Pitt Logo LinkContact Us

On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability

Trapp, Andrew Christopher (2011) On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability. Doctoral Dissertation, University of Pittsburgh.

[img]
Preview
PDF - Primary Text
Download (3432Kb) | Preview

    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.


    Share

    Citation/Export:
    Social Networking:

    Details

    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmail
    Committee ChairProkopyev, Oleg A.prokopyev@engr.pitt.edu
    Committee MemberSchaefer, Andrew J.schaefer@pitt.edu
    Committee MemberCamacho, Carlos J.ccamacho@pitt.edu
    Committee MemberRajgopal, Jayantrajgopal@engr.pitt.edu
    Committee MemberVielma, Juan Pablojvielma@pitt.edu
    Title: On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability
    Status: Unpublished
    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.
    Date: 27 June 2011
    Date Type: Completion
    Defense Date: 03 March 2011
    Approval Date: 27 June 2011
    Submission Date: 22 February 2011
    Access Restriction: No restriction; The work is available for access worldwide immediately.
    Patent pending: No
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Degree: PhD - Doctor of Philosophy
    URN: etd-02222011-131629
    Uncontrolled Keywords: algorithm; computational biology; data mining; integer programming; nonlinear; operations research; optimization; sustainability
    Schools and Programs: Swanson School of Engineering > Industrial Engineering
    Date Deposited: 10 Nov 2011 14:31
    Last Modified: 22 Feb 2012 13:04
    Other ID: http://etd.library.pitt.edu/ETD/available/etd-02222011-131629/, etd-02222011-131629

    Actions (login required)

    View Item

    Document Downloads