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This approach involves checking all possible combinations or permutations to find the solution. Although it's straightforward and easy to implement, it may not be the most efficient way due to its high time complexity.
Time Complexity: O(n^2) - where n is the size of the input.
Space Complexity: O(1) - constant space usage as no additional data structures are used.
1using System;
2
3class Program {
4 static void Main() {
5 solve();
6 }
7
8 static void solve() {
9 // Your brute-force logic here
10 Console.WriteLine("Solution not implemented yet");
11 }
12}This C# program defines the structure where a brute-force logic can be implemented to solve the problem. The solution is currently only a placeholder.
This approach uses a hash map to store intermediate results or counts, allowing for quicker lookup and more efficient processing, reducing the time complexity compared to the brute-force method.
Time Complexity: O(n) - linear time if hash lookups are constant time.
Space Complexity: O(n) - due to storage in the hash map.
1using System.Collections.Generic;
class Program {
static void Main() {
solve();
}
static void solve() {
// Implement optimized logic with hash map technique
Console.WriteLine("Solution not implemented yet");
}
}The C# code structure here encourages using Dictionary for optimizing solution efficiency beyond brute-force.