A Lipschitz Matrix for Parameter Reduction in Computational Science
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Publication:4997385
DOI10.1137/20M1364850MaRDI QIDQ4997385
Paul G. Constantine, Jeffrey Hokanson
Publication date: 29 June 2021
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1906.00105
information-based complexityuncertainty quantificationridge functiondesign of computer experimentsparameter reductionLipschitz matrix
Optimal statistical designs (62K05) Analysis of algorithms and problem complexity (68Q25) Special properties of functions of several variables, Hölder conditions, etc. (26B35) Computer science (68-XX)
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- Minimax models in the theory of numerical methods. Transl. from the 1989 Russian orig. by Olga Chuyan
- The design and analysis of computer experiments.
- Manifold learning for parameter reduction
- Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments
- A near-stationary subspace for ridge approximation
- Active Subspaces
- Mini-Minimax Uncertainty Quantification for Emulators
- The university of Florida sparse matrix collection
- Estimating Computational Noise
- Coordination and Geometric Optimization via Distributed Dynamical Systems
- The p-center location problem in an area
- The quickhull algorithm for convex hulls
- ARPACK Users' Guide
- Coffee-House Designs
- Ridge Functions
- An Algorithm for Minimax Approximation in the Nonlinear Case
- Data-Driven Polynomial Ridge Approximation Using Variable Projection
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