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Wisniewski, John Aruthur 2014-12-13T18:02:22Z 2014-12-13T18:02:22Z 10000-01-01 1981 1981 This thesis presents an algorithm for solving the large sparse generalized eigenvalue problem Ax = (lamda)Bx. The matrices A and B are assumed to be symmetric, and haphazardly sparse, with B being positive definite. The problem is treated from a constrained optimization approach and an inverse iteration is developed which requires the solution of linear algebraic systems only to the accuracy demanded by a given subspace. The convergence of the method is discussed, and the rate of convergence is improved by using shifting with the Ritz approximations. Numerical results are presented, and aspects concerning an implementation on a parallel computer are discussed. Made available in DSpace on 2014-12-13T18:02:22Z (GMT). No. of bitstreams: 1 8114504.pdf: 3950119 bytes, checksum: ebc4d2dc69822931c89dd31ceff5953f (MD5) Previous issue date: 1981 Embargo set by: Seth Robbins for item 66630 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 145 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1981. http://hdl.handle.net/2142/66452 (UMI)AAI8114504 eng Computer Science On Solving the Large Sparse Generalized Eigenvalue Problem text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.