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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/* C code for dynamic programming approach */In the C solution, we initialize an array to store the results of subproblems. We iterate through possible states and fill this array based on previous computations, adhering to the defined recurrence relation relevant for the problem.
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/* Java code for greedy approach */The Java approach utilizes simple loop iterations to apply the greedy algorithm, making decisions and updating the state in-place as we progress.