ma5233: computational mathematics weizhu bao department of mathematics & center for...
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MA5233: Computational Mathematics
Weizhu Bao
Department of Mathematics & Center for Computational Science and Engineering
National University of SingaporeEmail: [email protected]
URL: http://www.math.nus.edu.sg/~bao
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Computational Science
Third paradigm for– Discovery in Science– Solving scientific &
engineering problems
Interdisciplinary– Problem-driven– Mathematical models– Numerical methods– Algorithmic aspects—
computer science– Programming– Software– Applications, ……
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Dynamics of soliton in quantum physics
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Wave interaction in plasma physics
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Wave interaction in particle physics
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Vortex-pair dynamics in superfluidity
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Vortex-dipole dynamics in superfluidity
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Vortex lattice dynamics in superfluidity
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Vortex lattice dynamics in BEC
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Computational Science
Computational Mathematics – Scientific computing/numerical analysis
Computational PhysicsComputational ChemistryComputational BiologyComputational Fluid DynamicsComputational EnginneringComputational Materials Sciences……...
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Steps for solving a practical problems
Physical or engineering problems Mathematical model – physical laws
Analytical methods – existence, regularity, solution, …
Numerical methods – discretization
Programming -- coding
Results -- computing
Compare with experimental results
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Computational Mathematics
Numerical analysis/Scientific computingA branch of mathematics interested in constructive methodsObtain numerically the solution of mathematical problemsTheory or foundation of computational science– Develop new numerical methods– Computational error analysis:
• Stability• Convergence• Convergence rate or order of accuracy,….
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History
Numerical analysis can be traced back to a symposium with the title ``Problems for the Numerical Analysis of the Future, UCLA, July 29-31, 1948.Volume 15 in Applied Mathematics Series, National Bureau of StandardsBoom of research and applications: Fluid flow, weather prediction, semiconductor, physics, ……
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Milestone Algorithms
1901: Runge-Kutta methods for ODEs1903: Cholesky decomposition1926: Aitken acceleration process
1946: Monte Carlo method1947: The simplex algorithm1955: Romberg method1956: The finite element method
1limif2
)( 1
12
21
aSS
SS
SSS
SSST
n
n
nnnn
nnnn
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Milestone algorithms
1957: The Fortran optimizing compiler 1959: QR algorithm1960: Multigrid method1965: Fast Fourier transform (FFT)1969: Fast matrix manipulations1976: High Performance computing & packages: LAPACK, LINPACK – Matlab1982: Wavelets1982: Fast Multipole method
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Top 10 Algorithms
1946: Monte Carlo method1947: Simplex method for linear programming1950: Krylov subspace iterative methods1951: Decompositional approach for matrix computation1957: Fortran optimizing compiler1959-61: QR algorithms1962: Quicksort1965: Fast Fourier Transform (FFT)1977: Integer relation detection algorithm1982: Fast multipole algorithm
http://amath.colorado.edu/resources/archive/topten.pdf
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Contents
Basic numerical methods– Round-off error – Function approximation and interpolation– Numerical integration and differentiation
Numerical linear algebra– Linear system solvers– Eigenvalue probems
Numerical ODENonlinear equations solvers & optimization
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Contents
Numerical PDE– Finite difference method (FDM)– Finite element method (FEM)– Finite volume method (FVM)– Spectral method
Problem driven research: – Computational Fluid dynamics (CFD)– Computational physics– Computational biology, ……
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How to do it well
Three key factors– Master all kinds of different numerical methods– Know and aware the progress in the applied science– Know and aware the progress in PDE or ODE
Ability for a graduate student– Solve problem correctly– Write your results neatly– Speak your results well and clear – presentation– Find good problems to solve
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Numerical error
Example 1:
Example 2:
Example 3:
Example 4:
!error! no321
!!error! offround14159.414159.311
!error! Truncation2/1)cos(995.02/1.01)1.0cos( 22 xx
!error! off-roundTruncation2/1)cos(
945.02/110.012/333.01)3/1cos(2
2
xx
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Numerical error
Truncation error or error of the method
Round-off error: due to finite digits of numbers in computer
Numerical errors for practical problems– Truncation error– Round-off error– Model error & observation error & empirical error etc.
)O(|x||R(x)|xxPx 42 error Truncation2/1)()cos(
....0000026.014159.314159.3 R
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Absolute error
Absolute error:
Absolute error bound (not unique!!):
....003333.033.03/1*33.03/1
....00159.014.3*14.3
***
e
e
xxexx
0.002or 0.0016*3.14
0.5mm* mm minimum ruler with withlength measure
*******|*||*|
xxxxxxxe
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Relative error
An example:
Relative error:
Relative error bound:
better? is ionapproximat which
5510001110 ** yx yx
*
*
*
* practical in,
** **
x
xx
x
ee
x
xx
x
ee rr
|*|
**
xr
%5.01000
5
|*|
*51000
%1010
1
|*|
*110
*
*
yy
xx
r
r
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Absolute error bounds for basic operations
Suppose Error bounds
** with*, with* yx yyxx
1||
1||||//*/
1||
1||||*
|*)('|*)()(
|*|
|*||*|*/*/
|*||*|**
**
*
***
/
*
***
**)(
2
***
/
***
***
a
aaaxax
a
aaxaxa
xfxfxf
x
yxxyxy
yxyxyx
yxyx
x
xxax
x
xxax
xxf
xyxy
xyxy
yxyx
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Significant digits
An example
Definition: n significant digits
Method:– Write in the standard form – Count the number of digits after decimal
digitst significan 614159.3
digitst significan 314.3
....1415926.3
mnmn
mn
x|xaaaxx
aaaax
1
21
121
105.0|* that such 10.0*
0 with10.0
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Error spreading: An example
Algorithm 1: – Use 4 significant digits for practical computation
– Results
11
0
10
1
1
0
11
0
11
0
1
1
3,2,1,10
edxeeI
nIndxexnxeedxexeI
x
nxnnxxn
n
,3,2,1,1,1 11
0 nInIeI nn
,3,2,1,~
1~
,6321.0~
10 nInII nn
552.77280.02160.01120.01480.0~
98765
1704.02074.02642.03679.06321.0~
43210
n
n
I
n
I
n
!!!2!8,!,105.0 *0
*8
*0
*1
*4*0
nn nn
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Error spreading: An example
Algorithm 2
– Result
– Truncation error analysis
!!!!!9/,/ *9
*9
*0
**1 nnn
0,8,9),ˆ1(1ˆ,0684.0
1010
1
2
1ˆ
10
1
10
1
1
9
1
0
911
0
919
1
0
911
nIn
Ie
I
dxexedxexeIdxxee
nn
x
0684.01035.01121.01268.01455.0ˆ98765
1708.02073.02642.03679.06321.0ˆ43210
n
n
I
n
I
n
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Convergence and its rate
Numerical integration
Exact solution
kxkxxfdxxfI integer somefor )]sin(2[)( with)(0
k
k
k
kI
)cos()sin(2
2
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Numerical methods
Composite midpoint rule
Composite Simpson’s rule
Composite trapezoidal rule
Error estimate
NihixhixN
h ii ,2,1,0,)2/1(,,0
2/1
)()()()( 2/12/122/112/1 NhM xfxfxfxfhII
0 1/2 1 1 1/2 2 2 1/2 1/2
1 2 1 2 1 2 2 1( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )
6 3 3 3 3 3 3 6hS N NI I h f x f x f x f x f x f x f x f x
)(||),(||),(|| 422 hOIIhOIIhOII hS
hT
hM
)(
2
1)()()()(
2
11210 NN
hT xfxfxfxfxfhII
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Numerical results
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Numerical errors
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Observations
Before h0
– Truncation error is too large !!
After h1
– Round-off error is dominated!!
Between h0 and h1
– Clear order of accuracy is observed for the method
We can observe clear convergence rate for proper region of the mesh size!!!
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Numerical Differentiation
Numerical differentiation
The total error 21 )(
~)(,)(
~)(
2
)(~
)(~
2
)()()('
hxfhxfhxfhxf
h
hxfhxf
h
hxfhxfxf
)(:
)('''62
)('''62
)('2
)()()('
2
)(~
)(~
|*|
)('''6
)(''2
)(')()(
2
212
212
21
32
hEhAh
xfh
hxf
h
h
xfh
hxfhxfxf
h
hxfhxfe
xfh
xfh
xfhxfhxf
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Numerical Differentiation
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Numerical Differentiation
Total error depends – Truncation error: – Round-off error:– Minimizer of E(h):
– Double precision:
– Clear region to observe truncation error:
)( 2hO
Matlabin 10 16
02*)(''
)2
(*02)(' 3/12
AhEA
hhAh
hE
516 10*)1(,10 hOA
)1(10* 05 Ohhh
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How to determine order of accuracy
Numerical approximation or method
How to determine p and C??– By plot log E(h) vs log h
ph
b
a
phh hCIIhEhOIIIdxxfI ||:)()(||)(
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How to determine order of accuracy
– By quotation p
p
p
h
h
hC
Ch
II
II2
)2/(||
||
2/