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Update solution.md #3182

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4 changes: 2 additions & 2 deletions 1-js/05-data-types/04-array/10-maximal-subarray/solution.md
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Expand Up @@ -59,7 +59,7 @@ alert( getMaxSubSum([100, -9, 2, -3, 5]) ); // 100

The solution has a time complexity of [O(n<sup>2</sup>)](https://en.wikipedia.org/wiki/Big_O_notation). In other words, if we increase the array size 2 times, the algorithm will work 4 times longer.

For big arrays (1000, 10000 or more items) such algorithms can lead to a serious sluggishness.
For big arrays (1000, 10000 or more items) such algorithms can lead to serious sluggishness.

# Fast solution

Expand Down Expand Up @@ -91,4 +91,4 @@ alert( getMaxSubSum([-1, -2, -3]) ); // 0

The algorithm requires exactly 1 array pass, so the time complexity is O(n).

You can find more detail information about the algorithm here: [Maximum subarray problem](http://en.wikipedia.org/wiki/Maximum_subarray_problem). If it's still not obvious why that works, then please trace the algorithm on the examples above, see how it works, that's better than any words.
You can find more detailed information about the algorithm here: [Maximum subarray problem](http://en.wikipedia.org/wiki/Maximum_subarray_problem). If it's still not obvious why that works, then please trace the algorithm on the examples above, see how it works, that's better than any words.