Initialize:
max_so_far = 0
max_ending_here = 0
Loop for each element of the array
(a) max_ending_here = max_ending_here + a[i]
(b) if(max_ending_here < 0)
max_ending_here = 0
(c) if(max_so_far < max_ending_here)
max_so_far = max_ending_here
return max_so_far
Can anyone help me in understanding the optimal substructure and overlapping problem(bread and butter of DP) i the above algo?
According to this definition of overlapping subproblems, the recursive formulation of Kadane's algorithm (f[i] = max(f[i - 1] + a[i], a[i])) does not exhibit this property. Each subproblem would only be computed once in a naive recursive implementation.
It does however exhibit optimal substructure according to its definition here: we use the solution to smaller subproblems in order to find the solution to our given problem (f[i] uses f[i - 1]).
Consider the dynamic programming definition here:
In mathematics, computer science, and economics, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems1 and optimal substructure (described below). When applicable, the method takes far less time than naive methods that don't take advantage of the subproblem overlap (like depth-first search).
The idea behind dynamic programming is quite simple. In general, to solve a given problem, we need to solve different parts of the problem (subproblems), then combine the solutions of the subproblems to reach an overall solution. Often when using a more naive method, many of the subproblems are generated and solved many times. The dynamic programming approach seeks to solve each subproblem only once, thus reducing the number of computations
This leaves room for interpretation as to whether or not Kadane's algorithm can be considered a DP algorithm: it does solve the problem by breaking it down into easier subproblems, but its core recursive approach does not generate overlapping subproblems, which is what DP is meant to handle efficiently - so this would put it outside DP's specialty.
On the other hand, you could say that it is not necessary for the basic recursive approach to lead to overlapping subproblems, but this would make any recursive algorithm a DP algorithm, which would give DP a much too broad scope in my opinion. I am not aware of anything in the literature that definitely settles this however, so I wouldn't mark down a student or disconsider a book or article either way they labeled it.
So I would say that it is not a DP algorithm, just a greedy and / or recursive one, depending on the implementation. I would label it as greedy from an algorithmic point of view for the reasons listed above, but objectively I would consider other interpretations just as valid.
Note that I derived my explanation from this answer. It demonstrates how Kadane’s algorithm can be seen as a DP algorithm which has overlapping subproblems.
Imagine we have an array a from which we want to get the maximum subarray. To determine the max subarray that ends at index i the following recursive relation holds:
max_subarray_to(i) = max(max_subarray_to(i - 1) + a[i], a[i])
In order to get the maximum subarray of a we need to compute max_subarray_to() for each index i in a and then take the max() from it:
max_subarray = max( for i=1 to n max_subarray_to(i) )
Now, let's assume we have an array [10, -12, 11, 9] from which we want to get the maximum subarray. This would be the work required running Kadane's algorithm:
result = max(max_subarray_to(0), max_subarray_to(1), max_subarray_to(2), max_subarray_to(3))
max_subarray_to(0) = 10 # base case
max_subarray_to(1) = max(max_subarray_to(0) + (-12), -12)
max_subarray_to(2) = max(max_subarray_to(1) + 11, 11)
max_subarray_to(3) = max(max_subarray_to(2) + 9, 49)
As you can see, max_subarray_to() is evaluated twice for each i apart from the last index 3, thus showing that Kadane's algorithm does have overlapping subproblems.
Kadane's algorithm is usually implemented using a bottom up DP approach to take advantage of the overlapping subproblems and to only compute each subproblem once, hence turning it to O(n).
