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Nikolaos Sahinidis Rios, Luis Miguel 2015-09-28T15:37:23Z 2015-09-28T15:37:23Z 10000-01-01 2009 2009 In this thesis, we begin by presenting a comprehensive list of available methods and software and performing an extensive computational study that compares the solvers over a publicly available problem set. Then, we develop Model and Search (M&S), a new local search algorithm for derivative-free optimization. M&S performs a local search from a given point. The search is guided by identifying descent directions from a quadratic model fitted around the best known point, while using information from other evaluated points. We prove that M&S enjoys global convergence to a stationary point. We also propose a new global search algorithm for derivative-free optimization problems, in particular the Branch and Model (B&M) algorithm that is based on modeling the function of interest around each evaluated point by using information from other nearby evaluated points. Algorithm B&M is shown to perform a dense search and thus converge to a global minimum. While oriented towards a global search, B&M relies on the M&S algorithm for occasional local searches. Finally, we present an application of derivative-free solvers, including B&M, to the protein-ligand docking problem. Results show that B&M delivers satisfactory ligand conformations, even outperforming the state-of-the-art protein docking software AutoDock. Made available in DSpace on 2015-09-28T15:37:23Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3363076.pdf: 1736824 bytes, checksum: 95765121499ed12320184835dae7bf44 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 88376 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 120 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/87095 (MiAaPQ)AAI3363076 eng Operations Research Algorithms for Derivative-Free Optimization text Industrial Engineering Industrial Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.