In the process of learning algorithms, there are many case-specific solutions we need to learn. Not all can teach us broadly applicable lessons, though they’re still good to know. A few of these problems, however, are especially good for broadening our thinking and can give us tools that we can use in a broad spectrum of cases.
The Maximum Subarray problem was one of these eye-opening problems for me. The solution, using Kadane’s Algorithm, is a great introduction to dynamic programming and I think taking a little extra time on this problem is worthwhile. After studying this problem I knew how to look for much more efficient solutions rather than always writing iterative solutions with inefficient nested loops. It’ll show you how to take a solution with exponential time complexity and take it to linear time complexity.

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