Homotopy Optimization Methods and Protein Structure Prediction

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dc.contributor.advisorO'Leary, Dianne Pen_US
dc.contributor.authorDunlavy, Daniel Michaelen_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2005-10-11T10:07:59Z
dc.date.available2005-10-11T10:07:59Z
dc.date.issued2005-07-22en_US
dc.description.abstractThe focus of this dissertation is a new method for solving unconstrained minimization problems---<i>homotopy optimization using perturbations and ensembles</i> (HOPE). HOPE is a homotopy optimization method that finds a sequence of minimizers of a homotopy function that maps a template function to the target function, the function from our minimization problem. To increase the likelihood of finding a global minimizer, points in the sequence are perturbed and used as starting points to find other minimizers. Points in the resulting ensemble of minimizers are used as starting points to find minimizers of the homotopy function as it deforms the template function into the target function. We show that certain choices of the parameters used in HOPE lead to instances of existing methods: probability-one homotopy methods, stochastic search methods, and simulated annealing. We use these relations and further analysis to demonstrate the convergence properties of HOPE. The development of HOPE was motivated by the protein folding problem, the problem of predicting the structure of a protein as it exists in nature, given its amino acid sequence. However, we demonstrate that HOPE is also successful as a general purpose minimization method for nonconvex functions. Numerical experiments performed to test HOPE include solving several standard test problems and the protein folding problem using two different protein models. In the first model, proteins are modeled as chains of charged particles in two dimensions. The second is a backbone protein model, where the particles represent amino acids, each corresponding to a hydrophobic, hydrophilic, or neutral residue. In most of these experiments, standard homotopy functions are used in HOPE. Additionally, several new homotopy functions are introduced for solving the protein folding problems to demonstrate how HOPE can be used to exploit the properties or structure of particular problems. Results of experiments demonstrate that HOPE outperforms several methods often used for solving unconstrained minimization problems---a quasi-Newton method with BFGS Hessian update, a globally convergent variant of Newton's method, and ensemble-based simulated annealing.en_US
dc.format.extent6452063 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/2882
dc.language.isoen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledBiology, Molecularen_US
dc.subject.pquncontrolledhomotopy methoden_US
dc.subject.pquncontrolledglobal optimizationen_US
dc.subject.pquncontrolledstochastic methoden_US
dc.subject.pquncontrolledprotein structure predictionen_US
dc.subject.pquncontrolledenergy minimizationen_US
dc.subject.pquncontrolledenergy deformationen_US
dc.titleHomotopy Optimization Methods and Protein Structure Predictionen_US
dc.typeDissertationen_US

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