This research project consists of two major parts: parallelizable fast algorithms for large dense eigenproblems and inexact/inner-outer iteration methods for large sparse eigenproblems. Techniques of rational transformation and matrix-squaring for designing parallelizable fast algorithms for large dense eigenproblems have been developed which only use matrix- matrix multiplications and QR decompositions as building blocks. Now a general theory is to be established to encompass the class of algorithms developed and to also provide guidance to the derivation of new algorithms. Error analysis, robust deflation techniques and stopping criteria which are extremely important for the practical implementation of the algorithms will be fully investigated. This part of the research will result in deeper understanding of the theory of a new class of generalized eigensolvers and deliver a class of algorithms that are truly of high parallel efficiency and robustness. Efficient algorithms for eigenproblems of partial differential equations is the major topic of the second part of this research. Inner-outer iteration type of algorithms will be developed for the subspace iteration and Lanczos algorithms and the insight obtained will be applied to the PDE case. It is demonstrated that different iterative processes behave quite differently with respect to the distribution of the accuracy of each inner iteration, and each iterative process needs a careful analysis in order to find the distribution strategy that will give the minimum number of total inner iteration steps. Two algorithms, the variable-accuracy inner-outer iteration method and the successive inner-outer iteration method will be further analyzed and their convergence behavior explained. A PDE eigenproblem will be solved by putting an iterative process such as the Lanczos method in a Hilbert space setting. At each Lanczos iteration step, the matrix-vector multiplication corresponds to solving a boundary value problem for the differential operator, and different discretizations will be used to enhance efficiency. The research will result in deeper understanding and provide sound theoretical results of various iterative processes for solving large sparse eigenproblems in the setting of inexact matrix-vector multiplication. It will also give more efficient algorithms for solving large sparse eigenproblems that arise in many science and engineering areas.
