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Robert Michael Lewis
Researcher at College of William & Mary
Publications - 39
Citations - 7192
Robert Michael Lewis is an academic researcher from College of William & Mary. The author has contributed to research in topics: Nonlinear programming & Optimization problem. The author has an hindex of 23, co-authored 38 publications receiving 6812 citations. Previous affiliations of Robert Michael Lewis include Memorial University of Newfoundland & University of Texas Southwestern Medical Center.
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Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods ∗
TL;DR: This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited, then turns to a broad class of methods for which the underlying principles allow general-ization to handle bound constraints and linear constraints.
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Direct search methods: then and now
Robert Michael Lewis,Virginia Torczon,Virginia Torczon,Michael W. Trosset,Michael W. Trosset +4 more
TL;DR: Direct search methods are characterized by the absence of the construction of a model of the objective function as discussed by the authors, which is a characteristic of direct search methods for unconstrained optimization, and a modern perspective on this classical family of derivative-free algorithms is given in this paper.
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Problem Formulation for Multidisciplinary Optimization
TL;DR: The “individual discipline feasible” (IDF) approaches introduced here make use of existing specialized analysis codes, and they introduce significant opportunities for coarse-grained computational parallelism particularly well suited to heterogeneous computing environments.
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A Trust Region Framework for Managing the Use of Approximation Models in Optimization
TL;DR: An analytically robust, globally convergent approach to managing the use of approximation models of varying fidelity in optimization, based on the trust region idea from nonlinear programming, which is shown to be provably convergent to a solution of the original high-fidelity problem.
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Pattern Search Algorithms for Bound Constrained Minimization
TL;DR: This work proves global convergence despite the fact that pattern search methods do not have explicit information concerning the gradient and its projection onto the feasible region and consequently are unable to enforce explicitly a notion of sufficient feasible decrease.