在以下代码段中,std::sort
的时间消耗。这需要O(nlog(n))时间。 std::chrono
仅用于测量std::sort
。
我使用优化级别为-O3
的Intel编译器18.0.3编译了以下代码。我使用Redhat6。
#include <vector>
#include <random>
#include <limits>
#include <iostream>
#include <chrono>
#include <algorithm>
int main() {
std::random_device dev;
std::mt19937 rng(dev());
std::uniform_int_distribution<std::mt19937::result_type> dist(std::numeric_limits<int>::min(),
std::numeric_limits<int>::max());
int ret = 0;
const unsigned int max = std::numeric_limits<unsigned int>::max();
for (auto j = 1u; j < max; j *= 10) {
std::vector<int> vec;
vec.reserve(j);
for (int i = 0; i < j; ++i) {
vec.push_back(dist(rng));
}
auto t_start = std::chrono::system_clock::now();
std::sort(vec.begin(), vec.end());
const auto t_end = std::chrono::system_clock::now();
const auto duration = std::chrono::duration_cast<std::chrono::duration<double>>(t_end - t_start).count();
std::cout << "Time measurement: j= " << j << " took " << duration << " seconds.\n";
ret + vec[0];
}
return ret;
}
该程序的输出为
Time measurement: j= 1 took 1.236e-06 seconds.
Time measurement: j= 10 took 5.583e-06 seconds.
Time measurement: j= 100 took 1.0145e-05 seconds.
Time measurement: j= 1000 took 0.000110649 seconds.
Time measurement: j= 10000 took 0.00142651 seconds.
Time measurement: j= 100000 took 0.00834339 seconds.
Time measurement: j= 1000000 took 0.098939 seconds.
Time measurement: j= 10000000 took 0.938253 seconds.
Time measurement: j= 100000000 took 10.2398 seconds.
Time measurement: j= 1000000000 took 114.214 seconds.
Time measurement: j= 1410065408 took 163.824 seconds.
这似乎非常接近线性行为。
为什么std::sort
要求O(n)
而不是O(nlog(n))
?
答案 0 :(得分:12)
您呈现的图形非常适合y = x log (x)
。与x
相比,log(x)
的影响很小。我认为您的结果将通过x log (x)
的卡方检验,具有重要意义。
这里没有惊喜。
这是您欣赏O(n log n)并不比O(n)差很多的试金石。