Benchmark: quickselect_median

Script Preparation code:

function quickselect_median(arr) {
   const L = arr.length, halfL = L/2;
   if (L % 2 == 1)
      return quickselect(arr, halfL);
   else
      return 0.5 * (quickselect(arr, halfL - 1) + quickselect(arr, halfL));
}

function quickselect(arr, k) {
   // Select the kth element in arr
   // arr: List of numerics
   // k: Index
   // return: The kth element (in numerical order) of arr
   if (arr.length == 1)
      return arr[0];
   else {
      const pivot = arr[0];
      const lows = arr.filter((e)=>(e<pivot));
      const highs = arr.filter((e)=>(e>pivot));
      const pivots = arr.filter((e)=>(e==pivot));
      if (k < lows.length) // the pivot is too high
         return quickselect(lows, k);
      else if (k < lows.length + pivots.length)// We got lucky and guessed the median
         return pivot;
      else // the pivot is too low
         return quickselect(highs, k - lows.length - pivots.length);
   }
}

Tests:

arrTest1
[13, 13, 14, 14, 14, 15, 15, 16]
arrTest2
[7,3,5, 2300,5,4,0,123,2,76,768,28]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
arrTest1
arrTest2

Fastest: N/A

Slowest: N/A

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

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Let's break down the provided benchmark and explain what is tested, compared, and the pros/cons of different approaches.

**Benchmark Definition**

The provided JSON defines two JavaScript benchmarks: `quickselect_median`. This function is used to find the median value in an array. The implementation uses the QuickSelect algorithm, which is a variation of the QuickSort algorithm that selects a pivot element and partitions the array around it.

**Options Compared**

In this benchmark, we have two options compared:

1. **QuickSelect**: The original implementation using the QuickSelect algorithm.
2. **Naive Approach**: A simpler approach that iterates through the array to find the median value (not implemented in this example).

**Pros and Cons**

**QuickSelect**

Pros:

* Efficient: QuickSelect has an average time complexity of O(n), making it suitable for large datasets.
* Simple implementation: The algorithm is relatively straightforward to understand and implement.

Cons:

* Worst-case scenario: In the worst case, QuickSelect can have a time complexity of O(n^2). However, this occurs very rarely in practice.

**Naive Approach**

Pros:

* Simple to implement: The naive approach requires only basic iteration through the array.
* Easy to understand: The algorithm is straightforward and easy to comprehend.

Cons:

* Inefficient: The naive approach has a time complexity of O(n), which can be slower than QuickSelect for large datasets.

**Other Considerations**

* **Memory Usage**: Both implementations have a linear memory usage, as they work with the entire array. However, QuickSelect is generally more efficient in terms of memory usage due to its ability to partition the array.
* **Stability**: QuickSelect is not stable, meaning that it may swap equal elements during the partitioning process. The naive approach is also not stable.

**Library/Function**

In this benchmark, there is no explicit library or function used. However, the QuickSelect algorithm relies on a few built-in JavaScript functions:

* `Array.prototype.filter()`: Used to filter elements based on conditions.
* `Array.prototype.indexOf()` (not explicitly shown): Could be used to find the index of the median element if needed.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in this benchmark. The implementation is straightforward and uses standard JavaScript language constructs.

**Alternative Implementations**

If you're interested in exploring alternative implementations, consider the following:

* **Merge Sort**: Another sorting algorithm that can be used to find the median value.
* **Heap-based approach**: Using a heap data structure to find the median value more efficiently.
* **Iterative approach**: Implementing an iterative version of QuickSelect or using other algorithms like Binary Search.

Keep in mind that these alternatives might have different trade-offs and performance characteristics compared to the original implementation.

Related benchmarks:

quickselect_median (version: 0)

Comparing performance of: arrTest1 vs arrTest2

Created: 5 years ago by: Guest

Jump to the latest result

arrTest1

arrTest2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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