Generalized Householder transformations for the complex symmetric eigenvalue problem

Regular Article

Abstract

We present an intuitive and scalable algorithm for the diagonalization of complex symmetric matrices, which arise from the projection of pseudo-Hermitian and complex scaled Hamiltonians onto a suitable basis set of “trial” states. The algorithm diagonalizes complex and symmetric (non-Hermitian) matrices and is easily implemented in modern computer languages. It is based on generalized Householder transformations and relies on iterative similarity transformations TT′ = Q T T Q, where Q is a complex and orthogonal, but not unitary, matrix, i.e. Q T = Q −1 but Q +Q −1. We present numerical reference data to support the scalability of the algorithm. We construct the generalized Householder transformations from the notion that the conserved scalar product of eigenstates Ψ n and Ψ m of a pseudo-Hermitian quantum mechanical Hamiltonian can be reformulated in terms of the generalized indefinite inner product ∫ dx Ψ n (x, t) Ψ m (x, t), where the integrand is locally defined, and complex conjugation is avoided. A few example calculations are described which illustrate the physical origin of the ideas used in the construction of the algorithm.

Keywords

Machine Accuracy Anharmonic Oscillator Tridiagonal Matrix Generalize Householder Tridiagonal Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Società Italiana di Fisica and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of PhysicsMissouri University of Science and TechnologyRollaUSA
  2. 2.Max-Planck-Institut für QuantenoptikGarchingGermany
  3. 3.MTA-DE Particle Physics Research GroupDebrecenHungary

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