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Nonconvex robust optimization

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dc.contributor.advisor Dimitris J. Bertsimas. en_US
dc.contributor.author Teo, Kwong Meng en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US
dc.date.accessioned 2008-02-27T20:36:39Z
dc.date.available 2008-02-27T20:36:39Z
dc.date.copyright 2007 en_US
dc.date.issued 2007 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/40303
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Includes bibliographical references (p. 133-138). en_US
dc.description.abstract We propose a novel robust optimization technique, which is applicable to nonconvex and simulation-based problems. Robust optimization finds decisions with the best worst-case performance under uncertainty. If constraints are present, decisions should also be feasible under perturbations. In the real-world, many problems are nonconvex and involve computer-based simulations. In these applications, the relationship between decision and outcome is not defined through algebraic functions. Instead, that relationship is embedded within complex numerical models. Since current robust optimization methods are limited to explicitly given convex problems, they cannot be applied to many practical problems. Our proposed method, however, operates on arbitrary objective functions. Thus, it is generic and applicable to most real-world problems. It iteratively moves along descent directions for the robust problem, and terminates at a robust local minimum. Because the concepts of descent directions and local minima form the building blocks of powerful optimization techniques, our proposed framework shares the same potential, but for the richer, and more realistic, robust problem. en_US
dc.description.abstract (cont.) To admit additional considerations including parameter uncertainties and nonconvex constraints, we generalized the basic robust local search. In each case, only minor modifications are required - a testimony to the generic nature of the method, and its potential to be a component of future robust optimization techniques. We demonstrated the practicability of the robust local search technique in two realworld applications: nanophotonic design and Intensity Modulated Radiation Therapy (IMRT) for cancer treatment. In both cases, the numerical models are verified by actual experiments. The method significantly improved the robustness for both designs, showcasing the relevance of robust optimization to real-world problems. en_US
dc.description.statementofresponsibility by Kwong Meng Teo. en_US
dc.format.extent 138 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Operations Research Center. en_US
dc.title Nonconvex robust optimization en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 191109737 en_US


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