Faster Kriging: Facing High-Dimensional Simulators

Xuefei Lu (), Alessandro Rudi (), Emanuele Borgonovo () and Lorenzo Rosasco ()
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Xuefei Lu: Department of Decision Sciences, Bocconi University, 20136 Milan, Italy
Alessandro Rudi: The National Institute for Research in Computer Science and Automation (INRIA), École Normale Supérieure, Paris, France, 75012
Emanuele Borgonovo: Bocconi Institute for Data Science and Analytics (BIDSA), 20136 Milan, Italy, Department of Decision Sciences, Bocconi University, 20136 Milan, Italy
Lorenzo Rosasco: Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Università degli Studi di Genova, 16145 Genova, Italy, Laboratory for Computational and Statistical Learning (LCSL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, Istituto Italiano di Tecnologia, 16163 Genova, Italy

Operations Research, 2020, vol. 68, issue 1, 233-249

Abstract: Kriging is one of the most widely used emulation methods in simulation. However, memory and time requirements potentially hinder its application to data sets generated by high-dimensional simulators. We borrow from the machine learning literature to propose a new algorithmic implementation of kriging that, while preserving prediction accuracy, notably reduces time and memory requirements. The theoretical and computational foundations of the algorithm are provided. The work then reports results of extensive numerical experiments to compare the performance of the proposed algorithm against current kriging implementations, on simulators of increasing dimensionality. Findings show notable savings in time and memory requirements that allow one to handle inputs across more that 10,000 dimensions.

Keywords: simulation; kriging; metamodeling; machine learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:68:y:2020:i:1:p:233-249

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