### Determine whether a triangle can be built from a given set of edges

https://app.codility.com/programmers/lessons/6-sorting/triangle/
And here's a simple O(n log n) solution for it (what the site expects):

 1234567891011 int solution(std::vector& A) { if (A.size() <= 2) return 0; std::sort(A.begin(), A.end()); for (size_t i = 0; i < A.size() - 2; ++i) if ((long long)A[i] + A[i + 1] > A[i + 2]) return 1; return 0; }

What I'm a little curious about is to know if there's possibility to create an O(n) solution with a not-a-very-bad space complexity for the task.
Following my experience with previous tasks I tried to find a relationship/pattern between the results for a few tests on this current task. For example, I tried (using pen and paper) to see if the following items can guide me to a way to find the relationship/pattern for a better solution:
- max, min or average element (of the array)
- using bit manipulation like XOR
- number of elements of the array
- or using a side array

I dedicated about five hours to it but up to now I haven't found such a pattern or particular observation using the items/ideas above except for the last one, like:
- using a std::vector<size_t> of size INT_MAX and add each positive element in the index equal to it into the vector and that way to get it sorted.
- using a 2D std::vector of N*N (N: size of array) and insert all various sequences of the elements into it and finally search on it.

Apart from being sure if the above two ways using a side array can lead to a better solution in terms of time complexity or not, they're obviously quite inefficient in terms of space complexity.
What I need?

What I need is merely a hint! (not code)
I like myself to find a better solution (if any). So if possible just give me some hint or indirect idea for that purpose, please. Then I will try to create the code and then you can post your own ones and together compare them. This way I can take advantages of your experience well.
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Low-hanging fruit: use a non-comparison sorting algorithm.

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I believe "using a side array" will convey generally the same concept as non-comparison sorting algorithms that I'd thought of already. But that doesn't always work for the task well. for example, for array A, A: { 2, 6, 1, 1000, 5 } we expectedly need to use a side/aux array of size max: 1000, initializing it with zeros and then add the number of each element in the index equivalent to it in that aux array. I believe we will have the worst time and space complexity for that array A. Just suppose traversing 1000 elements of array aux for a 5-element size of array A.

Probably there's isn't a better solution than O(n log n) for the task. (As the task also expects that)
 Probably there's isn't a better solution than O(n log n) for the task

I don't believe there is a O(n) solution for this with good space-complexity.

You can use radix-sort and other non-comparison sorting algorithms to "cheat" your way into a better time complexity. But in reality they would likely take the same amount of time.

Something like radix sort is technically O(n) time, but is really O(k*n) time, where k is the length of the largest number. Radix sort only performs better than quicksort if k < log(n).

So you'd have O(k*n) or O(n*log(n)) time as your best implementations. Again though, O(k*n) is still technically O(n) depending on who you ask.

Things like this should show you the limitations of looking at time complexity alone - it's only a small part of the bigger picture.
Careful, your solution assumes that the sorted array has three contiguous (not just consecutive) values that satisfy the solution.
As for radix sort when we have an array with 10 numbers with max: 100
Time complexity:
O(n log n) = O(10 * 1)
O(n*k) = O(10 * 100)
This is apart from measuring space complexity of radix for which an array of 100 elements is needed.

I assume non-comparison sorting algorithms (with an acceptable space complexity) lead to a better time complexity than O(n log n) only when the elements are evenly distributed along the array with no huge difference in them.
Non-comparison sorts are not affected by element distribution.
Here is the solution:

 1234567891011121314151617181920212223 #include int solution(vector &A) { // write your code in C++14 (g++ 6.2.0) int size = (int)A.size(); if (size < 3) { return 0; } vector x; for (int i = 0; i < size; i++) { x.push_back(A[i]); } sort(x.begin(), x.end()); int i = 0; while (i < size - 2) { if ( x[i] + x[i + 1] > x[i + 2]){ return 1; } i++; } return 0; }
@denver2020

Uhh..
@Duthomhas
You mean applying a non-comparison sort algorithm for both the arrays below is the same?
A: { 2, 6, 1, 1000, 5 }
B: { 3, 12, 44, 32, 28 }

@denver2020 This is the O(n log n) time complexity I talked about already but there's subtle error on line 16 of your code.
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