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Romberg
1. ROMBERG
INTEGRATION
Presented by:
Nur Fateha Binti Zakaria (PMM0282/15)
Nuraini Binti Abu Hassan(PMM0306/15)
Lecterur’s Name:
Dr. Ahmad Lutfi Amri Ramli
School of Mathematical Sciences
University Science Malaysia
11800 USM Penang
(2015/2016)
2. Outline
→ Introduction
→Motivation
→Derivation of Romberg Integration
→Algorithm
→ Real Life Application Problem
→Comparison Between Romberg Integration,
Composite Simpson’s rule and Gaussian
Quadrature
→ Advantages and Disadvantages of Romberg
Integration
→Conclusion
3. Chapter 1
Introduction
Integration is the total value, or summation of
𝑓(𝑥)𝑑𝑥 over the range from a to b and can be written as:
𝐼 = 𝑓 𝑥 𝑑𝑥 = lim
𝑛→ ∞
𝑓(𝑥𝑖)∆𝑥
𝑛
𝑖=1
𝑏
𝑎
Where:
f(x) is the integrand
a= lower limit of integration
b= upper limit of integration
∆x=
𝑏−𝑎
𝑛
4. Chapter 1
Introduction
Romberg integration method is named after Werner
Romberg.
This method is an extrapolation formula of the
Trapezoidal Rule for integration. It provides a better
approximation of the integral by reducing the True Error.
5. Chapter 2
Motivation
(Trapezoidal Rule)
Trapezoidal Rule is a technique for approximating the
definite integral:
𝐼 = 𝑓 𝑥 𝑑𝑥
𝑏
𝑎
It work by approximating the area under the graph of the
function as a trapezoid by dividing the area into a number
some intervals with equal width.
Its general equation for n=1 :
T 𝑓, ℎ = 𝑓 𝑥 𝑑𝑥 =
ℎ
2
𝑓 𝑎 + 𝑓 𝑏 −
1
12
𝑓′′(𝑐)ℎ3
𝑏
𝑎
7. Chapter 2
Motivation
(Richardson’s Extrapolation)
Richardson’s extrapolation was named after Lewis Fry
Richardson.
This method is a sequence acceleration method, used to
improve the rate of convergence of a sequence.
Application of an iterative refinement techniques to
improve the error at each iteration. For each iteration:
𝐼 = 𝐼 ℎ + 𝐸(ℎ)
Two approximate integrals are used to compute a third more
accurate integral
Approximate
integral
Truncation error
8. Chapter 3
Derivation of Romberg Integration
(Error in Multiple Segment of Trapezoidal Rule)
𝐸𝑡 = −
(𝑏 − 𝑎)3
12𝑛2
𝑓′′(𝑐)𝑛
𝑖=1
𝑛
where 𝑐 ∈ [𝑎 + 𝑖 − 1 ℎ, 𝑎 + 𝑖ℎ] for each i.
𝑓′′(𝑐)𝑛
𝑖=1
𝑛
is the approximate average of f’’(x) in [a,b].
Because of that, we can say that :
𝐸𝑡 ≈ 𝛼
1
𝑛2
9. Chapter 3
Derivation of Romberg Integration
(Richardson’s Extrapolation for Trapezoidal
Rule)
𝐸𝑡 = 𝑇𝑟𝑢𝑒 𝑉𝑎𝑙𝑢𝑒 (𝑇𝑉) + 𝐴𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑖𝑜𝑛 𝑉𝑎𝑙𝑢𝑒(𝐼 𝑛)
𝐼 = 𝐼 ℎ + 𝐸(ℎ)
True error estimated as:
𝐸𝑡 ≈ 𝛼
1
𝑛2
→ 𝐸𝑡 ≈
𝐶
𝑛2
where C is the proportionality constant.
Then, we have:
𝐶
𝑛2
≈ 𝑇𝑉 − 𝐼 𝑛
10. Chapter 3
Derivation of Romberg Integration
(Richardson’s Extrapolation for Trapezoidal
Rule)
If we double the number of segments where from n →2n.
𝐶
(2𝑛)2
≈ 𝑇𝑉 − 𝐼2𝑛
Then, we get
𝑇𝑉 ≈ 𝐼2𝑛 +
𝐼2𝑛 − 𝐼 𝑁
3
13. Chapter 3
Derivation of Romberg Integration
• General expression:
𝐼 𝑘,𝑗 = 𝐼 𝑘−1,𝑗+1 +
𝐼 𝑘−1,𝑗+1 − 𝐼 𝑘−1,𝑗
4 𝑘−1 − 1
, 𝑘 ≥ 2
where:
k : order of extrapolation.
j : more and less accurate estimate of the integral.
14. Chapter 4
Algorithm
T approximate the integral 𝐼 = 𝑓 𝑥 𝑑𝑥
𝑏
𝑎
, select an integer n>0.
INPUT endpoints a,b; integer n
OUTPUT an array R. (Compute R by rows; only the last 2 rows are saved in storage).
Step 1: Set h=b-a;
𝑅1,1 = ℎ/2(𝑓 𝑎 + 𝑓 𝑏 )
Step 2: OUTPUT (𝑅1,1).
Step 3: For i =2,…….,n do Steps 4-8.
Step 4: Set 𝑅2,1 =
1
2
[𝑅1,1 + ℎ 𝑓(𝑎 + 𝑘 − 0.5 ℎ)].2 𝑖−2
𝑘=1
(Approximation from Trapezoidal method)
Step 5: For j=2,….., i
set 𝑅2,𝑗 = 𝑅2,𝑗−1 +
𝑅2,𝑗−1−𝑅1,𝑗−1
4 𝑗−1−1
. 𝐸𝑥𝑡𝑟𝑎𝑝𝑜𝑙𝑎𝑡𝑖𝑜𝑛 .
Steps 6: OUTPUT ((𝑅1,1) for j=1,2,……..i).
Step 7: Set h=h/2.
Step 8: For j=1,2,…..i set 𝑅1,𝑗. = 𝑅2,𝑗. (𝑈𝑝𝑑𝑎𝑡𝑒 𝑟𝑜𝑤 1 𝑜𝑓 𝑅)..
Step 9: STOP
15. Chapter 6
Real Life Application Problem
(Composite Simpson’s Rule)
𝑓(𝑡)
𝑏
𝑎
𝑑𝑡 =
ℎ
3
𝑓 𝑎 + 2 f 𝑡2𝑗
𝑗=1
𝑛 2−1
+ 4 f 𝑡2𝑗−1
𝑗=1
𝑛 2
+ 𝑓 𝑏 −
𝑏 − 𝑎
180
ℎ4
𝑓 4
𝜇 .
Using a=8, b=30 and n=8.
Solution: 11061.3m
Absolute error= 0.019303m
16. Chapter 6
Real Life Application Problem
(Gaussian Quadrature)
𝑓 𝑡 𝑑𝑡
𝑏
𝑎
=
𝑏 − 𝑎
2
𝑓
𝑏 − 𝑎
2
𝑡 +
𝑏 + 𝑎
2
𝑑𝑡.
1
−1
Using a=8, b=30 and n=8.
Solution: 11061.336m
Absolute error= 3.637x10−12m
17. Chapter 6
Real Life Application Problem
(Romberg Integration)
Step 1: Calculate the estimate of the integral using 1,2,4,8
subintervals using recursive integral:
𝑇 0 =
𝑏 − 𝑎
2
(𝑓 𝑎 + 𝑓 𝑏 )
𝑇 𝑗 =
𝑇(𝑗 − 1)
2
+ ℎ 𝑓[𝑥2𝑘−1]
2 𝑗−1
𝑘=1
j=1,2,3…
where ℎ =
𝑏−𝑎
2 𝑗 ,
𝑥 𝑘= 𝑥0 + 𝑘ℎ.
j T(j) Partition(𝟐𝒋)
0 118.68.348 1
1 11266.374 2
2 11112.821 4
3 11074.221 8
4 11065.933 16
18. Chapter 6
Real Life Application Problem
(Romberg Integration)
• Step 2 to 4: Find the first, second and third extrapolation:
𝐼 𝑘,𝑗 = 𝐼 𝑘−1,𝑗+1 +
𝐼 𝑘−1,𝑗+1 − 𝐼 𝑘−1,𝑗
4 𝑘−1 − 1
, 𝑘 ≥ 2
j 1st order 2nd order 3rd order
1 11065.716 11061.364 11061.335
2 11061.636 11061.335
3 11061.354
4 11063.170
19. Chapter 6
Real Life Application Problem
(Romberg Integration)
Segment (n) First order Second order Third order
1 11868.348
11065.716
2 11266.374 11061.364
11061.636 11061.335
4 11112.821 11061.335
11061.354
8 11074.221
Compare to exact solution, the absolute error is 0.0001046𝑚.
21. Chapter 8
Advantages and Disadvantages of Romberg
Integration
Advantages
Takes less computer
time compare to others.
Not difficult to
translates it by any
programming language
because it equally
interval.
User can easily pick a
suitable step size and
order.
Disadvantages
x Cannot deals with
unequally interval.
22. Chapter 9
Conclusion
Romberg integration is a powerful and quite a simple
method
Romberg integration method is the best method to solve
the integration problem because it have better accuracy
than other methods except for Gauss Quadrature method.
In aspects of computer timing, Romberg Integration is
better than Gauss Quadrature and Composite Simpson’s
rule