Experimental time complexity analysis is a way to estimate the time complexity of an algorithm by running it on various inputs and measuring the running time. Here is an example of how to perform experimental time complexity analysis in C++:

#include <iostream>
#include <chrono>
using namespace std;
using namespace std::chrono;

void someAlgorithm(int n) {
  // code for the algorithm
}

int main() {
  int n;
  cin >> n;

  auto start = high_resolution_clock::now();
  someAlgorithm(n);
  auto stop = high_resolution_clock::now();
  auto duration = duration_cast<microseconds>(stop - start);

  cout << "Time taken by function: "
       << duration.count() << " microseconds" << endl;
  return 0;
}

In this example, we have a function someAlgorithm() that takes an input parameter n. We use the library to measure the running time of the function by recording the start time and end time using high_resolution_clock::now(), and then calculating the duration using duration_cast(stop — start).

To perform the experimental time complexity analysis, we can run the someAlgorithm() function with various input sizes (for example, 10, 100, 1000, 10000, etc.), and record the corresponding running times. We can then plot the running times against the input sizes on a graph, and try to fit a curve to the data. The shape of the curve can give us an idea of the time complexity of the algorithm. For example, if the curve looks linear, then the algorithm is likely to have linear time complexity. If the curve looks quadratic, then the algorithm is likely to have quadratic time complexity.

However, it is important to note that experimental time complexity analysis is not a rigorous method and should be used with caution.