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Palindromic Quadratization and Structure-Preserving Algorithm for Palindromic Matrix Polynomials. Wei- Shuo Su . Nation Chiao Tung University Department of Applied Mathematics College of Science . 蘇偉碩 2013/12/8. Introduction and motivation Linearization and Quadratization - PowerPoint PPT Presentation
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Nation Chiao Tung UniversityDepartment of Applied Mathematics College of Science
Palindromic Quadratization and Structure-Preserving Algorithm for Palindromic Matrix Polynomials
Wei-Shuo Su 蘇偉碩
2013/12/81
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• Introduction and motivation• Linearization and Quadratization• Backward error influence and balancing • Numerical results
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We investigate the solution of a particular rational eigenvalue problem that arises in an industrial project studying the vibration of rail tracks under the excitation arising from high speed trains.
This eigenvalue problem has the form
where and with
Taiwan High Speed Rail
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𝐾𝑞+𝐷�̇�+𝑀 �̈�= 𝑓 (𝑡 )
The displacements of two boundary cross sections of the modeled rail are assumed to have a ratio λ, which is dependent on the excitation frequency of the external force.From the virtual work principle and strain-stress relationship, the governing equation for the displacement vector q involving viscous damping can be formulated by
where K,D, and M from the finite element discretizationon a uniform mesh satisfy the given boundary conditions.
The external force is assumed to be periodic.
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From the spectral modal analysis, we consider , where ω is the frequency of the external force and is the corresponding eigenmode.Consequently, we get the palindromic QEP
(𝜆2~𝐴1⊺+𝜆~𝐴0+
~𝐴1
❑ )~𝑥=0
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The “Linearization" is a typical and frequently used technique to solve the (PQEP) in which the problem is reformulated into a linear one which doubles the order of the system. We select suitable matrices and the vector and transform (PQEP) into the (GEP)
( 𝐴− 𝜆𝐵 )𝜑=0
satisfying the relationℰ (𝜆 ) ( 𝐴− 𝜆𝐵 )ℱ (𝜆 )=[𝒫(𝜆) 0
0 𝐼𝑛]In this case,
det = cdetshows that the eigenvalues are the same.
constant determinants
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The first companion form and
and the second companion form
and
Drawbacks:1. Doubles the size of the problem dimension2. Palindromic and symplectic may be lost
Symplectic: and both exist
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In the , W.-W. Lin propose some structure-preserving method to deal with ⊤-palindromic quadratic pencil.
1. Structure-preserving algorithms for palindromic quadratic eigenvalue problems arising from vibration on fast trains
And the numerical result is quite excellent.
Based on the spirit, we want to use the same algorithm to solve palindromic matrix eigenvalue problems.
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We consider the palindromic matric polynomials of degree
𝒫 (𝜆 )≡∑𝑘=0
𝑑−1
𝜆2𝑑−𝑘𝐴𝑑−𝑘⋆ +𝜆𝑑𝐴0+𝜀∑
𝑘=1
𝑑
𝜆𝑑−𝑘 𝐴𝑘
where and or .It also satisfies 𝒫 (𝜆 )=𝜀 𝜆2𝑑𝒫( 1𝜆 )
⋆
The eigenvalues appear in the pairs of the form .
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Definition: (Quadratization) is a quadratization of if there are matrix rational functions and with nonzero and constant determinants satisfying the two-sided factorization
ℰ (𝜆 ) 𝒬 (𝜆 )ℱ ( 𝜆 )=[𝒫(𝜆) 00 𝐼𝑞−𝑣 ]
P-Quadratization: and have the same structure property.
It also implies that detdet for some nonzero constant c, and and have the same eigenvalue.
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Theorem: (eigenvector relation) is an eigenvector of if and only if is an eigenvector of .
Theorem: can be -quadratized into a -palindromic quadratic pencil of the form𝒬 (𝜆 )≡ 𝜆2𝒜1
⋆+𝜆𝒜0+𝜀𝒜1
with . We show degree 4
(we can see the paper for generalize )
,
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H-even PEP
T-even PEP
H-odd PEP
T-odd PEP
H-palindromic
PEPT-palindromic
PEP
H-anti-palindromic
PEP
T-anti-palindromic PEP
H-palindromic
QEPT-palindromic
QEP
H-anti-palindromic
QEP
T-palindromic GEP
H-palindromic QEP
T-palindromic QEP
Solving flow chart
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Proposition:Given an H-anti-PQEP: , with Then is an eigenpair of the H-anti-PQEP if and only if is an eigenpair of the H-PQEP:
Let , where
is called a -even if and
is called a -odd if and
By the Cayley transformation, it can be transformed to a -PPEP 𝜆 → 𝜆− 𝑖
𝜆+𝑖
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H-even PEP
T-even PEP
H-odd PEP
T-odd PEP
H-palindromic
PEPT-palindromic
PEP
H-anti-palindromic
PEP
T-anti-palindromic PEP
H-palindromic
QEPT-palindromic
QEP
H-anti-palindromic
QEP
T-palindromic GEP
H-palindromic QEP
T-palindromic QEP
Linearizationwhere
Solving flow chart
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We consider the -PQEP𝒬 (𝜆 )𝑥≡ (𝜆2𝐴1⋆+𝜆𝐴0+𝐴1 )𝑥=0
In order the preserve the symplectic structure, we get the special linearization
(ℳ− 𝜆ℒ )𝑧≡([ 𝐴1 0−𝐴0 − 𝐼 ]−𝜆 [ 0 𝐼
𝐴1⋆ 0]) [𝑥𝑦 ]=0
where and it also satisfies,
So that the matrix pair has eigenvalues and .The pencil are called -symplectic
H-PQEP Linear
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The -transform of an -symplectic matrix pair is defined by
, It can be verify that and If is an eigenvalue of , so is .
Theorem:Suppose is an eigenvector of corresponding to . If or, then or is an eigenpair of , respectively.
(𝒮+𝒮−1)
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TheoremSuppose that is an eigenvector of corresponding to the eigenvalue , and denote with . Let be a root of the quadratic equation . Then
(i) At least one of vector and is nonzero(ii) If , then is an eigenvector of corresponding to (iii) If , then is an eigenvector of corresponding to .
relation
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,
However, Patel’s algorithm can only process in the real case. So we extend to a real matrix pair by
,
where and
It can be verify that if is an eigenvalue of , then and are eigenvalues of
Substituting the H-skew-Hamiltonian and can be write as
simplify case
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The pair can be reduced to block upper triangular forms
,
where are orthogonal satisfying , and are upper Hessenberg and upper triangular, respectively.
Theorem (i) If is an eigenpair of , then are eigenvectors of (ii) If is an eigenvector of corresponding to the eigenvalue ,
then is an eigenvector of
(𝒦2 ,𝒩2 )(~𝒦2 ,
~𝒩2)Patel’s Alg
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H-PQEP Linear (𝒮+𝒮−1)
simplify case
(𝒦2 ,𝒩2 )(~𝒦2 ,
~𝒩2)Patel’s Alg
Solve and follow the conversion relationship
SPA for H-PQEP flow chart
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Algorithm: SPA for H-PQEPInput: with and Output: All eigenvalues and eigenvectors of 1. Form the matrix pair 2. Reduce to block upper triangular forms3. Compute eigenpairs of by the QZ algorithm4. Compute , 5. Compute the eigenpair of by
6. Compute and by solving ; Compute and 7. If then it is an eigenvector of corresponding to ; If , then
it is an eigenvalue of corresponding to 𝑧 𝑗=𝒥𝑇 (𝑥1 𝑗−𝑦 2 𝑗+𝑖 (𝑥2 𝑗+ 𝑦1 𝑗 ))≡ [𝑧 𝑗1
𝑇 ,𝑧 𝑗2𝑇 ]𝑇
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We consider 𝒫 (𝜆 )=𝜆4 𝐴2𝐻+𝜆3 𝐴1
𝐻+𝜆2 𝐴0+𝜆 𝐴1+𝐴2
From the P-Quadration we mentioned
which satisfies𝑄 (𝜆 )=𝜆2𝒜1
𝐻+𝜆𝒜0+𝒜1
If is an eigenpair of , then is an eigenpair of with where
𝑧 2=1
𝑑1 𝜆0(𝜆02 𝐴2
𝐻 𝑧1+𝑧 1)
,
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In order to balance the entries of coefficient matrices in , we define a complex diagonal matrix
𝐷≡diag (2𝛼1 ,2𝛼2+𝑖 𝛽2 ,…,2𝛼𝑛+𝑖 𝛽𝑛)Such that in the new palindromic matrix polynomial
𝐷𝒫 (𝜆 )𝐷⋆
whose each entries are close to one as much as possible.That is
2𝛼 𝑗+𝑖 𝛽 𝑗 𝐴𝑘 ( 𝑗 , ℓ )2𝛼ℓ−𝑖 𝛽ℓ ≈1
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The parameters can be determined by solving the least square problems
,
Then, the parameters and are determined by
,
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Then we set the balancing coefficient
𝑑𝑖(𝑠 )≔√𝜌(𝑠 ) max
0≤𝑘≤𝑑 {∑𝑗=1𝑛
∑ℓ=1
𝑛
|𝐴𝑘 ( 𝑗 , ℓ )|/𝑛2}𝜌(𝑠 )=max {𝛿𝑖
( 𝑠) ; 𝑖=1 ,…,𝑚}
From the row balancing of , we first set𝜂𝑖
(𝑠 )=max {1 ,max {‖𝐴2𝑚−𝑘+𝑠− 1‖1:𝑘=0,1 ,…,2𝑖−3+𝑠}}
We take to be
𝛿𝑖(𝑠 )=√𝜂 𝑖
( 𝑠)(∑𝑗=1𝑛
∑ℓ=1
𝑛
|𝐴2𝑚−2 𝑖+1( 𝑗 , ℓ) /𝑛2|)
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We compare the computational cost for PQ_SPA and PL_SPA
We define the associated relative residual by RRes≡RRes (𝜆 ,𝑥 )≔ ‖𝒫 (𝜆 )𝑥‖2
[∑𝑘=1
𝑑
(|𝜆|𝑑+𝑘+|𝜆|𝑑−𝑘 )‖𝐴𝑘‖2+|𝜆|𝑑‖𝐴0‖2]‖𝑥‖2
The eigenvalues of H-PPEP appear in reciprocal pairs We define the reciprocities of the computed eigenvalues by
,
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Example 2.1 Consider the H-PPEP with and
complex matrices and normal distribution with zero mean and standard deviation
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Example 2.2 Consider the H-PPEP with and and and being defined as
,
where , and
{𝜑𝑖(𝑘 )=4 𝑖+𝑘− ℓ ,𝜑ℓ+𝑖
(𝑘)=4 𝑖−𝑘(𝑖=1 ,…, ℓ)
𝜑𝑛(𝑘)=4
𝑛2−𝑘if 𝑛 is odd with if n is even or
(Large
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Example 2.3 Consider the H-PPEP with and and being defined as
𝐴2=𝐵13 ∙𝑑𝑖𝑎𝑔 {𝜑1(3) ,…,𝜑𝑛
(3 ) } ∙𝐵23∈ℂ(𝑛×𝑛)
where , and is defined in ex2.2 with
(Large
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*REFERENCE*Chu, E. K.-W. and Hwang, T.-M. and Lin, W.-W. and Wu, C.-T.
Vibration of fast trains, palindromic eigenvalue problems and structure-preserving doubling algorithms, J. Comput. Appl. Math. 2008, 219:237--252*Huang, T.-M. and Lin, W.-W. and Qian, J. Structure-preserving
algorithms for palindromic quadratic eigenvalue problems arising from vibration on fast trains, SIAM J. Matrix Anal. Appl. 2009,30:1566-1592.*Mackey, D.S. and Mackey, N. and Mehl, C. and Mehrmann, V. Vector
Spaces of Linearizations for Matrix Polynomials, SIAM J. Matrix Anal. Appl. 2006, 28: 971--1004.*Li, R.-L. and Lin, W.-W. and Wang, C.-S. Structured Backward Error
for Palindromic Polynomial eigenvalue problems. Numerische Mathematik, 2010, 116(1): 95-122.*Huang, T.-M. and Lin, W.-W. and Su, W.-S. Palindromic
Quadratization and Structure-Preserving Algorithm for Palindromic Matrix Polynomials of Even Degree, Numerische Mathematik 2011, 118(4): 713-735.
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If is success in life, then equal plus and . Work is ; is play; and is keeping your mouth shut.
是成功, 是工作,是娛樂, 是沉默。亞伯特、愛因斯坦