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'''Interesting Calculation Methods'''
 
'''Interesting Calculation Methods'''
  
One interesting way to calculate pi is by throwing a bunch of sticks on the ground, on a grid of parallel lines.  By using the length of a stick, the size of the gaps between the lines, and the number of sticks that are crossing a line, we can calculate an estimate for pi.  This experiment is called Buffon’s needle, and you can try it for yourself here: https://ogden.eu/pi/
+
One interesting way to calculate π is by throwing a bunch of sticks on the ground, on a grid of parallel lines.  By using the length of a stick, the size of the gaps between the lines, and the number of sticks that are crossing a line, we can find an estimate for π.  This experiment is called Buffon’s needle, and you can try it for yourself here: https://ogden.eu/pi/
  
 
(insert image here)
 
(insert image here)
  
If we normalize the distance between lines to 1, we can draw each stick as follows, where D is the distance of its midpoint from the nearest line and theta is the angle of the stick from parallel.  The needle will hit the line if D <= 1/2sin(theta).
+
If we normalize the distance between lines to 1, we can draw each stick as follows, where D is the distance of its midpoint from the nearest line and θ is the angle of the stick from parallel.  The needle will hit the line if D 1/2sin(θ).
  
 
(insert image)
 
(insert image)
  
In order to determine how often this happens, we can plot 1/2sin(theta) on a graph.  As you can see, D can range from 0 to ½ and theta can range from 0 to pi radians.  The blue area of the graph is where D <= 1/2sin(theta).  So in order to find the probability that D <= 1/2sin(theta), we can take the area of the blue curve divided by the area of the rectangle of total possibilities.   
+
In order to determine how often this happens, we can plot 1/2sin(θ) on a graph.  As you can see, D can range from 0 to ½ and θ can range from 0 to π radians.  The blue area of the graph is where D 1/2sin(θ).  So in order to find the probability that D 1/2sin(θ), we can take the area of the blue curve divided by the area of the rectangle of total possibilities.   
 
   
 
   
 
(insert image)
 
(insert image)
 
    
 
    
The area under the blue curve, found using integration, is 1, and the area of the big rectangle is pi/2.  So (needles touching a line)/(total needles) ~= 1/(pi/2) = 2/pi.  We can input our real data and rearrange this equation to approximate pi.  This kind of calculation isn't very practical, but if you get too frustrated with infinite series and calculus then we've found throwing sticks on the ground to be quite cathartic.
+
The area under the blue curve, found using integration, is 1, and the area of the enclosing rectangle is π/2.  So (needles touching a line)/(total needles) 1/(π/2) = 2/π.  We can input our real data and rearrange this equation to approximate pi.  This kind of calculation isn't very practical, but if you get too frustrated with infinite series and calculus then we've found throwing sticks on the ground to be quite cathartic.
  
  

Revision as of 12:41, 17 November 2022

Introduction

Pi is possibly the most famous constant in mathematics. Interestingly, it has also been one of the most difficult to calculate throughout history and this has lead to some very creative solutions as problems such as this usually do.

- This needs revision

Basic Approximations

- Is this necessary as a separate heading? Should this come at a different spot in this page? - 22/7 - Rolling a wheel out on a road to determine pi - Bounds between 3 and 4 and how they are determined

Early Calculation

In some of the earliest historical records known of pi, it was recorded to be somewhere roughly between 3 and 4. Specifically, the Babylonians nearly 4,000 years ago seem to have believed pi was 25/8 or 3.125. The Egyptians (at around the same time) noted the pi was (16/9)^2 or roughly 3.16. The most well-known early algorithm for computing pi is popularly known from the mathematician Archimedes (and a similar, iterative method was used in China known as Liu Hiu's Pi Algorithm). Archimedes knew that pi was described as the ratio of a circle's circumference to its diameter and to him the most significant limiting factor in calculating pi was knowing the exact circumference of some theoretical circle defined by a known radius. He could however find a close approximation by creating polygons both inside and outside the circle with known side lengths to calculate pi. This grew ever closer to pi the more sides were added to these polygons but took a significant amount of work. There is a famous example of a man who dedicated decades to the calculation of pi using this method, his name was Ludolph van Ceulen. He calculated pi up to 35 places using this method and when he died the digits for his upper and lower bounds of pi (from the inner and outer polygons around the circle in the approximation) were inscribed on his tombstone.

Newtonian Calculation

Might want to find more references on this or use this video as the reference: https://www.youtube.com/watch?v=gMlf1ELvRzc Also might consider renaming this section "Infinite Series Calculation"

- Pascal's Triangle realization - Integration of unit circle - Infinite series and convergence rate optimization to find pi - Fun quote by Newton: "I am ashamed to tell you to how many figures I carried these computations, having no other business at the time."

Modern Calculation

- Algorithms by a computer - Is there a closed form known to find a particular digit of pi? - Reference the book Contact by Carl Sagan? - Largest number of digits of pi currently known

Interesting Calculation Methods

One interesting way to calculate π is by throwing a bunch of sticks on the ground, on a grid of parallel lines. By using the length of a stick, the size of the gaps between the lines, and the number of sticks that are crossing a line, we can find an estimate for π. This experiment is called Buffon’s needle, and you can try it for yourself here: https://ogden.eu/pi/

(insert image here)

If we normalize the distance between lines to 1, we can draw each stick as follows, where D is the distance of its midpoint from the nearest line and θ is the angle of the stick from parallel. The needle will hit the line if D ≤ 1/2sin(θ).

(insert image)

In order to determine how often this happens, we can plot 1/2sin(θ) on a graph. As you can see, D can range from 0 to ½ and θ can range from 0 to π radians. The blue area of the graph is where D ≤ 1/2sin(θ). So in order to find the probability that D ≤ 1/2sin(θ), we can take the area of the blue curve divided by the area of the rectangle of total possibilities.

(insert image)

The area under the blue curve, found using integration, is 1, and the area of the enclosing rectangle is π/2. So (needles touching a line)/(total needles) ≈ 1/(π/2) = 2/π. We can input our real data and rearrange this equation to approximate pi. This kind of calculation isn't very practical, but if you get too frustrated with infinite series and calculus then we've found throwing sticks on the ground to be quite cathartic.


Applications

- Drink containers? - Super computing benchmarks? - Car tires? - Others?

References

- Likely clean these up to look more APA

https://en.wikipedia.org/wiki/Pi https://en.wikipedia.org/wiki/Approximations_of_π - might need to use this more heavily https://en.wikipedia.org/wiki/Liu_Hui%27s_π_algorithm https://en.wikipedia.org/wiki/Chronology_of_computation_of_π - unused currently, might be a useful graphic https://mathshistory.st-andrews.ac.uk/Biographies/Van_Ceulen/ https://www.universetoday.com/110331/happy-pi-day-5-ways-nasa-uses-pi/#:~:text=In%20basic%20mathematics%2C%20Pi%20is,flight%2C%20to%20name%20a%20few. - unused currently, might be useful

Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman