The design of fast algorithms is not only about achieving faster speeds but also about retaining the ability to control the error and numerical stability. This is crucial to the reliability of computed numerical solutions. This dissertation studies topics related to structured matrix computations with an emphasis on their numerical analysis aspects and algorithms. The methods discussed here are all based on rich analytical results that are mathematically justified. In chapter 2, we present a series of comprehensive error analyses to an analytical matrix compression method and it serves as a theoretical explanation of the proxy point method. These results are also important instructions on optimizing the performance. In chapter 3, we propose a non-Hermitian eigensolver by combining HSS matrix techniques with a contour-integral based method. Moreover, probabilistic analysis enables further acceleration of the method in addition to manipulating the HSS representation algebraically. An application of the HSS matrix is discussed in chapter 4 where we design a structured preconditioner for linear systems generated by AIIM. We improve the numerical stability for the matrix-free HSS construction process and make some additional modifications tailored to this particular problem.
|Commitee:||Balakrishnan, Venkataramanan, Gleich, David F., Shen, Jie|
|School Location:||United States -- Indiana|
|Source:||DAI-B 81/3(E), Dissertation Abstracts International|
|Keywords:||Analytical method, Eigenvalue problem, Low-rank approximation, Preconditioner, Structured matrix, Fast algorithms|
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