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Generalized conflict learning for hybrid discrete/linear optimization

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dc.contributor.advisor Brian C. Williams. en_US
dc.contributor.author Li, Hui (Hui Xylo) en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.date.accessioned 2006-03-29T18:47:01Z
dc.date.available 2006-03-29T18:47:01Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/32466
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005. en_US
dc.description Includes bibliographical references (p. 73-76). en_US
dc.description.abstract Conflict-directed search algorithms have formed the core of practical, model-based reasoning systems for the last three decades. In many of these applications there is a series of discrete constraint optimization problems and a conflict-directed search algorithm, which uses conflicts in the forward search step to focus search away from known infeasibilities and towards the optimal solution. In the arena of model-based autonomy, discrete systems, like deep space probes, have given way to more agile systems, such as coordinated vehicle control, which must robustly control their continuous dynamics. Controlling these systems requires optimizing over continuous, as well as discrete variables, using linear and non-linear as well as logical constraints. This paper explores the development of algorithms for solving hybrid discrete/linear optimization problems that use conflicts in the forward search direction, generalizing from the conflict-directed search algorithms of model-based reasoning. We introduce a novel algorithm called Generalized Conflict-directed Branch and Bound (GCD-BB). GCD-BB extends traditional Branch and Bound (B&B), by first constructing conflicts from nodes of the search tree that are found to be infeasible or sub-optimal, and then by using these conflicts to guide the forward search away from known infeasible and sub-optimal states. We evaluate GCD-BB empirically on a range of test problems of coordinated air vehicle control. GCD-BB demonstrates a substantial improvement in performance compared to a traditional B&B algorithm, applied to either disjunctive linear programs or an equivalent binary integer program encoding. en_US
dc.description.statementofresponsibility by Hui Li. en_US
dc.format.extent 76 p. en_US
dc.format.extent 3569088 bytes
dc.format.extent 3572175 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
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 Aeronautics and Astronautics. en_US
dc.title Generalized conflict learning for hybrid discrete/linear optimization en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.identifier.oclc 61763107 en_US


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