runtime complexity >>> time complexity

Author: nickolashkraus

src/coin_change.py

@@ -11,8 +11,8 @@
 approach).
 
 Both the dynamic programming (bottom-up) and memoization (top-down) solutions
-have O(S * n) runtime complexity where 'S' is the amount and 'n' is the number
-of coins.
+have O(S * n) time complexity where 'S' is the amount and 'n' is the number of
+coins.
 
 This problem should not be confused with the coin change problem where you need
 to find the number of different ways to make up an amount using the given coins

src/flood_fill.py

@@ -43,8 +43,8 @@
         if (0 <= i < m and 0 <= j < n):
             ...
 
-This solution has O(n) runtime complexity and O(n) space complexity, since, in
-the worst case, we will need to visit every pixel in the image.
+This solution has O(n) time complexity and O(n) space complexity, since, in the
+worst case, we will need to visit every pixel in the image.
 """
 
 from collections import deque

src/linked_list_cycle.py

@@ -10,7 +10,7 @@
 
     See: https://en.wikipedia.org/wiki/Cycle_detection
 
-This solution has O(n) runtime complexity and O(1) space complexity.
+This solution has O(n) time complexity and O(1) space complexity.
 """
 
 from src.classes import ListNode

src/maximum_subarray.py

@@ -4,7 +4,7 @@
 https://leetcode.com/problems/maximum-subarray
 
 NOTES
-  * This problem can be solved in O(n) runtime complexity and O(1) space
+  * This problem can be solved in O(n) time complexity and O(1) space
     complexity using Kadane's algorithm.
 
 The key insight is that at each position (i), you only need to decide whether