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This approach leverages dynamic programming techniques to break down the problem into overlapping subproblems and solve them using a bottom-up manner. The core idea is to store the results of subproblems to avoid redundant computations, therefore optimizing the solution.
Time Complexity: O(n)
Space Complexity: O(n)
1''' Python code for dynamic programming approach '''In Python, we use a list to accumulate results from the smallest subproblems to bigger ones. Python's dynamic typing and simplified syntax make handling lists easier.
The greedy approach aims to find a solution by making the most favorable choice at every stage, intending to reach an overall optimal solution. This approach might not always work for all types of problems but can provide simpler solutions where applicable.
Time Complexity: O(n)
Space Complexity: O(1)
1/* C++ code for greedy approach*/The C++ solution follows similar principles as in C, using vectors or arrays. The focus is on maintaining minimal state, with decisions made based on available immediate information.