<summary>Why are the two methods equivalent?</summary>
Consider the desired maximum subarray. As you go along from left to right, the prefix sum solution will mark the start of that subarray as the "current minimum prefix sum". Kadane's Algorithm, on the other hand, will set the current value to 0 at that point. As both solutions iterate through the array, they eventually find the right side of the maximum sum, and they find the answer to the problem at that location. In essence, Kadane's Algorithm stores the maximum sum of a subarray that ends at the current location (which is what the prefix sum solution calculates on each iteration), but it calculates this value greedily instead.
**Task:** Given an array of size $N$, do the following operation $Q$ times: add $X$ to the values between $i$ and $j$. Afterwards, print the final array.
**Solution:** Consider the array formed by $a_i-a_{i-1}$. When processing a range addition, only two values in this difference array change! At the end, we can recover the original array using prefix sums. (The math is left as an exercise to the reader.)
Given a 2-dimensional array of size $N\cdot M$, answer $Q$ queries of the following form: Find the sum of all elements within the rectangle of indices $(x1,y1)$ to $(x2,y2)$.
Similar to prefix sums, you can also take prefix minimum or maximum; but *you cannot* answer min queries over an arbitrary range with prefix minimum. (This is because minimum doesn't have an inverse operation, the way subtraction is to addition.)
Instead of storing just the values themselves, you can also take a prefix sum over $i\cdot a_i$, or $10^i \cdot a_i$, for instance. Some math is usually helpful for these problems; don't be afraid to get dirty with algebra!
