Partition Algorithms

# 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 specificÂ 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 isÂ 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 useÂ case.

This might involve tweaking the logic for how elements are moved or adding additional processing steps. Hereâ€™s a simple custom partitionÂ 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 specificÂ 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 yourÂ needs.

This Question is from the Lesson:

### Partition Algorithms

An introduction to partitions, and the C++ standard library algorithms that create them

Answers to questions are automatically generated and may not have been reviewed.

This Question is from the Lesson:

### Partition Algorithms

An introduction to partitions, and the C++ standard library algorithms that create them

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