Possible usecase: Maximum subarray problem.
- Initialize two variables:
max_sum = current_sum = 0
- Loop through numbers in list
current_sum = max(0, current_sum + list[i])
max_sum = max(max_sum, current_sum)
- Kadane's Algorithm requires at least one positive number, so an input of all negative numbers would be invalid.
- If the array contains all non-negative numbers, then the problem is trivial; a maximum subarray is the entire array.
- If the array contains all non-positive numbers, then a solution is any subarray of size 1 containing the maximal value of the array (or the empty subarray (which has sum 0), if it is permitted).
- Several different sub-arrays may have the same maximum sum.
Solving Best Time to Buy and Sell Stock with Kadane's Algorithm
Slightly tweak the algorithm to track max profit and min price.
def maxProfit(self, prices: List[int]) -> int: max_profit, min_price = 0, float("inf") for price in prices: min_price = min(min_price, price) max_profit = max(max_profit, price - min_price) return max_profit