Numerical Methods for Solving Large Sparse Eigenvalue Problems and for the Analysis of Bifurcation Phenomena

Abstract

Research was concerned with designing and analyzing efficient and novel iterative algorithms for solving large sparse linear systems, typically arising from the discretizations of partial differential equations, which are highly parallelizable and converge fast. These include domain decomposition algorithms and multilevel preconditioners. Some basic dense linear algebra problems, including rank-revealing QR factorizations and stable Toeplitz solvers, which have applications to signal processing were considered.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1991
Accession Number
ADA244273

Entities

People

  • Tony F. Chan

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Aspect Ratio
  • California
  • Contracts
  • Decomposition
  • Differential Equations
  • Eigenvalues
  • Equations
  • Linear Systems
  • Mathematical Analysis
  • Mathematics
  • Military Research
  • Partial Differential Equations
  • Real Variables
  • Signal Processing

Fields of Study

  • Mathematics

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Linear Algebra