Minimax and Minimax Projection Designs Using Clustering

DOI10.1080/10618600.2017.1302881OpenAlexW2547357791MaRDI QIDQ123949

V. Roshan Joseph, Simon Mak, V. Roshan Joseph, Simon Mak

Publication date: 11 July 2017

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.03938

zbMATH Keywords

computer experimentsexperimental design\(k\)-means clusteringparticle swarm optimizationaccelerated gradient descent

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Space-filling experimental designs for constrained design spaces ⋮ Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces ⋮ APIK: Active Physics-Informed Kriging Model with Partial Differential Equations ⋮ Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework ⋮ Two‐dimensional projection uniformity for space‐filling designs ⋮ Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy ⋮ Bayesian Quadrature, Energy Minimization, and Space-Filling Design ⋮ Support points ⋮ A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs ⋮ minimaxdesign ⋮ cmenet: A New Method for Bi-Level Variable Selection of Conditional Main Effects ⋮ An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations ⋮ Maximum expected entropy transformed Latin hypercube designs

Uses Software

• randtoolbox
• nloptr
• minimaxdesign
• k-means++

Cites Work

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• Optimal sensor networks scheduling in identification of distributed parameter systems
• Two-dimensional minimax Latin hypercube designs
• Minimax distance designs in two-level factorial experiments
• Lectures on Modern Convex Optimization
• Principal points
• Note on Grouping
• On Equitable Resource Allocation Problems: A Lexicographic Minimax Approach
• Least squares quantization in PCM
• Estimation of Principal Points
• k-Means Has Polynomial Smoothed Complexity
• Maximum projection designs for computer experiments
• Fast hierarchical clustering and other applications of dynamic closest pairs
• On the distribution of points in a cube and the approximate evaluation of integrals

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