
The hyperbolic quadratic eigenvalue problem (HQEP) was shown to admit Courant–Fischer type min–max principles in 1955 by Duffin and Cauchy type interlacing inequalities in 2010 by Veselić. It can be regarded as the closest analog (among all kinds of quadratic eigenvalue problem) to the standard Hermitian eigenvalue problem (among all kinds of standard eigenvalue problem). In this paper, we conduct a systematic study on the HQEP both theoretically and numerically. On the theoretical front, we generalize Wielandt–Lidskii type min–max principles and, as a special case, Fan type trace min/max principles and establish Weyl type and Wielandt–Lidskii–Mirsky type perturbation results when an HQEP is perturbed to another HQEP. On the numerical front, we justify the natural generalization of the Rayleigh–Ritz procedure with existing principles and our new optimization principles, and, as consequences of these principles, we extend various current optimization approaches—steepest descent/ascent and nonlinear conjugate gradient type methods for the Hermitian eigenvalue problem—to calculate a few extreme eigenvalues (of both positive and negative type). A detailed convergence analysis is given for the steepest descent/ascent methods. The analysis reveals the intrinsic quantities that control convergence rates and consequently yields ways of constructing effective preconditioners. Numerical examples are presented to demonstrate the proposed theory and algorithms.
Numerical computation of eigenvalues and eigenvectors of matrices, 15A18, Eigenvalues, singular values, and eigenvectors, 65F15 (primary), 65F08, 65G99 (secondary), Error analysis and interval analysis, 15A42, Inequalities involving eigenvalues and eigenvectors, Computational methods for sparse matrices, QA1-939, 65F50, Preconditioners for iterative methods, Mathematics
Numerical computation of eigenvalues and eigenvectors of matrices, 15A18, Eigenvalues, singular values, and eigenvectors, 65F15 (primary), 65F08, 65G99 (secondary), Error analysis and interval analysis, 15A42, Inequalities involving eigenvalues and eigenvectors, Computational methods for sparse matrices, QA1-939, 65F50, Preconditioners for iterative methods, Mathematics
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