpretation can be found in Cressie (1993) or Roustant et al. (2012) for instance.”
Polynomial-Chaos-based Kriging (2015, neutral): “The implementation of the Kriging meta-modeling technique can be found in e.g. the Matlab toolbox DACE Lophaven et al. (2002) and the more recent R toolbox DiceKriging Roustant et al. (2012, 2013).”