SlideShare a Scribd company logo
1 of 22
Download to read offline
Hermite integrators and 2-parameter subgroup of
Riordan group
Keigo Nitadori
keigo@riken.jp
RIKEN Advanced Institute for Computational Science
July 13, 2016
Abstract
A general form for the family of 2-step Hermite integrators
Up to p-th order derivative of the force is calculated directly to
obtain 2(p + 1)-th order accuracy
Now, a simple and general form for the predictor is available
The mathematics was slightly simplified and generalized from the
last talk (99% of the proof was invented by Satoko Yamamoto)
Upper triangular Pascal matrix
This following upper triangular Pascal matrix appearers frequently in
this story:
(1) Upas ij
=
j
i
, U−1
pas ij
= (−1)i+j j
i
.
A (p + 1) × (p + 1) sized one looks like,
(2) Upas =
0
0
1
0
2
0 · · · p
0
0 1
1
2
1 · · · p
1
0 0 2
2 · · · p
2
...
...
...
...
...
0 0 0 · · · p
p
.
Polynomial shift
A vector of scaled force
(3) F(t) =
f (t)
h f (1) (t)
h2 f (2) (t)/2!
...
hn f (p) (t)/n!
,
where f (n) (t) = dn
dtn f (t) (0 ≤ n ≤ p), obeys a transformation
F(t + h) =
0
0
1
0
2
0 · · · p
0
0 1
1
2
1 · · · p
1
0 0 2
2 · · · p
2
...
...
...
...
...
0 0 0 · · · p
p
F(t) = UpasF(t).(4)
Linear equation to solve
For even order polynomial coefficients:
(5)
F+
0
F−
1
F+
2
F−
3
...
=
0
0
2
0
4
0
6
0 . . .
0 2
1
4
1
6
1 . . .
0 2
2
4
2
6
2 . . .
0 0 4
3
6
3 . . .
...
...
...
...
...
F0
F2
F4
F6
...
.
For odd order polynomial coefficients:
(6)
F−
0
F+
1
F−
2
F+
3
...
=
1
0
3
0
5
0
7
0 . . .
1
1
3
1
5
1
7
1 . . .
0 3
2
5
2
7
2 . . .
0 3
3
5
3
7
3 . . .
...
...
...
...
...
F1
F3
F5
F7
...
.
(definitions: F+
i = 1
2 (FR
i + FL
i ), F−
i = 1
2 (FR
i − FL
i ).)
Generalization
We modify the upper triangular Pascal matrix with 2-parameter (a, b),
as in
(7) [M(a,b)]ij =
aj + b
i
.
This is no longer an upper triangular matrix.
Our interest is to find the solutions
M−1
(2,0) for the even order terms, and
M−1
(2,1) for the odd order terms.
Unfortunately, explicit forms of their inverses seem to be hardly
available.
Matrix inverse (conjecture)
It turned out that, by multiplying (inverse) Pascal matrices, we can
write
(8) M−1
(a,b) = U−1
pas M(1/a,−b/a) U−1
pas,
or equivalently,
(9) M(a,b)U−1
pas
−1
= M(1/a,−b/a)U−1
pas.
Thus, if we define
(10) L(a,b) = M(a,b)U−1
pas,
then
L−1
(a,b) =L(1/a,−b/a),(11)
M−1
(a,b) =U−1
pasL(1/a,−b/a).(12)
Interpretation
A 2-parameter dense matrix M(a,b) can be factorized into
(13) M(a,b) = L(a,b)Upas
and we will soon see that L(a,b) is upper triangular.
Though explicit matrix elements of L(a,b) in general are hard to obtain
(see (3.150) GouldBK.pdf, Sprugnoli, 2006), all we need are
M−1
(2,0) =U−1
pasL(1/2,0),(14)
M−1
(2,1) =U−1
pasL(1/2,−1/2).(15)
Fortunately, matrix elements of U−1
pas, L(1/2,0), and L(1/2,−1/2) are
available explicitly.
Example (for p = 7, 16th-order) I
(16) [L1/2,0]nk =
(−2)k
(−4)n
k
n
2n − k − 1
n − k
1 0 0 0 0 0 0 0
0 1
2 0 0 0 0 0 0
0 −1
8
1
4 0 0 0 0 0
0 1
16 −1
8
1
8 0 0 0 0
0 − 5
128
5
64 − 3
32
1
16 0 0 0
0 7
256 − 7
128
9
128 − 1
16
1
32 0 0
0 − 21
1024
21
512 − 7
128
7
128 − 5
128
1
64 0
0 33
2048 − 33
1024
45
1024 − 3
64
5
128 − 3
128
1
128
Example (for p = 7, 16th-order) II
(17) [L1/2,−1/2]nk =
(−2)k
(−4)n
2n − k
n − k
1 0 0 0 0 0 0 0
−1
2
1
2 0 0 0 0 0 0
3
8 −3
8
1
4 0 0 0 0 0
− 5
16
5
16 −1
4
1
8 0 0 0 0
35
128 − 35
128
15
64 − 5
32
1
16 0 0 0
− 63
256
63
256 − 7
32
21
128 − 3
32
1
32 0 0
231
1024 − 231
1024
105
512 − 21
128
7
64 − 7
128
1
64 0
− 429
2048
429
2048 − 99
512
165
1024 − 15
128
9
128 − 1
32
1
128
L(a,b): 2-parameter lower-triangular matrix
We compute the matrix element from its definition:
[L(a,b)]ij = M(a,b)U−1
pas ij
(18)
=
∞
k=0
ak + b
i
(−1)k+j j
k
=
∞
k=0
[yi
](1 + y)ak+b
· [tk
](−1 + t)j
=
∞
k=0
[tk
](−1 + t)j
· [yi
](1 + y)b
(1 + y)a k
=[ti
](1 + t)b
−1 + (1 + t)a j
= R (1 + t)b
, −1 + (1 + t)a
ij
.
R(d(t), h(t)) is a Riordan array of which element is [ti]d(t)h(t)j.
Composition and convolution
To remove the summation on k, we used a relation
∞
k=0
[tk
] f (t) · [yn
]h(y)g(y)k
=
∞
m=0
∞
k=0
[tk
] f (t) · [yn−m
]hmg(y)k
(19)
=
∞
m=0
hm[tn−m
] f (g(t))
=
∞
m=0
[ym
]h(y) · [tn−m
] f (g(t))
=[tn
]h(t) f (g(t))
with f (t) = (−1 + t)j, g(y) = (1 + y)a, and h(t) = (1 + t)b.
Riordan array
is an infinitesimal lower triangle matrix, taking 2 formal power series.
The element at the n-th row and the k-th column is
(20) R d(t), h(t)
nk
= [tn
]d(t)h(t)k
.
Matrix multiplication is given by
R d(t), h(t) ◦ R f (t), g(t) = R d(t) · f h(t) , g h(t) .(21)
For our 2-parameter matrices
L(a,b) =R (1 + t)b
, −1 + (1 + t)a
,
L(c,d) =R (1 + t)d
, −1 + (1 + t)c
,
we have a multiplication
L(a,b) L(c,d) = R (1 + t)b
(1 + t)ad
, −1 + (1 + t)ac
= L(ac,ad+b).
(22)
Group structure
L(a,b) forms a subgroup of the Riordan group when a 0.
Multiplication:
L(a,b) L(c,d) = L(ac,ad+b).
Identity:
L(1,0) L(a,b) = L(a,b) L(1,0) = L(a,b)
Inverse:
L−1
(a,b) = L(1/a,−b/a).
The following 2 × 2 matrix preserves the same structure.
(23)
1 0
b a
1 0
d c
=
1 0
ad + b ac
.
Application to the Hermite integrators
Remember:
M−1
(2,0) =U−1
pasL−1
(2,0) = U−1
pasL(1/2,0),(24)
M−1
(2,1) =U−1
pasL−1
(2,1) = U−1
pasL(1/2,−1/2).(25)
All we need is the matrices elements of U−1
pas, L(1/2,0), and L(1/2,−1/2).
The first one is very simple: [U−1
pas]ij = (−1)i+j j
i .
Implementation (p = 7, 16th-order interpolation) I
Even order coefficients are computed in
F8
F10
F12
F14
=
1 −5 15 −35
0 1 −6 21
0 0 1 −7
0 0 0 1
×
(26)
5
64 − 3
32
1
16 0 0 0
− 7
128
9
128 − 1
16
1
32 0 0
21
512 − 7
128
7
128 − 5
128
1
64 0
− 33
1024
45
1024 − 3
64
5
128 − 3
128
1
128
F+
2 − 1
2 F−
1
F−
3
F+
4
F−
5
F+
6
F−
7
.
Implementation (p = 7, 16th-order interpolation) II
And the odd order ones
F9
F11
F13
F15
=
1 −5 15 −35
0 1 −6 21
0 0 1 −7
0 0 0 1
×
(27)
− 35
128
15
64 − 5
32
1
16 0 0 0
63
256 − 7
32
21
128 − 3
32
1
32 0 0
− 231
1024
105
512 − 21
128
7
64 − 7
128
1
64 0
429
2048 − 99
512
165
1024 − 15
128
9
128 − 1
32
1
128
F+
1 − F−
0
F−
2
F+
3
F−
4
F+
5
F−
6
F+
7
.
Implementation (p = 7, 16th-order interpolation) III
Right shift for the next predictor by right-bottom submatrix of Upas:
FR
8
FR
9
FR
10
FR
11
FR
12
FR
13
FR
14
FR
15
=
1 9 45 165 495 1287 3003 6435
0 1 10 55 220 715 2002 5005
0 0 1 11 66 286 1001 3003
0 0 0 1 12 78 364 1365
0 0 0 0 1 13 91 455
0 0 0 0 0 1 14 105
0 0 0 0 0 0 1 15
0 0 0 0 0 0 0 1
F8
F9
F10
F11
F12
F13
F14
F15
(28)
Matrix element of L(1/2, 0)
We use Lagrange inverse formula with w(t) = −1 +
√
1 + t. For
t = w(w + 2), φ(t) = 1/(2 + t) satisfies w = tφ(w).
[L1/2, 0]nk =[tn
] −1 + (1 + t)1/2 k
(29)
= [tn
]wk
=
k
n
[tn−1
]tk−1
φ(t)n
=
k
n
[tn−k
](2 + t)−n
= 2k−2n k
n
−n
n − k
= (−1)n+k
2k−2n k
n
2n − k − 1
n − k
.
In case n = 0, it is δk0.
Matrix element of L(1/2, −1/2)
Similarly,
[L1/2, −1/2]nk =[tn
](1 + t)−1/2
−1 + (1 + t)1/2 k
(30)
= [tn
]
wk
1 + w
= [tn
]
tk
1 + t
φ(t)n−1
φ(t) − tφ (t)
= [tn
]
tk
1 + t
1
(2 + t)n−1
2(1 + t)
(2 + t)2
= 2[tn−k
](2 + t)−n−1
= 2k−2n −n − 1
n − k
= (−1)n+k
2k−2n 2n − k
n − k
.
Review
We have concerned with a 2-parameter matrix
(31) [M(a,b)]ij =
aj + b
i
,
which has an LU factorized form
(32) M(a,b) = L(a,b)Upas
where Upas = M(1,0) is an upper triangle Pascal matrix and L(a,b) is a
lower triangle matrix expressed in a Riordan array form
(33) L(a,b) = R (1 + t)b
, −1 + (1 + t)a
.
Matrix inverse M−1
(a,b) is available in
(34) M−1
(a,b) = U−1
pasL−1
(a,b) = U−1
pasL(1/a,−b/a) = U−1
pas M(1/a,−b/a) U−1
pas,
Summary
We have generalized the 2-step Hermite integrators
Predictor and corrector
General and explicit matrix elements
Simple and economical formulation, easy to implement
Mathematical combinatorics have performed an essential role for
the derivation and proof
Formal power series, Lagrange inverse formula, Riordan array
Implementers only need to know about matrix vector
multiplications and binomial coefficients, not the full mathematics

More Related Content

What's hot

On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...
On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...
On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...inventionjournals
 
Diifferential equation akshay
Diifferential equation akshayDiifferential equation akshay
Diifferential equation akshayakshay1234kumar
 
Gamma & Beta functions
Gamma & Beta functionsGamma & Beta functions
Gamma & Beta functionsSelvaraj John
 
Csm chapters12
Csm chapters12Csm chapters12
Csm chapters12Pamela Paz
 
Algebra formulae
Algebra formulaeAlgebra formulae
Algebra formulaeTamizhmuhil
 
Gamma beta functions-1
Gamma   beta functions-1Gamma   beta functions-1
Gamma beta functions-1Selvaraj John
 
Interpolation In Numerical Methods.
 Interpolation In Numerical Methods. Interpolation In Numerical Methods.
Interpolation In Numerical Methods.Abu Kaisar
 
Gamma and betta function harsh shah
Gamma and betta function  harsh shahGamma and betta function  harsh shah
Gamma and betta function harsh shahC.G.P.I.T
 
MODULE 4- Quadratic Expression and Equations
MODULE 4- Quadratic Expression and EquationsMODULE 4- Quadratic Expression and Equations
MODULE 4- Quadratic Expression and Equationsguestcc333c
 
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...Editor IJCATR
 

What's hot (17)

Chapter 5 assignment
Chapter 5 assignmentChapter 5 assignment
Chapter 5 assignment
 
On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...
On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...
On Some Notable Properties of Zero Divisors in the Ring of Integers Modulo m ...
 
Diifferential equation akshay
Diifferential equation akshayDiifferential equation akshay
Diifferential equation akshay
 
Sub1567
Sub1567Sub1567
Sub1567
 
MA8353 TPDE
MA8353 TPDEMA8353 TPDE
MA8353 TPDE
 
Integration SPM
Integration SPMIntegration SPM
Integration SPM
 
Gamma & Beta functions
Gamma & Beta functionsGamma & Beta functions
Gamma & Beta functions
 
Csm chapters12
Csm chapters12Csm chapters12
Csm chapters12
 
11365.integral 2
11365.integral 211365.integral 2
11365.integral 2
 
Algebra formulae
Algebra formulaeAlgebra formulae
Algebra formulae
 
Gamma beta functions-1
Gamma   beta functions-1Gamma   beta functions-1
Gamma beta functions-1
 
Interpolation In Numerical Methods.
 Interpolation In Numerical Methods. Interpolation In Numerical Methods.
Interpolation In Numerical Methods.
 
Gamma and betta function harsh shah
Gamma and betta function  harsh shahGamma and betta function  harsh shah
Gamma and betta function harsh shah
 
MODULE 4- Quadratic Expression and Equations
MODULE 4- Quadratic Expression and EquationsMODULE 4- Quadratic Expression and Equations
MODULE 4- Quadratic Expression and Equations
 
Maths05
Maths05Maths05
Maths05
 
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...
On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph f...
 
Alg2 lesson 3-2
Alg2 lesson 3-2Alg2 lesson 3-2
Alg2 lesson 3-2
 

Similar to Hermite integrators and 2-parameter subgroup of Riordan group

Hermite integrators and Riordan arrays
Hermite integrators and Riordan arraysHermite integrators and Riordan arrays
Hermite integrators and Riordan arraysKeigo Nitadori
 
Notions of equivalence for linear multivariable systems
Notions of equivalence for linear multivariable systemsNotions of equivalence for linear multivariable systems
Notions of equivalence for linear multivariable systemsStavros Vologiannidis
 
IIT Jam math 2016 solutions BY Trajectoryeducation
IIT Jam math 2016 solutions BY TrajectoryeducationIIT Jam math 2016 solutions BY Trajectoryeducation
IIT Jam math 2016 solutions BY TrajectoryeducationDev Singh
 
ISI MSQE Entrance Question Paper (2008)
ISI MSQE Entrance Question Paper (2008)ISI MSQE Entrance Question Paper (2008)
ISI MSQE Entrance Question Paper (2008)CrackDSE
 
Docslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesDocslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesbarasActuarial
 
On problem-of-parameters-identification-of-dynamic-object
On problem-of-parameters-identification-of-dynamic-objectOn problem-of-parameters-identification-of-dynamic-object
On problem-of-parameters-identification-of-dynamic-objectCemal Ardil
 
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.Igor Moiseev
 
Hull White model presentation
Hull White model presentationHull White model presentation
Hull White model presentationStephan Chang
 
A note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianA note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianTomonari Masada
 
ISI MSQE Entrance Question Paper (2010)
ISI MSQE Entrance Question Paper (2010)ISI MSQE Entrance Question Paper (2010)
ISI MSQE Entrance Question Paper (2010)CrackDSE
 
math1مرحلة اولى -compressed.pdf
math1مرحلة اولى -compressed.pdfmath1مرحلة اولى -compressed.pdf
math1مرحلة اولى -compressed.pdfHebaEng
 
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...inventionjournals
 
EC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformEC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformNimithaSoman
 
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...Tomoya Murata
 
dhirota_hone_corrected
dhirota_hone_correcteddhirota_hone_corrected
dhirota_hone_correctedAndy Hone
 
Last+minute+revision(+Final)+(1) (1).pptx
Last+minute+revision(+Final)+(1) (1).pptxLast+minute+revision(+Final)+(1) (1).pptx
Last+minute+revision(+Final)+(1) (1).pptxAryanMishra860130
 
8 Continuous-Time Fourier Transform Solutions To Recommended Problems
8 Continuous-Time Fourier Transform Solutions To Recommended Problems8 Continuous-Time Fourier Transform Solutions To Recommended Problems
8 Continuous-Time Fourier Transform Solutions To Recommended ProblemsSara Alvarez
 

Similar to Hermite integrators and 2-parameter subgroup of Riordan group (20)

Hermite integrators and Riordan arrays
Hermite integrators and Riordan arraysHermite integrators and Riordan arrays
Hermite integrators and Riordan arrays
 
Notions of equivalence for linear multivariable systems
Notions of equivalence for linear multivariable systemsNotions of equivalence for linear multivariable systems
Notions of equivalence for linear multivariable systems
 
IIT Jam math 2016 solutions BY Trajectoryeducation
IIT Jam math 2016 solutions BY TrajectoryeducationIIT Jam math 2016 solutions BY Trajectoryeducation
IIT Jam math 2016 solutions BY Trajectoryeducation
 
ISI MSQE Entrance Question Paper (2008)
ISI MSQE Entrance Question Paper (2008)ISI MSQE Entrance Question Paper (2008)
ISI MSQE Entrance Question Paper (2008)
 
PCA on graph/network
PCA on graph/networkPCA on graph/network
PCA on graph/network
 
Docslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesDocslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tables
 
On problem-of-parameters-identification-of-dynamic-object
On problem-of-parameters-identification-of-dynamic-objectOn problem-of-parameters-identification-of-dynamic-object
On problem-of-parameters-identification-of-dynamic-object
 
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.
Singularities in the one control problem. S.I.S.S.A., Trieste August 16, 2007.
 
final19
final19final19
final19
 
Hull White model presentation
Hull White model presentationHull White model presentation
Hull White model presentation
 
A note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianA note on variational inference for the univariate Gaussian
A note on variational inference for the univariate Gaussian
 
ISI MSQE Entrance Question Paper (2010)
ISI MSQE Entrance Question Paper (2010)ISI MSQE Entrance Question Paper (2010)
ISI MSQE Entrance Question Paper (2010)
 
math1مرحلة اولى -compressed.pdf
math1مرحلة اولى -compressed.pdfmath1مرحلة اولى -compressed.pdf
math1مرحلة اولى -compressed.pdf
 
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
 
EC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformEC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transform
 
Solution homework2
Solution homework2Solution homework2
Solution homework2
 
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...
Doubly Accelerated Stochastic Variance Reduced Gradient Methods for Regulariz...
 
dhirota_hone_corrected
dhirota_hone_correcteddhirota_hone_corrected
dhirota_hone_corrected
 
Last+minute+revision(+Final)+(1) (1).pptx
Last+minute+revision(+Final)+(1) (1).pptxLast+minute+revision(+Final)+(1) (1).pptx
Last+minute+revision(+Final)+(1) (1).pptx
 
8 Continuous-Time Fourier Transform Solutions To Recommended Problems
8 Continuous-Time Fourier Transform Solutions To Recommended Problems8 Continuous-Time Fourier Transform Solutions To Recommended Problems
8 Continuous-Time Fourier Transform Solutions To Recommended Problems
 

More from Keigo Nitadori

上三角 Pascal 行列による多項式のシフト
上三角 Pascal 行列による多項式のシフト上三角 Pascal 行列による多項式のシフト
上三角 Pascal 行列による多項式のシフトKeigo Nitadori
 
A block-step version of KS regularization
A block-step version of KS regularizationA block-step version of KS regularization
A block-step version of KS regularizationKeigo Nitadori
 
Higher order derivatives for N -body simulations
Higher order derivatives for N -body simulationsHigher order derivatives for N -body simulations
Higher order derivatives for N -body simulationsKeigo Nitadori
 
Convergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootConvergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootKeigo Nitadori
 
Snake eats leapfrog (in Japanese)
Snake eats leapfrog (in Japanese)Snake eats leapfrog (in Japanese)
Snake eats leapfrog (in Japanese)Keigo Nitadori
 

More from Keigo Nitadori (7)

上三角 Pascal 行列による多項式のシフト
上三角 Pascal 行列による多項式のシフト上三角 Pascal 行列による多項式のシフト
上三角 Pascal 行列による多項式のシフト
 
A block-step version of KS regularization
A block-step version of KS regularizationA block-step version of KS regularization
A block-step version of KS regularization
 
Higher order derivatives for N -body simulations
Higher order derivatives for N -body simulationsHigher order derivatives for N -body simulations
Higher order derivatives for N -body simulations
 
Convergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootConvergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-root
 
FMMの実装と導出
FMMの実装と導出FMMの実装と導出
FMMの実装と導出
 
Snake eats leapfrog (in Japanese)
Snake eats leapfrog (in Japanese)Snake eats leapfrog (in Japanese)
Snake eats leapfrog (in Japanese)
 
Rh typing
Rh typingRh typing
Rh typing
 

Recently uploaded

VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PPRINCE C P
 
Botany krishna series 2nd semester Only Mcq type questions
Botany krishna series 2nd semester Only Mcq type questionsBotany krishna series 2nd semester Only Mcq type questions
Botany krishna series 2nd semester Only Mcq type questionsSumit Kumar yadav
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​kaibalyasahoo82800
 
Green chemistry and Sustainable development.pptx
Green chemistry  and Sustainable development.pptxGreen chemistry  and Sustainable development.pptx
Green chemistry and Sustainable development.pptxRajatChauhan518211
 
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral Analysis
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral AnalysisRaman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral Analysis
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral AnalysisDiwakar Mishra
 
Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)PraveenaKalaiselvan1
 
Animal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxAnimal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxUmerFayaz5
 
Natural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsNatural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsAArockiyaNisha
 
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINChromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINsankalpkumarsahoo174
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfSumit Kumar yadav
 
Biological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfBiological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfmuntazimhurra
 
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...ssifa0344
 
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 60009654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000Sapana Sha
 
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.Nitya salvi
 
Pests of mustard_Identification_Management_Dr.UPR.pdf
Pests of mustard_Identification_Management_Dr.UPR.pdfPests of mustard_Identification_Management_Dr.UPR.pdf
Pests of mustard_Identification_Management_Dr.UPR.pdfPirithiRaju
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)Areesha Ahmad
 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoSérgio Sacani
 
Chemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfChemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfSumit Kumar yadav
 
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...Lokesh Kothari
 
DIFFERENCE IN BACK CROSS AND TEST CROSS
DIFFERENCE IN  BACK CROSS AND TEST CROSSDIFFERENCE IN  BACK CROSS AND TEST CROSS
DIFFERENCE IN BACK CROSS AND TEST CROSSLeenakshiTyagi
 

Recently uploaded (20)

VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C P
 
Botany krishna series 2nd semester Only Mcq type questions
Botany krishna series 2nd semester Only Mcq type questionsBotany krishna series 2nd semester Only Mcq type questions
Botany krishna series 2nd semester Only Mcq type questions
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​
 
Green chemistry and Sustainable development.pptx
Green chemistry  and Sustainable development.pptxGreen chemistry  and Sustainable development.pptx
Green chemistry and Sustainable development.pptx
 
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral Analysis
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral AnalysisRaman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral Analysis
Raman spectroscopy.pptx M Pharm, M Sc, Advanced Spectral Analysis
 
Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)
 
Animal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxAnimal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptx
 
Natural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsNatural Polymer Based Nanomaterials
Natural Polymer Based Nanomaterials
 
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATINChromatin Structure | EUCHROMATIN | HETEROCHROMATIN
Chromatin Structure | EUCHROMATIN | HETEROCHROMATIN
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdf
 
Biological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdfBiological Classification BioHack (3).pdf
Biological Classification BioHack (3).pdf
 
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
TEST BANK For Radiologic Science for Technologists, 12th Edition by Stewart C...
 
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 60009654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
9654467111 Call Girls In Raj Nagar Delhi Short 1500 Night 6000
 
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.
❤Jammu Kashmir Call Girls 8617697112 Personal Whatsapp Number 💦✅.
 
Pests of mustard_Identification_Management_Dr.UPR.pdf
Pests of mustard_Identification_Management_Dr.UPR.pdfPests of mustard_Identification_Management_Dr.UPR.pdf
Pests of mustard_Identification_Management_Dr.UPR.pdf
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)
 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on Io
 
Chemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdfChemistry 4th semester series (krishna).pdf
Chemistry 4th semester series (krishna).pdf
 
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
Labelling Requirements and Label Claims for Dietary Supplements and Recommend...
 
DIFFERENCE IN BACK CROSS AND TEST CROSS
DIFFERENCE IN  BACK CROSS AND TEST CROSSDIFFERENCE IN  BACK CROSS AND TEST CROSS
DIFFERENCE IN BACK CROSS AND TEST CROSS
 

Hermite integrators and 2-parameter subgroup of Riordan group

  • 1. Hermite integrators and 2-parameter subgroup of Riordan group Keigo Nitadori keigo@riken.jp RIKEN Advanced Institute for Computational Science July 13, 2016
  • 2. Abstract A general form for the family of 2-step Hermite integrators Up to p-th order derivative of the force is calculated directly to obtain 2(p + 1)-th order accuracy Now, a simple and general form for the predictor is available The mathematics was slightly simplified and generalized from the last talk (99% of the proof was invented by Satoko Yamamoto)
  • 3. Upper triangular Pascal matrix This following upper triangular Pascal matrix appearers frequently in this story: (1) Upas ij = j i , U−1 pas ij = (−1)i+j j i . A (p + 1) × (p + 1) sized one looks like, (2) Upas = 0 0 1 0 2 0 · · · p 0 0 1 1 2 1 · · · p 1 0 0 2 2 · · · p 2 ... ... ... ... ... 0 0 0 · · · p p .
  • 4. Polynomial shift A vector of scaled force (3) F(t) = f (t) h f (1) (t) h2 f (2) (t)/2! ... hn f (p) (t)/n! , where f (n) (t) = dn dtn f (t) (0 ≤ n ≤ p), obeys a transformation F(t + h) = 0 0 1 0 2 0 · · · p 0 0 1 1 2 1 · · · p 1 0 0 2 2 · · · p 2 ... ... ... ... ... 0 0 0 · · · p p F(t) = UpasF(t).(4)
  • 5. Linear equation to solve For even order polynomial coefficients: (5) F+ 0 F− 1 F+ 2 F− 3 ... = 0 0 2 0 4 0 6 0 . . . 0 2 1 4 1 6 1 . . . 0 2 2 4 2 6 2 . . . 0 0 4 3 6 3 . . . ... ... ... ... ... F0 F2 F4 F6 ... . For odd order polynomial coefficients: (6) F− 0 F+ 1 F− 2 F+ 3 ... = 1 0 3 0 5 0 7 0 . . . 1 1 3 1 5 1 7 1 . . . 0 3 2 5 2 7 2 . . . 0 3 3 5 3 7 3 . . . ... ... ... ... ... F1 F3 F5 F7 ... . (definitions: F+ i = 1 2 (FR i + FL i ), F− i = 1 2 (FR i − FL i ).)
  • 6. Generalization We modify the upper triangular Pascal matrix with 2-parameter (a, b), as in (7) [M(a,b)]ij = aj + b i . This is no longer an upper triangular matrix. Our interest is to find the solutions M−1 (2,0) for the even order terms, and M−1 (2,1) for the odd order terms. Unfortunately, explicit forms of their inverses seem to be hardly available.
  • 7. Matrix inverse (conjecture) It turned out that, by multiplying (inverse) Pascal matrices, we can write (8) M−1 (a,b) = U−1 pas M(1/a,−b/a) U−1 pas, or equivalently, (9) M(a,b)U−1 pas −1 = M(1/a,−b/a)U−1 pas. Thus, if we define (10) L(a,b) = M(a,b)U−1 pas, then L−1 (a,b) =L(1/a,−b/a),(11) M−1 (a,b) =U−1 pasL(1/a,−b/a).(12)
  • 8. Interpretation A 2-parameter dense matrix M(a,b) can be factorized into (13) M(a,b) = L(a,b)Upas and we will soon see that L(a,b) is upper triangular. Though explicit matrix elements of L(a,b) in general are hard to obtain (see (3.150) GouldBK.pdf, Sprugnoli, 2006), all we need are M−1 (2,0) =U−1 pasL(1/2,0),(14) M−1 (2,1) =U−1 pasL(1/2,−1/2).(15) Fortunately, matrix elements of U−1 pas, L(1/2,0), and L(1/2,−1/2) are available explicitly.
  • 9. Example (for p = 7, 16th-order) I (16) [L1/2,0]nk = (−2)k (−4)n k n 2n − k − 1 n − k 1 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 −1 8 1 4 0 0 0 0 0 0 1 16 −1 8 1 8 0 0 0 0 0 − 5 128 5 64 − 3 32 1 16 0 0 0 0 7 256 − 7 128 9 128 − 1 16 1 32 0 0 0 − 21 1024 21 512 − 7 128 7 128 − 5 128 1 64 0 0 33 2048 − 33 1024 45 1024 − 3 64 5 128 − 3 128 1 128
  • 10. Example (for p = 7, 16th-order) II (17) [L1/2,−1/2]nk = (−2)k (−4)n 2n − k n − k 1 0 0 0 0 0 0 0 −1 2 1 2 0 0 0 0 0 0 3 8 −3 8 1 4 0 0 0 0 0 − 5 16 5 16 −1 4 1 8 0 0 0 0 35 128 − 35 128 15 64 − 5 32 1 16 0 0 0 − 63 256 63 256 − 7 32 21 128 − 3 32 1 32 0 0 231 1024 − 231 1024 105 512 − 21 128 7 64 − 7 128 1 64 0 − 429 2048 429 2048 − 99 512 165 1024 − 15 128 9 128 − 1 32 1 128
  • 11. L(a,b): 2-parameter lower-triangular matrix We compute the matrix element from its definition: [L(a,b)]ij = M(a,b)U−1 pas ij (18) = ∞ k=0 ak + b i (−1)k+j j k = ∞ k=0 [yi ](1 + y)ak+b · [tk ](−1 + t)j = ∞ k=0 [tk ](−1 + t)j · [yi ](1 + y)b (1 + y)a k =[ti ](1 + t)b −1 + (1 + t)a j = R (1 + t)b , −1 + (1 + t)a ij . R(d(t), h(t)) is a Riordan array of which element is [ti]d(t)h(t)j.
  • 12. Composition and convolution To remove the summation on k, we used a relation ∞ k=0 [tk ] f (t) · [yn ]h(y)g(y)k = ∞ m=0 ∞ k=0 [tk ] f (t) · [yn−m ]hmg(y)k (19) = ∞ m=0 hm[tn−m ] f (g(t)) = ∞ m=0 [ym ]h(y) · [tn−m ] f (g(t)) =[tn ]h(t) f (g(t)) with f (t) = (−1 + t)j, g(y) = (1 + y)a, and h(t) = (1 + t)b.
  • 13. Riordan array is an infinitesimal lower triangle matrix, taking 2 formal power series. The element at the n-th row and the k-th column is (20) R d(t), h(t) nk = [tn ]d(t)h(t)k . Matrix multiplication is given by R d(t), h(t) ◦ R f (t), g(t) = R d(t) · f h(t) , g h(t) .(21) For our 2-parameter matrices L(a,b) =R (1 + t)b , −1 + (1 + t)a , L(c,d) =R (1 + t)d , −1 + (1 + t)c , we have a multiplication L(a,b) L(c,d) = R (1 + t)b (1 + t)ad , −1 + (1 + t)ac = L(ac,ad+b). (22)
  • 14. Group structure L(a,b) forms a subgroup of the Riordan group when a 0. Multiplication: L(a,b) L(c,d) = L(ac,ad+b). Identity: L(1,0) L(a,b) = L(a,b) L(1,0) = L(a,b) Inverse: L−1 (a,b) = L(1/a,−b/a). The following 2 × 2 matrix preserves the same structure. (23) 1 0 b a 1 0 d c = 1 0 ad + b ac .
  • 15. Application to the Hermite integrators Remember: M−1 (2,0) =U−1 pasL−1 (2,0) = U−1 pasL(1/2,0),(24) M−1 (2,1) =U−1 pasL−1 (2,1) = U−1 pasL(1/2,−1/2).(25) All we need is the matrices elements of U−1 pas, L(1/2,0), and L(1/2,−1/2). The first one is very simple: [U−1 pas]ij = (−1)i+j j i .
  • 16. Implementation (p = 7, 16th-order interpolation) I Even order coefficients are computed in F8 F10 F12 F14 = 1 −5 15 −35 0 1 −6 21 0 0 1 −7 0 0 0 1 × (26) 5 64 − 3 32 1 16 0 0 0 − 7 128 9 128 − 1 16 1 32 0 0 21 512 − 7 128 7 128 − 5 128 1 64 0 − 33 1024 45 1024 − 3 64 5 128 − 3 128 1 128 F+ 2 − 1 2 F− 1 F− 3 F+ 4 F− 5 F+ 6 F− 7 .
  • 17. Implementation (p = 7, 16th-order interpolation) II And the odd order ones F9 F11 F13 F15 = 1 −5 15 −35 0 1 −6 21 0 0 1 −7 0 0 0 1 × (27) − 35 128 15 64 − 5 32 1 16 0 0 0 63 256 − 7 32 21 128 − 3 32 1 32 0 0 − 231 1024 105 512 − 21 128 7 64 − 7 128 1 64 0 429 2048 − 99 512 165 1024 − 15 128 9 128 − 1 32 1 128 F+ 1 − F− 0 F− 2 F+ 3 F− 4 F+ 5 F− 6 F+ 7 .
  • 18. Implementation (p = 7, 16th-order interpolation) III Right shift for the next predictor by right-bottom submatrix of Upas: FR 8 FR 9 FR 10 FR 11 FR 12 FR 13 FR 14 FR 15 = 1 9 45 165 495 1287 3003 6435 0 1 10 55 220 715 2002 5005 0 0 1 11 66 286 1001 3003 0 0 0 1 12 78 364 1365 0 0 0 0 1 13 91 455 0 0 0 0 0 1 14 105 0 0 0 0 0 0 1 15 0 0 0 0 0 0 0 1 F8 F9 F10 F11 F12 F13 F14 F15 (28)
  • 19. Matrix element of L(1/2, 0) We use Lagrange inverse formula with w(t) = −1 + √ 1 + t. For t = w(w + 2), φ(t) = 1/(2 + t) satisfies w = tφ(w). [L1/2, 0]nk =[tn ] −1 + (1 + t)1/2 k (29) = [tn ]wk = k n [tn−1 ]tk−1 φ(t)n = k n [tn−k ](2 + t)−n = 2k−2n k n −n n − k = (−1)n+k 2k−2n k n 2n − k − 1 n − k . In case n = 0, it is δk0.
  • 20. Matrix element of L(1/2, −1/2) Similarly, [L1/2, −1/2]nk =[tn ](1 + t)−1/2 −1 + (1 + t)1/2 k (30) = [tn ] wk 1 + w = [tn ] tk 1 + t φ(t)n−1 φ(t) − tφ (t) = [tn ] tk 1 + t 1 (2 + t)n−1 2(1 + t) (2 + t)2 = 2[tn−k ](2 + t)−n−1 = 2k−2n −n − 1 n − k = (−1)n+k 2k−2n 2n − k n − k .
  • 21. Review We have concerned with a 2-parameter matrix (31) [M(a,b)]ij = aj + b i , which has an LU factorized form (32) M(a,b) = L(a,b)Upas where Upas = M(1,0) is an upper triangle Pascal matrix and L(a,b) is a lower triangle matrix expressed in a Riordan array form (33) L(a,b) = R (1 + t)b , −1 + (1 + t)a . Matrix inverse M−1 (a,b) is available in (34) M−1 (a,b) = U−1 pasL−1 (a,b) = U−1 pasL(1/a,−b/a) = U−1 pas M(1/a,−b/a) U−1 pas,
  • 22. Summary We have generalized the 2-step Hermite integrators Predictor and corrector General and explicit matrix elements Simple and economical formulation, easy to implement Mathematical combinatorics have performed an essential role for the derivation and proof Formal power series, Lagrange inverse formula, Riordan array Implementers only need to know about matrix vector multiplications and binomial coefficients, not the full mathematics