Benchmark: Classic Permute vs Johnson-Trotter Permute

Tests:

Classic
function permuteSlice(arr) { function generate(input, output, permutations) { if (input.length === 0) { permutations.push(output.slice()); } for (let i = 0; i < input.length; i++) { let newInput = input.slice(0, i).concat(input.slice(i + 1)); output.push(input[i]); generate(newInput, output, permutations); output.pop(); } return permutations; } return generate(arr, [], []); } permuteSlice([1, 2, 3, 4, 5])
Johnson Trotter
function johnsonTrotterPermute(arr) { const permutations = []; function generate(n, input) { if (n === 1) { permutations.push([...input]); return; } generate(n - 1, input); // Recursively generate permutations for n - 1 for (let i = 0; i < n - 1; i++) { // Decide which two elements to swap based on the parity of n if (n % 2 === 0) { [input[i], input[n - 1]] = [input[n - 1], input[i]]; } else { [input[0], input[n - 1]] = [input[n - 1], input[0]]; } generate(n - 1, input); } } generate(arr.length, arr); return permutations; } johnsonTrotterPermute([1, 2, 3, 4, 5])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Classic
Johnson Trotter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

I'll break down the explanation into smaller sections to make it easier to understand.

**Benchmark Definition and Script Preparation Code**

The benchmark definition is provided in JSON format, which represents two test cases: Classic Permute and Johnson-Trotter Permute. The script preparation code is not explicitly provided, but based on the benchmark definitions, we can infer that it's a JavaScript function that generates an array of random numbers.

**Classic Permute**

The Classic Permute algorithm uses a recursive approach to generate all permutations of the input array. It works by iterating through each element in the array and recursively generating permutations for the remaining elements. The current element is then added to the output array, and the process repeats until all elements have been processed.

```javascript
function permuteSlice(arr) {
  function generate(input, output, permutations) {
    if (input.length === 0) {
      permutations.push(output.slice());
    }
    for (let i = 0; i < input.length; i++) {
      let newInput = input.slice(0, i).concat(input.slice(i + 1));
      output.push(input[i]);
      generate(newInput, output, permutations);
      output.pop();
    }
    return permutations;
  }
  return generate(arr, [], []);
}
```

**Johnson-Trotter Permute**

The Johnson-Trotter Permute algorithm uses a non-recursive approach to generate all permutations of the input array. It works by iteratively swapping elements in the array based on a formula that depends on the length of the array.

```javascript
function johnsonTrotterPermute(arr) {
  const permutations = [];
  function generate(n, input) {
    if (n === 1) {
      permutations.push([...input]);
      return;
    }
    generate(n - 1, input);
    for (let i = 0; i < n - 1; i++) {
      // Decide which two elements to swap based on the parity of n
      if (n % 2 === 0) {
        [input[i], input[n - 1]] = [input[n - 1], input[i]];
      } else {
        [input[0], input[n - 1]] = [input[n - 1], input[0]];
      }
      generate(n - 1, input);
    }
  }
  generate(arr.length, arr);
  return permutations;
}
```

**Pros and Cons of Different Approaches**

*   **Classic Permute**: Pros:
    *   Easy to understand and implement
    *   Simple recursive approach makes it suitable for small arrays
    Cons:
    *   Time complexity is O(n!) because it generates all permutations, which can lead to performance issues with large input sizes
    *   Uses more memory since it stores all generated permutations in the output array
*   **Johnson-Trotter Permute**: Pros:
    *   Faster than Classic Permute for large arrays because it only generates necessary permutations and uses less memory
    Cons:
    *   More complex algorithm to understand and implement
    *   Requires knowledge of mathematical formulas to implement correctly

**Library Used in Test Case**

In the test cases, there is no explicit library mentioned. However, the `permuteSlice` function uses a custom implementation, while the `johnsonTrotterPermute` function also has a custom implementation.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in these benchmark definitions.

**Latest Benchmark Result**

The latest benchmark result shows that Chrome 117 (Desktop) achieved higher performance for both test cases. However, the exact results depend on the actual execution time and the device's specifications.

**Other Alternatives**

If you want to explore alternative algorithms for generating permutations, here are a few options:

*   **Backtracking Algorithm**: This algorithm works by recursively exploring all possible combinations of elements in the array.
*   **Cycle Detection Algorithm**: This algorithm detects cycles in the permutation generation process and avoids redundant calculations.
*   **Approximation Algorithms**: These algorithms use heuristics to generate approximate permutations, which can be faster but may not produce exact results.

Note that these alternatives might have different trade-offs between performance, memory usage, and accuracy.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36 Edg/117.0.2045.47

Browser/OS: Chrome 117 on Windows

View result in a separate tab

Test name	Executions per second
Classic	31153.1 Ops/sec
Johnson Trotter	285938.6 Ops/sec

Related benchmarks:

Classic Permute vs Johnson-Trotter Permute (version: 0)

Comparing performance of: Classic vs Johnson Trotter

Created: one year ago by: Guest

Jump to the latest result

Classic

Johnson Trotter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):