A Provably Convergent Multifidelity Optimization Algorithm not Requiring High-Fidelity Derivatives

By Andrew I. March, Karen E. Willcox. 2010

51 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability, Mathematical Optimization

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Citations in the corpus

Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “The most straightforward idea is to decrease the fidelity to trade evaluation time (March and Willcox, 2012; Fernández-Godino et al., 2016).”
Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019, methodology): “One of the most popular method is multi-ﬁdelity optimization (March and Willcox 2012; Fern ´ andez-Godino et al. 2016).”
Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “Multi-fidelity optimization is an optimization technique which focuses on decreasing the evaluation cost by combining a large number of cheap low-fidelity evaluations and a small number of expensive high-fidelity evaluation [38, 79, 50].”
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, unknown): “Examples in the literature include the use of simplified-physics models in design optimization [3, 167], multigrid approaches to optimization of systems governed by differential equations [124, 151], and mesh coarsening for solving a linear inverse problem [18].”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “Multifidelity optimization reduces the runtime of the optimization process by using low-fidelity models to accelerate the search [24, 112, 71, 70] or by using a low-fidelity model in conjunction with adaptive corrections and a trust-region modelmanagement scheme [1, 2, 24, 135, 172].”
Optimal Model Management for Multifidelity Monte Carlo Estimation (2016, methodology): “Multifidelity optimization uses surrogate models to accelerate convergence to an optimal solution [4, 1, 32, 21].”