docs: add complexities

Author: sounmind

3sum/evan.js

@@ -47,3 +47,14 @@ var threeSum = function (nums) {
 
   return result;
 };
+
+/**
+ * Time Complexity: O(n^2)
+ * The algorithm involves sorting the input array, which takes O(n log n) time.
+ * The main part of the algorithm consists of a loop that runs O(n) times, and within that loop, there is a two-pointer technique that runs in O(n) time.
+ * Thus, the overall time complexity is O(n log n) + O(n^2), which simplifies to O(n^2).
+ *
+ * Space Complexity: O(n)
+ * The space complexity is O(n) due to the space needed for the sorted array and the result array.
+ * Although the sorting algorithm may require additional space, typically O(log n) for the in-place sort in JavaScript, the dominant term is O(n) for the result storage.
+ */

top-k-frequent-elements/evan.js

@@ -20,3 +20,16 @@ var topKFrequent = function (nums, k) {
 
   return sorted.slice(0, k).map(([num]) => num);
 };
+
+/**
+ * Time Complexity: O(n log n)
+ * - Counting the frequency of each element takes O(n) time.
+ * - Sorting the entries by frequency takes O(n log n) time.
+ * - Extracting the top k elements and mapping them takes O(k) time.
+ * - Therefore, the overall time complexity is dominated by the sorting step, resulting in O(n log n).
+
+ * Space Complexity: O(n)
+ * - The counter map requires O(n) space to store the frequency of each element.
+ * - The sorted array also requires O(n) space.
+ * - Therefore, the overall space complexity is O(n).
+ */