Alternatives to partition()
Are there any alternatives to using std::partition()
or std::ranges::partition()
that offer better performance or additional features?
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:
#include <algorithm>
#include <iostream>
#include <vector>
int main() {
std::vector<int> A = {1, -6, 7, 2, 5, 4};
auto isEven = [](int x) { return x % 2 == 0; };
std::ranges::stable_partition(A, isEven);
std::cout << "After stable_partition: ";
for (int x : A) {
std::cout << x << ", ";
}
}
After 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()
:
#include <algorithm>
#include <execution>
#include <iostream>
#include <vector>
int main() {
std::vector<int> A = {1, -6, 7, 2, 5, 4};
auto isEven = [](int x) { return x % 2 == 0; };
std::partition(std::execution::par,
A.begin(), A.end(), isEven);
std::cout << "After parallel partition: ";
for (int x : A) {
std::cout << x << ", ";
}
}
After 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:
#include <iostream>
#include <vector>
#include <algorithm>
template <typename Iterator, typename Predicate>
Iterator custom_partition(Iterator first,
Iterator last, Predicate pred) {
Iterator result = first;
for (Iterator it = first; it != last; ++it) {
if (pred(*it)) {
std::iter_swap(it, result);
++result;
}
}
return result;
}
int main() {
std::vector<int> A = {1, -6, 7, 2, 5, 4};
auto isEven = [](int x) { return x % 2 == 0; };
auto partition_point = custom_partition(
A.begin(), A.end(), isEven);
std::cout << "After custom partition: ";
for (int x : A) {
std::cout << x << ", ";
}
}
After 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.
Partition Algorithms
An introduction to partitions, and the C++ standard library algorithms that create them