Recent Advances in the Numerical Solution of Quadratic ...ftisseur/talks/talk_ala09.pdf · Appears...
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Recent Advances in the
Numerical Solution of
Quadratic Eigenvalue Problems
Françoise Tisseur
School of Mathematics
The University of Manchester
http://www.ma.man.ac.uk/~ftisseur/
SIAM Conference on Applied Linear Algebra
October 2009
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Nonlinear Eigenproblems
Let F : Ω → Cn×n be analytic on open set Ω ⊆ C.
The nonlinear eigenvalue problem: Find scalars λ and
nonzero x , y ∈ Cn satisfying F (λ)x = 0 and y∗F (λ) = 0.
λ is an e’val, x , y are corresponding right and left e’vecs.
E’vals are solutions of det(F (λ)) = 0.
In practice, elements of F most often polynomial, rational or
exponential functions of λ.
Can be very difficult to solve (poor conditioning, algebraic
structure to be preserved).
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NLEVP Toolbox
Collection of Nonlinear Eigenvalue Problems :
T. Betcke, N. J. Higham, V. Mehrmann, C. Schröder, F. T.
Quadratic, polynomial, rational and other nonlinear
eigenproblems.
Provided in the form of a MATLAB Toolbox.
Problems from real-life applications + specifically
constructed problems.
http://www.mims.manchester.ac.uk/research/
numerical-analysis/nlevp.html
New release to come. Further contributions welcome.
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Sample of Problems
Bicycle (pep,qep,real,parameter-dependent)
2 × 2 quadratic poly. arising in study of bicycle self-stability.
Railtrack (pep,qep,t-palindromic,sparse)
T-palindromic quadratic of size 1005. Stems from a model of
vibration of rail tracks under the excitation of high speed trains.
Butterfly (pep,real, T-even,scalable)
quartic matrix polynomial with T-even structure.
Loaded string (rep,real,symmetric,scalable)
rational eigenvalue problem describing eigenvibration of a
loaded string.
Gun (nep,sparse) nonlinear eigenvalue problem modeling a
radio-frequency gun cavity.
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Quadratic Eigenvalue Problem (QEP)
Concentrate on
Q(λ) = λ2M + λD + K ,
with M , D, K ∈ Cn×n.
Appears in many practical applications.
Recent work on quadratization: convert degree ℓ matrix
polynomials to degree 2 (Al-Ammari & T, 2009; Huang,
Lin & Su, 2008)
Mainly for structured matrix polynomials.
Use new efficient algorithms for quadratics.
Theoretical reasons.
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Outline
Review of recent progress.
Structure Preserving Transformations.
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Linearization
Standard way of treating QEPs both theoretically and
numerically.
Convert Q(λ) = λ2M + λD + K into a linear pencil such as
C1(λ) = λ
[M 0
0 I
]+
[D K
−I 0
].
(first companion form)
C1(λ) is a linearization of Q, i.e, there exist E(λ) and
F (λ) with constant, nonzero determinants s.t.
[Q(λ) 0
0 I
]= E(λ)C1(λ)F (λ).
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Work on Linearizations
New (structure preserving) linearizations derived along with
algorithms preserving spectral properties in finite precision
arithmetic.
[Antoniou, Higham, Lin, Mackey, Mackey, Mehl, Mehrmann,
T., Vologiannidis, . . . ]
Mackey, Mackey, Mehl & Mehrmann (2006) introduce
L1(Q) =
L(λ) : L(λ)
[λI
I
]=
[v1Q(λ)v2Q(λ)
], v ∈ C
2
.
Almost all pencils in L1 are linearizations.
E’vecs of Q easily recovered from e’vecs of L ∈ L1
([M4, 2006], [Higham, Li, T., 2007]).
L1(Q) is a rich source of interesting linearizations.
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Block Symmetric Linearizations
Higham, Mackey, Mackey and T. (2006) define
B(Q) :=
λX + Y ∈ L1(Q) : XB = X , Y B = Y
=
v1L1(λ) + v2L2(λ), v ∈ C2 ,
where
L1(λ) = λ
[M 0
0 −K
]+
[D K
K 0
], L2(λ) = λ
[0 M
M D
]+
[−M 0
0 K
].
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Block Symmetric Linearizations
Higham, Mackey, Mackey and T. (2006) define
B(Q) :=
λX + Y ∈ L1(Q) : XB = X , Y B = Y
=
v1L1(λ) + v2L2(λ), v ∈ C2 ,
where
L1(λ) = λ
[M 0
0 −K
]+
[D K
K 0
], L2(λ) = λ
[0 M
M D
]+
[−M 0
0 K
].
L ∈ B(Q) with vector v is a linearization of Q iff e’val of Q
is not a root of p(x ; v) = v1x + v2.
[Higham, Mackey, T., 2009] Identified definite pencils in
B(Q) for hyperbolic (overdamped) Q.
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Linearization Process
Better understanding of linearization process and effects of
scaling on
conditioning of eigenvalues,
backward error of computed eigenpairs.
[Adhikari, Alam, Betcke, Higham, Kressner, Li, Mackey, Mehl,
Mehrmann, T., . . . ]
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Linearization Process
Better understanding of linearization process and effects of
scaling on
conditioning of eigenvalues,
backward error of computed eigenpairs.
[Adhikari, Alam, Betcke, Higham, Kressner, Li, Mackey, Mehl,
Mehrmann, T., . . . ]
Illustration: free vibrations of aluminium beam.
M > 0, K > 0, D ≥ 0 ⇒ all ei’vals have Re(λ) ≤ 0.
D is rank 1. Can show n pure imaginary e’vals.
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Eigenvalues of Q(λ) = λ2M + λD + K
C1(λ) = λ
[M 0
0 I
]+
[D K
−I 0
].
L1(λ) = λ
[M 0
0 −K
]+
[D K
K 0
], L2(λ) = λ
[0 M
M D
]+
[−M 0
0 K
].
coeffs = nlevp(’damped−beam’,100);
K = coeffs1; D = coeffs2; M = coeffs3;
I = eye(2*nele); O = zeros(2*nele);
eval = eig([D K; -I 0],-[M O; O I]; % C1
%eval = eig([D K; -I 0],-[M O; O I]; % L1
%eval = eig([D K; -I 0],-[M O; O I]; % L2
plot(eval,’.r’);
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Computed Spectrum of C1, L1 and L2
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
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Comments
Show practical value of condition numbers and backward
errors for understanding the quality of computed results.
Importance of scaling QEPs before computing e’vals via
linearization.
Results not confined to the beam problem but apply to
any QEP.
[Grammont, Higham, T., 2009] General framework for
analyzing the linearization process (see Grammont’s talk,
MS12, Monday).
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Spectrum of C1, L2 before/after Scaling
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
−16 −14 −12 −10 −8 −6 −4 −2 0 2 4−4
−3
−2
−1
0
1
2
3
4x 10
6
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A Black Box (Dense Case)
[Hammarling, Higham, Munro, T. ]
step 1: Apply Fan, Lin and Van Dooren scaling.
step 2: Construct “a" companion linearization.
step 3: Deflate zeros and ∞ e’vals.
step 4: Solve generalized eigenproblem with QZ.
Optional:
step 5: Recover right/left e’vecs of Q from those of
companion form.
step 6: Compute e’vals condition number.
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Basic Facts
Triples (M , D, K ) cannot , in general, be simultaneously
diagonalized.
(See Lancaster, MS35, Wednesday)
No analog of generalized Schur form for matrix triples.
Cannot simultaneously tridiagonalize (M , D, K ).
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Diagonalization of Quadratics
For n × n symmetric Q(λ) = λ2M + λD + K with all e’vals
distinct, there exists U ∈ R2n×2n s.t.
UT
(λ
[0 M
M D
]+
[−M 0
0 K
])U = λ
[0 ΛM
ΛM ΛD
]+
[−ΛM 0
0 ΛK
],
where ΛM , ΛD and ΛK are diagonal matrices.
(Garvey, Friswell, Prells, 2002)
New diagonal quadratic matrix polynomial
QΛ(λ) = λ2ΛM + λΛC + ΛK .
Q(λ) and QΛ(λ) have the same e’vals.
U is a structure preserving transformation.
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Structure Preserving Transformation (SPT)
Recall: L(λ) ∈ B(Q) ⇐⇒ L(λ) = v1L1(λ) + v2L2(λ),
L1(λ) = λ
[M 0
0 −K
]+
[D K
K 0
], L2(λ) = λ
[0 M
M D
]+
[−M 0
0 K
].
Assume M nonsingular.
Definition
SL, SR nonsingular define an SPT for Q(λ) if
STL L2(λ)SR = λ
[0 M
M D
]+
[−M 0
0 K
]=: L2(λ).
L(λ) is a linearization of Q(λ) = λ2M + λD + K .
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Some Properties
Let (SL, SR) be an SPT mapping Q(λ) to Q(λ).
SPTs preserve block structure of pencils in B(Q) .
STL B(Q)SR = B(Q). Moreover, L(λ) ∈ B(Q) with vector v
⇔ L(λ) = STL L(λ)SR ∈ B(Q) with vector v .
Well-defined relations between e’vecs of Q and Q .
Let (λ, x , y) be an eigentriple of Q(λ). Then
[λx
x
]= SR
[λx
x
],
[λy
y
]= SL
[λy
y
],
where x , y such that Q(λ)x = 0, y∗Q(λ) = 0.
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Constraints
S =
[S11 S12
S21 S22
], T =
[T11 T12
T21 T22
], Sij , Tij ∈ R
n×n.
(S, T ) defines a SPT iff
ST11MT21 + ST
21MT11 + ST21CT21 = 0, (1)
−ST11MT12 + ST
21KT22 = 0, (2)
−ST12MT12 + ST
22KT22 = 0, (3)
ST11MT22 + ST
21MT12 + ST21DT22 = ST
11MT11 − ST21KT21, (4)
ST22MT11 + ST
12MT21 + ST22DT21 = ST
11MT11 − ST21KT21. (5)
5n2 constraints .
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Elementary SPTs (symmetric case)
Let T = I2n +
[abT adT
af T ahT
]∈ R
2n×2n, a, b, d , f , h ∈ Rn.
Rank-2 modification of I2n.
Let V = [ b d f h ] ∈ Rn×4.
For almost all a ∈ Rn, any solution V to VA = B defines
an SPT T .
A ∈ R4×3, B ∈ R
n×3 depend on a, M, D and K .
If (M , D, K )T
7−→ (M, D, K ) then M, D, K are low rank
modifications of M , D, K .
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An Application: Deflation of Eigenvalues
Let (λj , xj), j = 1, 2 be two given eigenpairs of Q(λ).
Want to transform n × n Q(λ) into
Q(λ) =
[Qd(λ) 0
0 q(λ)
]n − 1
1
such that
Λ(Q) = Λ(Q), (same spectrum)
q(λj) = 0, j = 1, 2.
Deflation procedure decoupling Q(λ) into two quadratics.
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Decoupling by Similarity
Suppose there exist nonsingular SL, SR s.t.
SLQ(λ)SR =
[Qd(λ) 0
0 q(λ)
]= Q(λ).
The roots λ1, λ2 of q(λ) are e’vals of Q and Q
Q(λj)xj = 0, Q(λj)en = 0, j = 1, 2
with e’vecs related by S−1R [ x1 x2 ] = [ en en ] .
Decoupling possible only if e’vecs x1 and x2 are parallel.
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Decoupling by Similarity
Suppose there exist nonsingular SL, SR s.t.
SLQ(λ)SR =
[Qd(λ) 0
0 q(λ)
]= Q(λ).
The roots λ1, λ2 of q(λ) are e’vals of Q and Q
Q(λj)xj = 0, Q(λj)en = 0, j = 1, 2
with e’vecs related by S−1R [ x1 x2 ] = [ en en ] .
Decoupling possible only if e’vecs x1 and x2 are parallel.
Is there an elementary SPT mapping Q to Q s.t. e’vecs of
Q with e’vals λ1, λ2 are parallel?
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Constraints
(λ1, x1), (λ2, x2) to be deflated with λ1 6= λ2 and x1 6= αx2.
Aim: construct T = I2n +[
abT
af TadT
ahT
]and nonzero z ∈ R
n s.t.
Q(λ)T
7−→ Q(λ) with Q(λj)z = 0, j = 1, 2.
Yields a, z and zT [ b d f h ] = zT V .
T is an SPT ⇐⇒ VA = B .
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Existence of SPT T
Theorem
Eigenpairs (λ1, x1), (λ2, x2) with λ1 6= λ2 either real or complex
conjugate can be mapped to (λ1, z), (λ2, z) by elementary
SPTs if
xTj Q′(λj)xj 6= 0, j = 1, 2,
real eigenpairs have opposite type:
sign(xT1 Q′(λ1)x1) = −sign(xT
2 Q′(λ2)x2).
Can generate a family of SPTs mapping (λj , xj) to (λj , z),j = 1, 2.
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Deflation of (λ1, z), (λ2, z)
Lemma
If (λ2j M + λjC + K )z = 0, j = 1, 2 with λ1 6= λ2 then
(M , C, K )z = (mp, cp, kp), p ∈ Rn, pT z = 1,
c = −m(λ1 + λ2), k = mλ1λ2.
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Deflation of (λ1, z), (λ2, z)
Lemma
If (λ2j M + λjC + K )z = 0, j = 1, 2 with λ1 6= λ2 then
(M , C, K )z = (mp, cp, kp), p ∈ Rn, pT z = 1,
c = −m(λ1 + λ2), k = mλ1λ2.
Let nonsingular G be such that
Gen = z, GT p = en.
Then GT MGen = GT Mz = mGT p = men and Lemma ⇒
GT (M , C, K )G =
([M 0
0 m
],
[C 0
0 c
],
[K 0
0 k
]).
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Example 1
Q(λ) = λ2
[2 −1
−1 3
]+ λ
[0 1
1 0
]+
[3 2
2 3
]
Given λ1,2 = −0.34 ± 1.84i and associated e’vecs, our
deflation procedure yields
λ2[
5.6 2.0e-162.0e-16 −1.4e-1
]+ λ
[−1.6 −9.4e-16
−9.4e-16 −9.3e-2
]+
[1.6 −9.8e-17
−9.8e-17 −4.8e-1
],
with κ2(T ) = 7.9 and κ2(G) ≈ 1.
Decoupling accomplished to within the working precision.
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Example 2: Damped Beam Problem
M, C, K generated by nlevp(’damped−beam’,nele).
Q(λ) = λ2M + λC + K and undamped Qu(λ) = λ2M + K have
n e’vals in common that we deflate by
our decoupling procedure: QS
7−→[
Q1(λ)0
0Q2(λ)
],
using special property of Q(λ) to orthogonally block
diag’lize it and then diag’lize one block with Cholesky-QR
(transformation W ).
n κ2(S) κ2(W )16 4.47e1 3.79e1
32 9.57e1 7.84e1
64 1.95e2 1.57e2
κ2(E) = ‖E‖2‖E−1‖2
κ2(S) not much larger that κ2(W ).
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Concluding Remarks
Deflation procedure extends to nonsymmetric quadratics.
First attempt at defining an SPT with a well-defined
action.
Deflation procedure finds application in
second-order model reduction,
model updating with no spill-over.
For papers and Eprints,
http://www.ma.man.ac.uk/~ftisseur/
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