For this task I made a Python implementation of an algorithm to find the maximum sum subarray within an array. The algorithm implemented here is known as Kadane's algorithm, which efficiently solves the maximum subarray sum problem in linear time complexity.
Kadane's algorithm is a dynamic programming-based approach to find the maximum sum subarray within an array of integers. It iterates through the array, maintaining the maximum sum found so far (max_so_far) and the current sum of elements (curr_sum). At each step, it updates 'curr_sum' by adding the current element to it and compares it with 'max_so_far'. If 'curr_sum' becomes greater than 'max_so_far', 'max_so_far' is updated accordingly. If 'curr_sum' becomes negative, it is reset to zero, effectively starting a new subarray. By iteratively updating these variables, the algorithm finds the maximum sum subarray.
This code implements Kadane's algorithm to efficiently find the maximum sum subarray within an array of integers. It handles both positive and negative numbers, as well as cases where the entire array is negative. To use the code you must call the 'max_sum_subarray' function and pass any array of integers as an argument. The function will return the maximum sum subarray along with its start and end indices.