Randomization for Efficient Nonlinear Parametric Inversion.
Degree: PhD, Mathematics, 2018, Virginia Tech
Nonlinear parametric inverse problems appear in many applications in science and engineering. We focus on diffuse optical tomography (DOT) in medical imaging. DOT aims to recover an unknown image of interest, such as the absorption coefficient in tissue to locate tumors in the body. Using a mathematical (forward) model to predict measurements given a parametrization of the tissue, we minimize the misfit between predicted and actual measurements up to a given noise level. The main computational bottleneck in such inverse problems is the repeated evaluation of this large-scale forward model, which corresponds to solving large linear systems for each source and frequency at each optimization step. Moreover, to efficiently compute derivative information, we need to solve, repeatedly, linear systems with the adjoint for each detector and frequency. As rapid advances in technology allow for large numbers of sources and detectors, these problems become computationally prohibitive. In this thesis, we introduce two methods to drastically reduce this cost.
To efficiently implement Newton methods, we extend the use of simultaneous random sources to reduce the number of linear system solves to
include simultaneous random detectors. Moreover, we combine simultaneous random sources and detectors with optimized ones that lead to faster convergence and more accurate solutions.
We can use reduced order models (ROM) to drastically reduce the size of the linear systems to be solved in each optimization step while still solving the inverse problem accurately. However, the construction of the ROM bases still incurs a substantial cost. We propose to use randomization to drastically reduce the number of large linear solves needed for constructing the global ROM bases without degrading the accuracy of the solution to the inversion problem.
We demonstrate the efficiency of these approaches with 2-dimensional and 3-dimensional examples from DOT; however, our methods have the potential to be useful for other applications as well.
Advisors/Committee Members: De Sturler, Eric (committeechair), Kilmer, Misha (committee member), Chung, Matthias (committee member), Beattie, Christopher A. (committee member), Gugercin, Serkan (committee member).
Subjects/Keywords: DOT; PaLS; stochastic programming; randomization; inverse problems; optimization; model order reduction
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APA (6th Edition):
Sariaydin, S. (2018). Randomization for Efficient Nonlinear Parametric Inversion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83451
Chicago Manual of Style (16th Edition):
Sariaydin, Selin. “Randomization for Efficient Nonlinear Parametric Inversion.” 2018. Doctoral Dissertation, Virginia Tech. Accessed September 16, 2019.
MLA Handbook (7th Edition):
Sariaydin, Selin. “Randomization for Efficient Nonlinear Parametric Inversion.” 2018. Web. 16 Sep 2019.
Sariaydin S. Randomization for Efficient Nonlinear Parametric Inversion. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Sep 16].
Available from: http://hdl.handle.net/10919/83451.
Council of Science Editors:
Sariaydin S. Randomization for Efficient Nonlinear Parametric Inversion. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83451