big o complexity section edits

README.md

@@ -273,6 +273,18 @@ Coding examples:
 - print "hello";
 
 
+**O(log n)**
+
+O(log n) is slightly more difficult to explain. 
+One good example of a O(log n) problem is when searching up a phone book. Even if the phone book is thick, you would not need to search every name, but you just have to look for the name under the correct alphabet. 
+
+Reducing the problem size with every iteration
+
+Coding examples:
+- Binary search
+- Finding largest/smallest number in a binary search tree
+
+
 **O(N)**
 
 Performance will grow linearly and in direct proportion to the size of the input data set.
@@ -282,7 +294,18 @@ Coding examples:
 - Traversing a linked list
 - Linear Search
 
-**O(N)^2**
+
+**O(n log n)**
+
+To better understand it, think of it as O(N) and O(log n). An example of such a notation is the Quick sort when we divide the array into two parts and each time it takes O(N) time to find a pivot element. 
+
+Coding examples:
+- Merge Sort
+- Heap Sort
+- Quick Sort
+
+
+**O(N^2)**
 
 Performance is directly proportional to the square of the size of the input data set.
 This is common with nested iterations over the data set.
@@ -294,32 +317,14 @@ Coding examples:
 - Insertion Sort
 -	Selection Sort
 
-**O(2N)**
 
-Performance doubles with each additon to the input data set. The growth curve of an O(2N) function is exponential.
+**O(2^N)**
+
+Performance doubles with each additon to the input data set. The growth curve of an O(2^N) function is exponential.
 
 Coding examples: 
 - Recursive calculation of Fibonacci numbers
 
-**O(log n)**
-
-O(log n) is slightly more difficult to explain. 
-One good example of a O(log n) problem is when searching up a phone book. Even if the phone book is thick, you would not need to search every name, but you just have to look for the name under the correct alphabet. 
-
-Reducing the problem size with every iteration
-
-Coding examples:
-- Binary search
-- Finding largest/smallest number in a binary search tree
-
-**O(n log n)**
-
-To better understand it, think of it as O(N) and O(log n). An example of such a notation is the Quick sort when we divide the array into two parts and each time it takes O(N) time to find a pivot element. 
-
-Coding examples:
-- Merge Sort
-- Heap Sort
-- Quick Sort
 
 ## Big-Ω (Big-Omega) notation
 It describes: