Comparing element vs number of operations Above example is O(n) - aka Linear Time

Above example is O(1) - aka Constant Time

O(3) is still Constant time. It's still denotated as O(1) using bigO notation

Assume we have to perform every operation (the objective happens at the end)

Drop the constants from the Big O calculation. Because as n gets larger, the constants become relatively less significant.

For example, if there are 2 inputs, each used in different for loops, we need to use two terms. i.e. O(a + b)

For example, if the total complexity is O(n + n^2), then drop the O(n) for just O(n^2) because O(n) will become relatively less important as n increases

Interesting note:

• O(1) - no loops
• O(log(n)) - searching when the items are sorted
• O(n) - single loop
• O(n*log(n)) - sorting
• O(n^2) - nested loops
• O(2^n) is common when doing recurrsion
• O(n!) is when a loop is bing added for every element

• Arrays
• Linked Lists
• Stacks
• Queues
• Trees
• Tries
• Graphs
• Hash Tables

• Sorting
• Searching
• Recurrsion
• Dynamic Programming

• Access
• Searching
• Insertion
• Deletion
• Traversal (Access each point exactly once for processing)
• Sorting

• Readability
• Space Complexity
• Time Complexity