Write an efficient Java program to find the sum of contiguous subarray within a one-dimensional array of numbers which has the largest sum.
Kadane’s Algorithm:
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[ad type=”banner”]
Explanation:
Simple idea of the Kadane’s algorithm is to look for all positive contiguous segments of the array (max_ending_here is used for this). And keep track of maximum sum contiguous segment among all positive segments (max_so_far is used for this). Each time we get a positive sum compare it with max_so_far and update max_so_far if it is greater than max_so_far
Lets take the example: {-2, -3, 4, -1, -2, 1, 5, -3} max_so_far = max_ending_here = 0 for i=0, a[0] = -2 max_ending_here = max_ending_here + (-2) Set max_ending_here = 0 because max_ending_here < 0 for i=1, a[1] = -3 max_ending_here = max_ending_here + (-3) Set max_ending_here = 0 because max_ending_here < 0 for i=2, a[2] = 4 max_ending_here = max_ending_here + (4) max_ending_here = 4 max_so_far is updated to 4 because max_ending_here greater than max_so_far which was 0 till now for i=3, a[3] = -1 max_ending_here = max_ending_here + (-1) max_ending_here = 3 for i=4, a[4] = -2 max_ending_here = max_ending_here + (-2) max_ending_here = 1 for i=5, a[5] = 1 max_ending_here = max_ending_here + (1) max_ending_here = 2 for i=6, a[6] = 5 max_ending_here = max_ending_here + (5) max_ending_here = 7 max_so_far is updated to 7 because max_ending_here is greater than max_so_far for i=7, a[7] = -3 max_ending_here = max_ending_here + (-3) max_ending_here = 4
Program:
Output :
Maximum contiguous sum is 7[ad type=”banner”]
Above program can be optimized further, if we compare max_so_far with max_ending_here only if max_ending_here is greater than 0.
Time Complexity: O(n)
Algorithmic Paradigm: Dynamic Programming