You are given n days and for each day (di) you could either perform a high effort tasks (hi) or a low effort tasks (li) or no task with the constraint that you can choose a high-effort tasks only if you chose no task on the previous day. Write a program to find the maximum amount of tasks you can perform within these n days.
No. of days (n) = 5 Day L.E. H.E 1 1 3 2 5 6 3 4 8 4 5 7 5 3 6 Maximum amount of tasks = 3 + 5 + 4 + 5 + 3 = 20
To find the maximum amount of tasks done till i’th day, we need to compare 2 choices:
- Go for high effort tasks on that day, then find the maximum amount of tasks done till (i – 2) th day.
- Go for low effort task on that day and find the maximum amount of tasks done till (i – 1) th day.
Let high [1…n] be the input array for high effort task amount on i’th day and low [1…n] be the input array for low effort task amount on ith day.
Let max_task (high , low , i) be the function that returns maximum amount of task done till ith day, so it will return max(high[i] + max_task(high, low, (i – 2)), low [i] + max_task (high, low, (i – 1)))
Therefore, the problem has optimal substructure property as the problem can be solved using solutions to subproblems.
Following is a simple recursive implementation of the High-effort vs. Low-effort task problem. The implementation simply follows the recursive structure mentioned above. So, High-effort vs. Low-effort Task Problem has both properties of a dynamic programming problem.
It should be noted that the above function computes the same subproblems again and again.
Therefore, this problem has Overlapping Subproblems Property. So the High-effort vs. Low-effort Task Problem has both the properties of a dynamic programming problem.
Dynamic Programming Solution
Time Complexity : O(n)
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- Travelling Salesman Problem | Set 1 (Naive and Dynamic Programming)
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