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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
Python solution using a list to store the values of subproblems. It recursively calculates and stores results for use in overlapping subproblems.