21,659 research outputs found
Computation of multiple eigenvalues and generalized eigenvectors for matrices dependent on parameters
The paper develops Newton's method of finding multiple eigenvalues with one
Jordan block and corresponding generalized eigenvectors for matrices dependent
on parameters. It computes the nearest value of a parameter vector with a
matrix having a multiple eigenvalue of given multiplicity. The method also
works in the whole matrix space (in the absence of parameters). The approach is
based on the versal deformation theory for matrices. Numerical examples are
given. The implementation of the method in MATLAB code is available.Comment: 19 pages, 3 figure
A numerical method to compute derivatives of functions of large complex matrices and its application to the overlap Dirac operator at finite chemical potential
We present a method for the numerical calculation of derivatives of functions
of general complex matrices. The method can be used in combination with any
algorithm that evaluates or approximates the desired matrix function, in
particular with implicit Krylov-Ritz-type approximations. An important use case
for the method is the evaluation of the overlap Dirac operator in lattice
Quantum Chromodynamics (QCD) at finite chemical potential, which requires the
application of the sign function of a non-Hermitian matrix to some source
vector. While the sign function of non-Hermitian matrices in practice cannot be
efficiently approximated with source-independent polynomials or rational
functions, sufficiently good approximating polynomials can still be constructed
for each particular source vector. Our method allows for an efficient
calculation of the derivatives of such implicit approximations with respect to
the gauge field or other external parameters, which is necessary for the
calculation of conserved lattice currents or the fermionic force in Hybrid
Monte-Carlo or Langevin simulations. We also give an explicit deflation
prescription for the case when one knows several eigenvalues and eigenvectors
of the matrix being the argument of the differentiated function. We test the
method for the two-sided Lanczos approximation of the finite-density overlap
Dirac operator on realistic gauge field configurations on lattices with
sizes as large as and .Comment: 26 pages elsarticle style, 5 figures minor text changes, journal
versio
An integral method for solving nonlinear eigenvalue problems
We propose a numerical method for computing all eigenvalues (and the
corresponding eigenvectors) of a nonlinear holomorphic eigenvalue problem that
lie within a given contour in the complex plane. The method uses complex
integrals of the resolvent operator, applied to at least column vectors,
where is the number of eigenvalues inside the contour. The theorem of
Keldysh is employed to show that the original nonlinear eigenvalue problem
reduces to a linear eigenvalue problem of dimension .
No initial approximations of eigenvalues and eigenvectors are needed. The
method is particularly suitable for moderately large eigenvalue problems where
is much smaller than the matrix dimension. We also give an extension of the
method to the case where is larger than the matrix dimension. The
quadrature errors caused by the trapezoid sum are discussed for the case of
analytic closed contours. Using well known techniques it is shown that the
error decays exponentially with an exponent given by the product of the number
of quadrature points and the minimal distance of the eigenvalues to the
contour
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