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Cubically Convergent Iterations for Invariant Subspace Computation

Date

Authors

Absil, P-A

Sepulchre, R

Van Dooren, P

Mahony, Robert

Publisher

Society for Industrial and Applied Mathematics

Abstract

We propose a Newton-like iteration that evolves on the set of fixed dimensional subspaces of ℝn and converges locally cubically to the invariant subspaces of a symmetric matrix. This iteration is compared in terms of numerical cost and global behavior w

Keywords

Keywords: Data coding; Jacobi-Davidson method; Newton equations; Normalization; Algorithms; Computational methods; Data compression; Eigenvalues and eigenfunctions; Heuristic methods; Mathematical operators; Matrix algebra; Optimization; Riccati equations; Set theo Cubic convergence; Global convergence; Grassmann manifold; Invariant subspace; Inverse iteration; Newton method; Rayleigh quotient; Symmetric eigenproblem

URI

http://hdl.handle.net/1885/77992

Collections

Open Access Research

Source

SIAM Journal on Matrix Analysis and Applications

Type

Journal article

DOI

10.1137/S0895479803422002