Preface

Im just kinda taking notes and taking the slides from a yt vid so i can maybe remember stuff / copy stuff for later
also to partially increase my typing speed without looking at my keyboard and idk maybe get better at typing given i dont use the standerd asdf jkl; hand position
rather i do shift+awr jop' layout and use alot of range for my index fingers and spread them out its not optimal but this is realy all i need coz im a gamer lmfao

also [] is my ring finger and all numbers are my ring fingers wish i had an ergo keyboard lol

https://www.youtube.com/watch?v=RBSGKlAvoiM&ab_channel=freeCodeCamp.org

Example page, and literal first 6 minutes of the video

Why Data Structures?

• They are essential Ingredients in creeating fast and powerfull algorithms.
• they help to manage and organize data.
• they make code cleaner and easier to understand. (even if it lowers efficiency)

Abstract Data Types VS. Data Structures

Abstract data type

An abstract data type (ADT) is an abstraction of a data structure which provides only the interface to which a data structure must adhere to which a data structure must adhere to.

the interface does not give any specific details about how something should be implemented or in what programming language.

Examples

Complexity Analysis

As programmers, we often find ourselves asking the same two questions over and over again:

1. How much time does this algorithm need to finish?
   1. if it takes the length of the universe to complete its useless
2. How much space does this algorithm need for its computation?
   1. likewise if it takes the entire bit size of internet its also useless

Big-O Notation 

_{yes it's the letter O not the number 0}
Big-O Notation gives an upper bound of the complexity in the worst case, helping to quantify performance as the input size becomes arbitrarily large.

Ex, looking for the number 7 in a list, and it occurring last. this scenario would include all values in that list, if allocating space per number, you'd allocate the entire list.

n - The size of the input
Complexities ordered in from smallest to largest

Big-O Properties

_{*c = constant, n = number}
if your constant is large, like 2 billion its likely to have substantial run-time penalties

_{Big-O Examples}

let f be a function that describes the running time of a particular algorithm for an input of size n:

f(n) = 7log(n)^3 + 15(n)^3 + 15n^2 + 8 
_{this is Quadratic because there's ^3 then ^2 then ^1 or a regular value at the end}
O(f(n)) = O(n3)

The following run in constant time: O(1)

_{:= is used to explicitly signal the initialization or assignment of a new value to a variable. and is usually used for pseudo code
additionally since c can be run in a single cycle and the while loop is always the same it will always run 11 times,}

The Following runs in Linear time: O(n)

:0
While i < n Do
  i = i + 1

:0
While i < n Do
  i = i + 3

since i + 1 is 1/3 the speed of i + 3 we finish in appx 1/3 the time, regardless it will allways finish at a constant rate, if we were to plot it on a graph its a straight line.

Both of the following run in Quadratic time the first may be obvious since n work done n times is n*n = O(n^2), but what about the second one?

--still pseudo code but more lua like coz end = end of loop
for (i := 0 ; i < n; i = i + 1 )
  for (j := 0 ; j < n; j = j + 1 )
  end
end
f(n) = n*n = n^2 , O(f(n)) = O(n^2)

for (i := 0 ; i < n; i = i + 1 )
  for (j := i ; j < n; j = j + 1 )
------------^ replaced 0 with i
  end
end

_{encase you didn't understand the pseudo code. like how i didn't... they are declaring what i is in the for loop, then declaring the condition of the for loop if i < n then  giving the condition once the loop ends. eg in python}

n = 10  # Example value for n
for i in range(n):  # i goes from 0 to n-1. i is implicity declaired as 0 unless previously stated. (it hasnt)
    for j in range(n):  # j goes from 0 to n-j. again implicitly declairing the value of j
        pass  # replace with whatever ud do after the math.

n = 10  # Example value for n

def foo():
    global i, j, exitcond
    while not exitcond:
        if i < n:
            j = i
            while j < n:
                # Replace this with whatever you'd do after the math.
                pass
                j += 1
            i += 1
        else:
            exitcond = True
            print("This is now exiting the exit condition if loop.")

# Initial values
i = 0
j = 0
exitcond = False

foo()

for a moment focus on the second loop....
Since i goes from [0,n) the ammount of looping done is directly determined by what i is.
Remark that if i =0, we do n work, we do n-2 work, etc...

so the question then becomes what is: 
(n) + (n-1) + (n-2) + (n-3_ + ... + 3 + 2 + 1 ??
Remarkabley this turns out to be n(n+1)/2, so
 O(n(n+1)/2 = O(((n^2) / 2) + n/2 = (0(n2)

for (i := 0 ; i < n; i = i + 1 )
  for (j := i ; j < n; j = j + 1 )

since