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Dynamic Programming  Maximum Subarray Problem
Objective: The maximum subarray problem is the task of finding the contiguous subarray within a onedimensional array of numbers that has the largest sum.
Example:
int [] A = {−2, 1, −3, 4, −1, 2, 1, −5, 4}; Output: contiguous subarray with the largest sum is 4, −1, 2, 1, with sum 6.
Approach:
Earlier we have seen how to solve this problem using Kadane's Algorithm. In this post, we will how to solve it using Dynamic programming.
We will solve this problem in a bottomup manner.
let's say
"MS(i) is the maximum sum ending at index i"
To calculate the solution for any element at index "i" has two options
 EITHER added to the solution found till "i1"^{th} index
 OR start a new sum from the index "i".
Recursive Solution:
MS(i) = Max[MS(i1) + A[i] , A[i]]
Code:
Output:
[0, 1, 3, 0, 0, 2, 9, 7, 10] Maximum subarray is 10
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