Remi.b - 1 month ago 6

C++ Question

My code spends 40% of its time searching through unsorted vectors. More specifically, the searching function

`my_search`

`N`

`N`

The algorithm

`my_search`

`N`

`int sum_of_weight = 0;`

for(int i=0; i<num_choices; i++) {

sum_of_weight += choice_weight[i];

}

int rnd = random(sum_of_weight);

for(int i=0; i<num_choices; i++) {

if(rnd < choice_weight[i])

return i;

rnd -= choice_weight[i];

}

from this post.

I could sort the vector before searching but takes a time of the order of O(N log N) (depending on the sort algorithm used) and I doubt (but might be wrong as I haven't tried) that I would gain much time especially as the weights have little variance.

Another solution would be to store information of how much weight there is before a series of points. For example, while summing the vector, every N/10 elements, I could store the information of how much weights has been summed yet. Then, I could first compare

`rnd`

- Would this be a good solution?
- Is there a name for the process I described?
- How can I estimate what is the right number of breakpoints to store as a function of ?
`N`

- Is there a better solution?

Answer

`log(N)`

Solution```
{
std::vector<double> sums;
double sum_of_weight = 0;
for(int i=0; i<num_choices; i++) {
sum_of_weight += choice_weight[i];
sums.push_back(sum_of_weight);
}
sum_of_weight = random(sum_of_weight);
std::vector<double>::iterator high = std::upper_bound(sums.begin(), sums.end(), sum_of_weight);
if (*high - sum_of_weight > choice_weight[std::distance(sums.begin(), high) - 1]) {
high--;
}
return std::distance(sums.begin(), high);
}
```

Essentially the same idea you have for a better way to solve it, but rather than store only a 10th of the elements, store all of them and use binary search to find the index of the one closest to your value.

Even though this solution is `O(logN)`

, you really have to ask yourself if it's worth it. Is it worth it to have to create an extra vector, thus accumulating extra clock cycles to store things in the vector, the time it takes for vectors to resize, the time it takes to call a function to perform binary search, etc?

*As I was writing the above, I realised you can use a deque instead and that will almost get rid of the performance hit from having to resize and copy contents of vectors without affecting the O(1) lookup of vectors.*

So I guess the question remains, is it worth it to copy over the elements into another container and then only do an `O(logN)`

search?

TBH, I don't think you've gained much from this optimization. In fact I think you gained an overhead of `O(logN)`

.

Source (Stackoverflow)

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