Sponsored
Sponsored
This approach involves breaking down the problem into smaller subproblems, solving each of them independently, and combining their results in an efficient way. It often uses recursion to handle each subproblem.
Time Complexity: T(n) = 2T(n/2) + O(n) => O(n log n)
Space Complexity: O(log n) due to recursion stack space.
1function solveProblem(parameters) {
2 // Base case solution
3 if (baseCondition) {
4 return;
5 }
6 // Divide
7 let mid = calculateMiddle;
8 // Conquer
9 solveProblem(leftPart);
10 solveProblem(rightPart);
11 // Combine
12 // combine left and right results
13}
14
15solveProblem(initialParameters);JavaScript implementation that recursively divides the problem into smaller parts, solves them, and combines their solutions.
This approach involves solving complex problems by breaking them into simpler overlapping subproblems, storing the results of subproblems to avoid redundant calculations, and constructing a solution from these stored results.
Time Complexity: O(n)
Space Complexity: O(n)
1#include <vector>
std::vector<int> dp(100, -1); // Initialize dp array
int solveProblem(int n) {
if (n == 0 || n == 1) return n;
if (dp[n] != -1) return dp[n];
dp[n] = solveProblem(n-1) + solveProblem(n-2);
return dp[n];
}
int main() {
int n = 10;
std::cout << solveProblem(n) << std::endl;
return 0;
}C++ code using a vector to store results of subproblems in a similar manner to C, making subproblem solutions accessible for reuse.