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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.
1def solve_problem(parameters):
2 # Base case solution
3 if base_condition:
4 return
5 # Divide
6 mid = calculate_middle
7 # Conquer
8 solve_problem(left_part)
9 solve_problem(right_part)
10 # Combine
11 # combine left and right results
12
13solve_problem(initial_parameters)Python's function uses recursion to handle each subproblem after dividing the original problem. It combines results of subproblems to form the solution to the original problem.
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.