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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/* Java code for dynamic programming approach */The Java implementation uses an array to store intermediate results. A simple loop is used to calculate and store these results based on previously computed values.
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''' Python code for greedy approach '''Python's inherent capabilities allow concise expression of greedy algorithms, where decision points are processed sequentially within a loop structure.