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This approach utilizes hash maps (dictionaries in Python) to achieve optimal time and space complexity for operations such as insertion, deletion, and lookup. By storing elements as keys in a hash map, we benefit from average-case O(1) time complexity for these operations.
Time Complexity: O(1) on average for search, insert, and delete due to the hash map.
Space Complexity: O(n), where n is the number of elements stored.
1using System;
2using System.Collections.Generic;
3
4class MySet {
5 private Dictionary<int, bool> hashMap;
6
7 public MySet() {
8 hashMap = new Dictionary<int, bool>();
9 }
10
11 public void Insert(int key) {
12 hashMap[key] = true;
13 }
14
15 public bool Search(int key) {
16 return hashMap.ContainsKey(key);
17 }
18
19 static void Main() {
20 MySet mySet = new MySet();
21 mySet.Insert(10);
22 Console.WriteLine("Search for 10: " + mySet.Search(10)); // Output: True
23 Console.WriteLine("Search for 20: " + mySet.Search(20)); // Output: False
24 }
25}
26C# provides the Dictionary class, similar to Java's HashMap, allowing us to manage our key-value pairs efficiently.
This approach leverages sorted arrays to perform efficient binary searches. Operations are optimized for scenarios requiring sorted data, such as when frequent minimum/maximum queries are performed.
Time Complexity: O(n log n) for insertion (due to sorting), O(log n) for search (binary search).
Space Complexity: O(n).
1
In C, we maintain sorted arrays using qsort after each insertion. Searches are performed with a custom binary search function for efficiency.