| dc.identifier.uri | http://hdl.handle.net/1951/60286 | |
| dc.identifier.uri | http://hdl.handle.net/11401/71513 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | This thesis studies a class of model-based randomized algorithms for solving general optimization problems. These are iterative algorithms that sample from and update an underlying distribution over the feasible solution space. We find that the model-based algorithms can be interpreted as the well-known stochastic approximation (SA) method. Following the connection between model-based algorithms and SA, we build a framwork to analyze the convergence and the convergence rate of these algorithms. Moreover, we present an instantiation of this framework which is the modified version of the Cross Entropy (CE) method, and analyze its convergence properties and numerical performance. In addition, we also propose a novel random search algorithm called Model-based Annealing Random Search (MARS). By exploiting its connection to SA we provide its global convergence result and analyze the asymptotic convergence rate as well. Finally, the empirical results of MARS show promising performance in comparison with some other existing methods. | |
| dcterms.available | 2013-05-24T16:38:21Z | |
| dcterms.available | 2015-04-24T14:47:47Z | |
| dcterms.contributor | Hu, Jiaqiao | en_US |
| dcterms.contributor | Feinberg, Eugene | en_US |
| dcterms.contributor | Arkin, Estie | en_US |
| dcterms.contributor | Djuric, petar | en_US |
| dcterms.creator | Hu, Ping | |
| dcterms.dateAccepted | 2013-05-24T16:38:21Z | |
| dcterms.dateAccepted | 2015-04-24T14:47:47Z | |
| dcterms.dateSubmitted | 2013-05-24T16:38:21Z | |
| dcterms.dateSubmitted | 2015-04-24T14:47:47Z | |
| dcterms.description | Department of Applied Mathematics and Statistics | en_US |
| dcterms.extent | 144 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/1951/60286 | |
| dcterms.identifier | http://hdl.handle.net/11401/71513 | |
| dcterms.issued | 2012-05-01 | |
| dcterms.language | en_US | |
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Previous issue date: 1 | en |
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Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Operations research | |
| dcterms.title | A Stochastic Approximation Interpretation for Model-based Optimization Algorithms | |
| dcterms.type | Dissertation | |
