"""
Suppose that we analyze an algorithm and decide that it has the following relationship between the input size, n, and the number of operations needed to carry out the algorithm:

N = n^2 + 5
Where n is the input size and N is the number of operations required.

For example, if we gave this algorithm an input of 22, the number of required operations would be 2^2 +5 or simply 9.



The thing to notice in the above exercise, is this: In n^2 + 5n 
2
 +5, the 55 has very little impact on the total efficiency—especially as the input size gets larger and larger. Asking the computer to do 10,005 operations vs. 10,000 operations makes little difference. Thus, it is the n^2n 
2
  that we really care about the most, and the + 5+5 makes little difference.

Most of the time, when analyzing the efficiency of an algorithm, the most important thing to know is the order. 
In other words, we care a lot whether the algorithm's time-complexity has a linear order or a quadratic order (or some other order). 
This means that very often (in fact, most of the time) when you are asked to analyze an algorithm, you can do so by making an approximation that significantly simplifies things. 
In this next video, Brynn will discuss this concept and show how it's used with Big O Notation.
https://youtu.be/zOenWuEDhFo
"""

"""
Suppose that we analyze an algorithm and decide that it has the following relationship between the input size, n, and the number of operations needed to carry out the algorithm:

N = n^2 + 5
Where n is the input size and N is the number of operations required.

For example, if we gave this algorithm an input of 22, the number of required operations would be 2^2 +5 or simply 9.



The thing to notice in the above exercise, is this: In n^2 + 5n 
2
 +5, the 55 has very little impact on the total efficiency—especially as the input size gets larger and larger. Asking the computer to do 10,005 operations vs. 10,000 operations makes little difference. Thus, it is the n^2n 
2
  that we really care about the most, and the + 5+5 makes little difference.

Most of the time, when analyzing the efficiency of an algorithm, the most important thing to know is the order. 
In other words, we care a lot whether the algorithm's time-complexity has a linear order or a quadratic order (or some other order). 
This means that very often (in fact, most of the time) when you are asked to analyze an algorithm, you can do so by making an approximation that significantly simplifies things. 
In this next video, Brynn will discuss this concept and show how it's used with Big O Notation.
https://youtu.be/zOenWuEDhFo
"""

Suppose that we analyze an algorithm and decide that it has the following relationship between the input size, n, and the number of operations needed to carry out the algorithm:  N = n^2 + 5 Where n is the input size and N is the number of operations required.  For example, if we gave this algorithm an input of 22, the number of required operations would be 2^2 +5 or simply 9.    The thing to notice in the above exercise, is this: In n^2 + 5n  2  +5, the 55 has very little impact on the total efficiency—especially as the input size gets larger and larger. Asking the computer to do 10,005 operations vs. 10,000 operations makes little difference. Thus, it is the n^2n  2   that we really care about the most, and the + 5+5 makes little difference.  Most of the time, when analyzing the efficiency of an algorithm, the most important thing to know is the order.  In other words, we care a lot whether the algorithm's time-complexity has a linear order or a quadratic order (or some other order).  This means that very often (in fact, most of the time) when you are asked to analyze an algorithm, you can do so by making an approximation that significantly simplifies things.  In this next video, Brynn will discuss this concept and show how it's used with Big O Notation. https://youtu.be/zOenWuEDhFo