Big-O Notation

1. Linear Time O(n)

def linear_search(array, element):
    for i in range(len(array)):
        if array[i] == element:
            return i

2. Logarithm Time O(log n)

def binary_search(array, element):
    low, high = 0, len(array)-1
    while low <= high:
        mid = (high + low) // 2
        val = array[mid]
        if val == element:
            return mid
        elif val < element:
            low = mid + 1
        else:
            high = mid - 1

3. Polynomial Time O(n^number)

This example for O(n^2)

def bubble_sort(collection):
	length = len(collection)
	for i in range(length - 1):
		swapped = False
		for j in range(length - 1 - i):
			if collection[j] > collection[j+1] :
				swapped = True
				collection[j], collection[j+1] = collection[j+1] , collection[j]
		if not swapped:
			break
	return collection

4. Exponential Time O(number^n)

This example for O(3^n)

from itertools import chain, combinations
def subsets(iterable):
	s = list(iterable)
	return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
print(list(sunsets(['a', 'b', 'c'])))

Sort

• bubble sort
• bead sort

Cryptograpy

• ceasar
• a1z26
• one time pad cipher

Search

• binary search
• linear search
• search insert
• search range
• first occurrence
• last occurrence
• top one
• two sum

Other

• zigzag
• move zearos
• limit
• is isomophic
• remove min
• rotate