Alternatives to partition()

While std::partition() and std::ranges::partition() are powerful tools for dividing collections, there are alternatives that might offer better performance or additional features depending on your specific needs.

1. Use stable_partition()

If you need to maintain the relative order of elements, std::ranges::stable_partition() is a better choice despite its higher complexity. It ensures the order of equivalent elements is preserved:

1#include <algorithm>
2#include <iostream>
3#include <vector>
4
5int main() {
6  std::vector<int> A = {1, -6, 7, 2, 5, 4};
7
8  auto isEven = [](int x) { return x % 2 == 0; };
9
10  std::ranges::stable_partition(A, isEven);
11
12  std::cout << "After stable_partition: ";
13  for (int x : A) {
14    std::cout << x << ", ";
15  }
16}

1After stable_partition: -6, 2, 4, 1, 7, 5,

2. Parallel Algorithms

For large datasets, parallel algorithms can significantly speed up processing. The <execution> library in C++17 introduces parallel versions of many algorithms, including partition():

1#include <algorithm>
2#include <execution>
3#include <iostream>
4#include <vector>
5
6int main() {
7  std::vector<int> A = {1, -6, 7, 2, 5, 4};
8
9  auto isEven = [](int x) { return x % 2 == 0; };
10
11  std::partition(std::execution::par,
12    A.begin(), A.end(), isEven);
13
14  std::cout << "After parallel partition: ";
15  for (int x : A) {
16    std::cout << x << ", ";
17  }
18}

1After parallel partition: -6, 2, 4, 1, 7, 5,

3. Custom Partitioning Functions

For specialized needs, custom partitioning functions can offer more control. You can implement a tailored partitioning algorithm optimized for your specific use case.

This might involve tweaking the logic for how elements are moved or adding additional processing steps. Here's a simple custom partition function:

1#include <iostream>
2#include <vector>
3#include <algorithm>
4
5template <typename Iterator, typename Predicate>
6Iterator custom_partition(Iterator first,
7  Iterator last, Predicate pred) {
8  Iterator result = first;
9  for (Iterator it = first; it != last; ++it) {
10    if (pred(*it)) {
11      std::iter_swap(it, result);
12      ++result;
13    }
14  }
15  return result;
16}
17
18int main() {
19  std::vector<int> A = {1, -6, 7, 2, 5, 4};
20  auto isEven = [](int x) { return x % 2 == 0; };
21
22  auto partition_point = custom_partition(
23    A.begin(), A.end(), isEven);
24
25  std::cout << "After custom partition: ";
26  for (int x : A) {
27    std::cout << x << ", ";
28  }
29}

1After custom partition: -6, 2, 4, 1, 7, 5,

4. Library-Specific Algorithms

Certain libraries offer enhanced partitioning functions. Libraries like Boost provide additional algorithms and data structures that might offer improved performance or flexibility for specific applications.

Conclusion

While std::partition() and std::ranges::partition() are versatile and efficient for many cases, exploring alternatives like stable_partition(), parallel algorithms, custom functions, or library-specific tools can provide better performance or additional features tailored to your needs.