1.1.7 - Example Problems
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Interactive Audio Lesson
Listen to a student-teacher conversation explaining the topic in a relatable way.
Introduction to Finite Differences
🔒 Unlock Audio Lesson
Sign up and enroll to listen to this audio lesson
Today we're going to talk about finite differences. Can anyone tell me what a finite difference is?
Isn’t it just the change in the function values over a small interval?
Exactly! A finite difference, like \( \Delta f(x) \), measures the change in a function as we increment x by a small amount, denoted as h.
But how does that help us in the real world?
Great question! Finite differences are widely used in numerical methods to approximate derivatives and in interpolation, which allows us to estimate unknown values.
Can you give an example?
Of course! Let’s consider the example where we have a function defined at discrete points, like a table of values.
At the end of this discussion, remember: finite differences are the building blocks for numerical methods, especially in interpolation.
First Forward Differences
🔒 Unlock Audio Lesson
Sign up and enroll to listen to this audio lesson
Let’s calculate the first forward differences using our provided data. Our points are x = 1, 2, 3, 4 with values 1, 8, 27, and 64.
How do we go about that?
We use the formula \( \Delta f(1) = f(2) - f(1) \). So, it would be \( 8 - 1 = 7 \). What’s the next one?
That would be \( \Delta f(2) = f(3) - f(2) = 27 - 8 = 19 \)!
Right! Now onto \( \Delta f(3) \). Who can compute that?
It’s \( 64 - 27 = 37 \).
Fantastic! Now we have our first forward differences: 7, 19, and 37. Let’s summarize these findings.
Second Forward Differences
🔒 Unlock Audio Lesson
Sign up and enroll to listen to this audio lesson
Now, let’s find the second forward differences. Who remembers how to do that?
Do we subtract the first forward differences?
Yes! So, we calculate \( \Delta^2 f(1) = \Delta f(2) - \Delta f(1) = 19 - 7 = 12 \). What’s next?
That means \( \Delta^2 f(2) = 37 - 19 = 18 \).
Exactly! So, our second forward differences are 12 and 18. Let’s summarize everything we’ve learned today about forward differences.
Introduction & Overview
Read summaries of the section's main ideas at different levels of detail.
Quick Overview
Standard
In this section, we delve into example problems that illustrate how to compute first and second forward differences for given function values at equidistant points, showcasing the practical use of finite differences in function approximation.
Detailed
Detailed Summary
In the section titled 'Example Problems', we explore practical applications of finite differences by calculating the first and second forward differences for a set of function values at equally spaced points. The data provided in the example covers several discrete values of a function f(x), allowing us to illustrate the methodology for applying finite difference formulas.
The forward differences are computed to show the incremental change in function values as we move along the x-axis. This is important in numerical analysis as it lays the groundwork for constructing polynomial interpolating functions, which can be used for approximating unknown function values.
The calculations will highlight the use of the formula for first forward difference:
\[ \Delta f(x) = f(x+h) - f(x) \]
and for second forward differences:
\[ \Delta^2 f(x) = \Delta(\Delta f(x)) = f(x + 2h) - 2f(x + h) + f(x) \]
In summary, these calculations not only provide insight into the behavior of functions but also reinforce the significance of finite differences in the realm of numerical methods.
Youtube Videos
Audio Book
Dive deep into the subject with an immersive audiobook experience.
First Forward Difference Calculations
Chapter 1 of 2
🔒 Unlock Audio Chapter
Sign up and enroll to access the full audio experience
Chapter Content
Example 1: Given 𝑓(𝑥) at equally spaced points:
x f(x)
1 1
2 8
3 27
4 64
Find the first and second forward differences.
Solution:
• Δ𝑓(1)= 8− 1 = 7
• Δ𝑓(2)= 27− 8 = 19
• Δ𝑓(3)= 64− 27 = 37
Detailed Explanation
In this example, we are given a set of function values at specific points along an x-axis. Our goal is to calculate the first forward differences, which measure how much the function values change as we move from one point to the next.
- Starting with the pairs of points:
- For x=1, 𝑓(1)=1 and 𝑓(2)=8. The first forward difference Δ𝑓(1) is the change in function values: 8 - 1 = 7.
- For x=2, 𝑓(2)=8 and 𝑓(3)=27. The first forward difference Δ𝑓(2) is: 27 - 8 = 19.
- For x=3, 𝑓(3)=27 and 𝑓(4)=64. The last forward difference Δ𝑓(3) is: 64 - 27 = 37.
So, we find the first differences to be 7, 19, and 37 for the first three intervals.
Examples & Analogies
Imagine you are tracking the growth of a plant over time. If the plant is 1cm tall after the first day, 8cm after the second day, 27cm after the third day, and 64cm after the fourth day, the first forward differences tell you how much the plant's height increases each day: it grew 7cm from day 1 to day 2, 19cm from day 2 to day 3, and 37cm from day 3 to day 4.
Second Forward Difference Calculations
Chapter 2 of 2
🔒 Unlock Audio Chapter
Sign up and enroll to access the full audio experience
Chapter Content
• Δ2𝑓(1) = 19 −7 = 12
• Δ2𝑓(2) = 37 −19 = 18
Detailed Explanation
Now we find the second forward differences, which tell us how the first differences are changing:
- For the first second difference Δ2𝑓(1): we take Δ𝑓(2) and Δ𝑓(1): 19 - 7 = 12.
- For the second second difference Δ2𝑓(2): we take Δ𝑓(3) and Δ𝑓(2): 37 - 19 = 18.
This shows that the 'growth rate' of the function itself is increasing, as the second forward differences are indicating how much the first differences are growing.
Examples & Analogies
Continuing with the plant growth analogy, the second forward differences reveal how quickly the growth rate is increasing. If the plant grew by 12cm more growth after day 2 compared to day 1, and by an additional 6cm more growth after day 3 compared to day 2, it indicates that the plant is not just growing taller but is also growing faster over time.
Key Concepts
-
Finite Differences: Discrete changes in function values.
-
First Forward Difference: The difference calculated from two consecutive outputs of a function.
-
Second Forward Difference: The change from the first forward differences.
Examples & Applications
Example 1: Given f(x) values at x = {1, 2, 3, 4}: Calculate 1st forward differences and 2nd forward differences.
Example 2: Applications of forward differences in polynomial interpolation to estimate f(2.5).
Memory Aids
Interactive tools to help you remember key concepts
Rhymes
Finite differences rise, in numerical skies, estimating function lows, wherever data flows.
Stories
Imagine a traveler moving from point to point on a map. The changes in their journey help us predict the best path ahead, much like finite differences help us estimate function behavior.
Memory Tools
The acronym DFF can help you remember: Delta First Forward for Finite differences.
Acronyms
FIND
Finite Interpolation Numbers through Differences.
Flash Cards
Glossary
- Finite Difference
A mathematical expression representing the change in the value of a function as its input is incremented by a small amount.
- Forward Difference
A finite difference that calculates the change in a function value at two consecutive points.
- Second Forward Difference
The finite difference calculated from the first forward differences.
- Interpolation
The method of estimating unknown values that fall within the range of a discrete set of known values.
Reference links
Supplementary resources to enhance your learning experience.