Trapp, Andrew Christopher (2011) On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability. Doctoral Dissertation, University of Pittsburgh.
Abstract
This thesis consists of three essays concerning the use of optimization techniques to solve four problems in the fields of data mining, computational biology, and sustainable energy devices. To the best of our knowledge, the particular problems we discuss have not been previously addressed using optimization, which is a specific contribution of this dissertation. In particular, we analyze each of the problems to capture their underlying essence, subsequently demonstrating that each problem can be modeled as a nonlinear (mixed) integer program. We then discuss the design and implementation of solution techniques to locate optimal solutions to the aforementioned problems. Running throughout this dissertation is the theme of using mixed-integer programming techniques in conjunction with context-dependent algorithms to identify optimal and previously undiscovered underlying structure.
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Details |
| Item Type: | University of Pittsburgh ETD |
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| ETD Committee: | | ETD Committee Type | Committee Member | Email |
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| Committee Chair | Prokopyev, Oleg A. | prokopyev@engr.pitt.edu | | Committee Member | Schaefer, Andrew J. | schaefer@pitt.edu | | Committee Member | Camacho, Carlos J. | ccamacho@pitt.edu | | Committee Member | Rajgopal, Jayant | rajgopal@engr.pitt.edu | | Committee Member | Vielma, Juan Pablo | jvielma@pitt.edu |
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| Title: | On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability |
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| Status: | Unpublished |
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| Abstract: | This thesis consists of three essays concerning the use of optimization techniques to solve four problems in the fields of data mining, computational biology, and sustainable energy devices. To the best of our knowledge, the particular problems we discuss have not been previously addressed using optimization, which is a specific contribution of this dissertation. In particular, we analyze each of the problems to capture their underlying essence, subsequently demonstrating that each problem can be modeled as a nonlinear (mixed) integer program. We then discuss the design and implementation of solution techniques to locate optimal solutions to the aforementioned problems. Running throughout this dissertation is the theme of using mixed-integer programming techniques in conjunction with context-dependent algorithms to identify optimal and previously undiscovered underlying structure. |
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| Date: | 27 June 2011 |
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| Date Type: | Completion |
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| Defense Date: | 03 March 2011 |
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| Approval Date: | 27 June 2011 |
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| Submission Date: | 22 February 2011 |
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| Access Restriction: | No restriction; Release the ETD for access worldwide immediately. |
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| Patent pending: | No |
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| Institution: | University of Pittsburgh |
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| Thesis Type: | Doctoral Dissertation |
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| Refereed: | Yes |
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| Degree: | PhD - Doctor of Philosophy |
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| URN: | etd-02222011-131629 |
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| Uncontrolled Keywords: | algorithm; computational biology; data mining; integer programming; nonlinear; operations research; optimization; sustainability |
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| Schools and Programs: | Swanson School of Engineering > Industrial Engineering |
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| Date Deposited: | 10 Nov 2011 14:31 |
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| Last Modified: | 22 Feb 2012 13:04 |
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| Other ID: | http://etd.library.pitt.edu/ETD/available/etd-02222011-131629/, etd-02222011-131629 |
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