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What is the SURROGATES toolbox?

The SURROGATES toolbox is a general-purpose library of multidimensional function approximation and optimization methods for MATLAB and OCTAVE. The current version includes, among others, the following capabilities:

• • Surrogates: Gaussian process, kriging, polynomial response surface, radial basis neural network, and linear Shepard.

  • Classical error analysis.

  • Variants of the efficient global optimization (EGO) algorithm

The SURROGATES toolbox uses the following collection of third party software:

• • GPML by Rasmussen and Williams [1]: www.gaussianprocess.org/gpml

  • DACE by Lophaven et al. [2]: www2.imm.dtu.dk/~hbn/dace

  • RBF by Jekabsons [3]: www.cs.rtu.lv/jekabsons/regression

  • SVM by Gunn [4]: www.isis.ecs.soton.ac.uk/resources/svminfo

Returning the favor

I strongly ask the user's community to give credit to the individual components and to the SURROGATES toolbox in any publication derived from the use of the toolbox. For example, when I publish my papers I usually have a paragraph like this:

Table 1 details the different surrogates used during this investigation. The SURROGATES toolbox was also used for easy manipulation of the surrogates.

Table 1: Setup for the set of used surrogates. The GPML[1], DACE [2], MATLAB neural networks [5], RBF [3], SURROGATES [7], and SVM [4] toolboxes were used to run the Gaussian process, kriging, radial basis neural network, radial basis function, linear Shepard algorithms, and support vector regression algorithms, respectively.

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Version 3.0

References

[1] CE Rasmussen and CK Williams, Gaussian Processes for Machine Learning, The MIT Press, 2006.

[2] SN Lophaven, HB Nielsen, and J Sondergaard, "DACE - a MATLAB kriging toolbox," Tech. Rep. IMM-TR-2002-12, Technical University of Denmark, Denmark, Aug 2002, available at http://www2.imm.dtu.dk/~hbn/dace/.

[3] G Jekabsons, RBF: Radial Basis Function interpolation for MATLAB/OCTAVE, Riga Technical University, Latvia, version 1.1 ed., 2009, available at http://www.cs.rtu.lv/jekabsons/regression.html.

[4] SR Gunn, "Support vector machines for classification and regression," Tech. Rep., University of Southampton, UK, 1997, available at http://www.isis.ecs.soton.ac.uk/resources/svminfo/.

[5] MathWorks contributors, MATLAB The language of technical computing, The MathWorks, Inc, Natick, MA, USA, version 7.0 release 14 ed., 2004.

[6] WI Thacker, J Zhang, LT Watson, JB Birch, MA Iyer, and MW Berry, "Algorithm 905: SHEPPACK: modified Shepard algorithm for interpolation of scattered multivariate data," ACM Transactions on Mathematical Software, Vol. 37, No. 3, 2010, pp. 1-20.

[7] FAC Viana, SURROGATES Toolbox User's Guide, Gainesville, FL, USA, version 3.0 ed., 2011, available at https://sites.google.com/site/srgtstoolbox/.