📝 Introduction
Have you ever been asked this classic interview question?
"Given an array of integers, find the contiguous subarray with the largest sum."
This seemingly simple problem has a brilliant linear-time solution, and it’s called Kadane’s Algorithm. In this post, we’ll explore how it works, walk through an example, and understand why it's so powerful.
Let’s dive in!
💡 Kadane’s Algorithm – Intuition
Kadane’s Algorithm makes a genius observation:
At every element, ask:
“Should I start a new subarray here, or continue the one I already have?”
It keeps track of two variables:
sum: The maximum sum of a subarray that ends at the current position.
max : The maximum sum seen so far across all positions.
✨ Why It’s Awesome
Kadane’s Algorithm is a must-know for interviews. It's simple, elegant, and incredibly fast.
It also serves as a template for many variations of the problem:
Maximum subarray with size constraints
Circular subarrays
2D matrix versions
Maximum product subarray
🧰 Try It Yourself
Here’s the problem on https://leetcode.com/problems/maximum-subarray/description/
Go solve it now and test your understanding!
💬 Final Thoughts
If you enjoyed this breakdown, consider sharing it with your fellow coders!
Feel free to drop your thoughts, edge cases, or your own Kadane variant in the comments below. 🚀
Top comments (0)