Julia implementation of the `quadeig` algorithm for the solution of quadratic matrix pencils
Author pablosanjose
1 Star
Updated Last
1 Year Ago
Started In
March 2021


The QuadEig package implements the quadeig algorithm to deflate zero and infinite eigenvalues of quadratic pencils. The algorithm is published in:

"An Algorithm for the Complete Solution of Quadratic Eigenvalue Problems" S. Hammarling, C. J. Munro, and F. Tisseur. ACM Trans. Math. Softw. 39, (2013)

Given a quadratic pencil Q(λ) = A₀ + λ A₁ + λ² A₂, where Aᵢ are square matrices of size N, we want to solve the quadratic right-eigenvalue problem Q(λ)φ = 0. A powerful approach is to linearize Q(λ) into an equivalent linear pencil L(λ) = A - λB of size 2N, and solve the generalized eigenvalue problem L(λ)φ´ = (A - λ B)φ´ = 0 instead. There are many possible linearizations. The eigenvectors φ´ of L will be related to the original φ in a way that depends on the chosen linearization.

The quadeig algorithm helps to more efficiently solve the L(λ)φ´ = 0 problem by transforming L(λ) into a smaller L₋(λ) = Q L(λ) V with orthogonal Q and V operators. L₋(λ) shares the same finite eigenvalues as L, but has less (or no) λ = 0 and λ = ∞ eigenvalues which one wants to discard. This process is called "deflation".

The algorithm relies on the specific structure of the so-called second companion linearization, defined by matrices A = [A₁ -I; A₀ 0], B = [-A₂ 0; 0 -I] of size 2N. The right-eigenvectors of the original problem Q are obtained from those of L (deflated or not) by φ = V * φ´[1:N], where V is the deflation transformation on the right. For undeflated linearizations, V is not the identity, because a non-deflating transformation of the second companion linearization is performed for performance reasons.

The QuadEig package exports a linearize function to build L, and a deflate function to transform an L into a deflated L₋. The A, B and V matrices of a linearization l can be accessed by l.A, l.B and l.V, or through destructuring A, B, V = l.


julia> using QuadEig, LinearAlgebra

julia> A₀ = rand(6,6); A₁ = rand(6, 6); A₂ = rand(6, 6);

julia> A₀[:, 1:3] .= A₀[:, 4:6];  # This creates 3 λ = 0 eigenvalues

julia> A₂[:, 2:3] .= A₂[:, 4:5];  # This creates 2 λ = ∞ eigenvalues

julia> l = linearize(A₀, A₁, A₂)
Linearization{T}: second companion linearization of quadratic pencil
  Matrix size    : 20 × 20
  Matrix type    : Matrix{ComplexF64}
  Scalings γ, δ  : (1.0, 1.0)
  Deflated       : false

julia> eigvals(l.A, l.B)  # Note the 3 zero (within machine precision) and 2 infinite (NaN) eigenvalues
12-element Vector{ComplexF64}:
     -10.86932670379268 - 7.26632452718938e-15im
    -0.9585605368704543 - 1.3660371264720934im
    -0.9585605368704528 + 1.3660371264720919im
    -0.4087545396926745 + 0.8719854788559378im
    -0.4087545396926742 - 0.8719854788559386im
 -8.591313021709173e-16 + 0.0im
 -7.180754871717689e-17 + 0.0im
 2.0338957932144944e-17 - 0.0im
    0.32412827713495224 - 4.9149302039555125e-17im
      1.281810969835058 + 7.879489919456524e-16im
                    NaN + NaN*im
                    NaN + NaN*im

julia> d = deflate(l)  # or deflate(A₀, A₁, A₂)
Linearization{T}: second companion linearization of quadratic pencil
  Matrix size    : 7 × 7
  Matrix type    : Matrix{ComplexF64}
  Scalings γ, δ  : (1.0, 1.0)
  Deflated       : true (12 -> 7)

julia> eigvals(d.A, d.B)  # The finite eigenvalues are the same, within machine precision
7-element Vector{ComplexF64}:
  -10.869326703792948 - 4.237043003816544e-20im
  -0.9585605368704537 + 1.366037126472092im
  -0.9585605368704532 - 1.366037126472091im
 -0.40875453969267517 + 0.8719854788559379im
 -0.40875453969267417 - 0.8719854788559379im
  0.32412827713495357 - 0.0im
    1.281810969835057 + 1.0657465373859974e-16im

The deflate function admits an atol keyword argument to specify a threshold for eigenvalues to deflate (|λ| < atol for zeros and |λ| > atol⁻¹ for infinities).

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