### Breaking Down JavaScript Solutions To Common Algorithmic Questions (Part 1)

#### Emma Wedekind ✨ on February 20, 2019

Have you ever struggled to develop an algorithm solution on a technical interview? In this short tutorial, we'll break down three top algorithm c... [Read Full]

Love this post!! My only criticism would be that the code transitions between each brute force solution and each optimized solution are not improvements in algorithmic complexity (Big O) but are readability improvements from using standard library functions. I still think it's cool to showcase how knowledge of available standard library methods can clean up solutions tremendously!

Well.. some of them are optimized for better Big O run times, but not all

Unless I'm mistaken (please correct me if I'm wrong), the complexity is O(N) for all 6 solutions, where N is the number of letters in the reversed string, the number of letters in the sentence (because of String#split), and the total number of elements in all the arrays. I definitely apologize for being nitpicky here, but my concern is that newer developers will read this and misunderstand what you mean by "optimize" the solution. In this case you're optimizing readability, which is great, but the algorithmic complexity is unchanged.

I’m not positive how Math.max works and if the last example functions as a double nested loop. The last example wouldn’t necessarily be O(n) as input size grew and varied (but I’m not sure as to specifics). So the first 2 I believe are O(n) while the last one is probably O(n) given that the sub arrays have a finite set of elements, but for growing and variable input could creep up to O(2) But agreed I should change the wording from optimized.

Ahh I understand the confusion now. The last example is definitely tricky because it's easy to read it as one loop. In actuality, Math.max has to iterate over the full argument list in order to produce the maximum, making the last example O(N) if you consider N to be the total number of elements across all of the arrays. You could also say the last example is O(N * M) where N is the number of arrays and M is the number of elements in each array, but this assumes that each array has the same length, which may not be the case.

A great demonstration for beginners, but the terms "brute force" and "optimized" are quite misleading.

Brute force usually means exhaustive search, for example in password cracking, where all the possible combinations of letters are tried. Better term would be "naive".

Optimized usually means faster, better than naive simple method. In these examples no optimized solution is faster than the "brute force". It's still the same asymptotic complexity, but usually more operations, so normally it'd be slower. It's optimized for code size, I give you that.

Actually, all those "ugly", C style version for for loops and nester for loops usually perform better than their functional equivalents, because they usually allocate less memory to store temporaries. For example:

``````const arrOfLengths = arrOfWords.map(item => item.length);
``````

creates an array to store lengths of words. This could be avoided by calculating the maximum on the fly, like in the "brute force" solution. Imagine a situation where you only have memory to store the original list of words, say it's 2GB long. It's still O(n), but requires double the memory.

yes correct! Also in the interviews they(recruiters) would expect us to write the code without using in built methods.

they would expect us to write the code without using in built methods

They would? Since when? Where?

In most of the top companies the interviewers will ask us to write the code without using any in build methods.

I guess I understand :/

but while working in a software company we should use the in built methods for code readability.

Great post! I particularly like the use of `Math.max` and spread operator.

For returning the length of the longest word I think a single `reduce` would be slightly quicker than a `map` followed by a `Math.max`.

e.g.

``````string.split(' ').reduce((longest, item) => Math.max(longest, item.length), 0)
``````

For anyone interested I posted an performance analysis and some gotchas of these solutions:

Nice work. Would love to see actual performance of each method as well though : )

I'd use this for Longest Word. It's one less iteration, and can be easily modified to return the word.

``````function findLongestWordLength(str) {
const arrOfWords = str.split(/\s+/)
const longestWord = arrOfWords.reduce((a, b) => b.length > a.length ? b : a)
return longestWord
}
``````

I've never thought of the trick used in the last two optimized solutions, using `Math` with a spread operator. Will definitely look into those more!

Way better than using Math.max.apply!

Sweet and elegant!

I am cat dad

what is the time complexity of array.length in Javascript

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