# 5 Best Ways to Find the Kth Largest Product of Elements from Two Arrays in Python

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π‘ Problem Formulation: The task is to compute the kth largest product by pairing elements from two different arrays in Python. Consider you have arrays `arr1 = [3, 5, 2]` and `arr2 = [4, 8, 1]`, and you need the 3rd largest product. Your program should efficiently output the result, which, in this case, is `8` (the product of 2 and 4).

## Method 1: Brute Force Approach

This method involves computing all possible products between the elements of the two arrays, sorting the computed products, and then selecting the kth largest value. This approach is straightforward but not efficient for large arrays as its time complexity is O(n^2 log n^2), where n is the size of the larger array.

Here’s an example:

```import heapq

def kth_largest_product_brute_force(arr1, arr2, k):
products = []
for num1 in arr1:
for num2 in arr2:
heapq.heappush(products, num1 * num2)

largest_products = heapq.nlargest(k, products)
return largest_products[-1]

# Example use case:
arr1 = [3, 5, 2]
arr2 = [4, 8, 1]
k = 3
print(kth_largest_product_brute_force(arr1, arr2, k))
```

Output:

`8`

This snippet defines a function `kth_largest_product_brute_force()` that takes two lists and an integer k as arguments. It computes all possible products, uses a min heap to store them, and then extracts the kth largest product using the `heapq.nlargest()` function.

## Method 2: Sort and Select

This method also calculates all possible products but optimizes by sorting the list of products once rather than pushing each product to a heap. The time complexity remains O(n^2 log n^2). However, the sorting step generally executes faster in practice than maintaining a heap for every insertion.

Here’s an example:

```def kth_largest_product_sort_select(arr1, arr2, k):
products = [x * y for x in arr1 for y in arr2]
products.sort(reverse=True)
return products[k-1]

# Example use case:
arr1 = [3, 5, 2]
arr2 = [4, 8, 1]
k = 3
print(kth_largest_product_sort_select(arr1, arr2, k))
```

Output:

`8`

The function `kth_largest_product_sort_select()` computes the products using a list comprehension, sorts them in descending order, and returns the k-1 indexed value (since list indices start at 0) to find the kth largest product.

## Method 3: Use a Max Heap

Instead of sorting all products, we could use a max heap to store them. We repeatedly extract the maximum until we reach the kth element. While this method improves the efficiency of finding the kth largest product, building and extracting from the max heap has a time complexity of O(n^2 + k log n^2).

Here’s an example:

```import heapq

def kth_largest_product_max_heap(arr1, arr2, k):
products = [-x * y for x in arr1 for y in arr2]  # negative values to build a max heap
heapq.heapify(products)
largest_product = None
for _ in range(k):
largest_product = -heapq.heappop(products)
return largest_product

# Example use case:
arr1 = [3, 5, 2]
arr2 = [4, 8, 1]
k = 3
print(kth_largest_product_max_heap(arr1, arr2, k))
```

Output:

`8`

The example creates a max heap by inserting the negative of each product, then inverts the negatively pulled values to get the actual kth largest value.

## Method 4: Priority Queue with Early Termination

Employ a priority queue to store each product, but with early termination to avoid unnecessary calculations. When the size of the priority queue reaches k, we pop the smallest element after each new addition. This maintains a max heap of size k, improving efficiency to O(k log k + (n^2 – k) log k). This is beneficial when k is significantly smaller than n^2.

Here’s an example:

```import heapq

def kth_largest_product_priority_queue(arr1, arr2, k):
min_heap = []
for num1 in arr1:
for num2 in arr2:
heapq.heappush(min_heap, num1 * num2)
if len(min_heap) > k:
heapq.heappop(min_heap)
return min_heap[0]

# Example use case:
arr1 = [3, 5, 2]
arr2 = [4, 8, 1]
k = 3
print(kth_largest_product_priority_queue(arr1, arr2, k))
```

Output:

`8`

This function creates a min heap to ensure the heap never stores more than k elements, popping the smallest element each time the threshold is exceeded. This keeps the largest elements in the heap and returns the kth largest by the end of the iteration.

## Bonus One-Liner Method 5: Using Sorting with List Comprehensions

Leveraging the conciseness of Python, you can achieve the same result as some of the above methods with a one-liner. This is not necessarily the most efficient method for large datasets, but it’s a clear and compact solution. The time complexity is the same as the sort and select method, O(n^2 log n^2).

Here’s an example:

```arr1 = [3, 5, 2]
arr2 = [4, 8, 1]
k = 3
kth_largest_product = lambda a1, a2, k: sorted([x * y for x in a1 for y in a2], reverse=True)[k-1]
print(kth_largest_product(arr1, arr2, k))
```

Output:

`8`

The one-liner uses a lambda function to encapsulate the list comprehension and sorting logic, succinctly computing the kth largest product without the need for a separate function definition.

## Summary/Discussion

Each method has its own strengths and weaknesses:

• Method 1: Brute Force Approach. Simple to understand. Inefficient for large datasets.
• Method 2: Sort and Select. Faster than method 1 in practice. Still not optimal for large datasets.
• Method 3: Use a Max Heap. More efficient for finding the kth largest value. Still requires full computation of products.
• Method 4: Priority Queue with Early Termination. Optimizes for smaller values of k. More efficient when k is much smaller than n^2.
• Method 5: One-Liner Using Sorting with List Comprehensions. Compact and clear, but shares the efficiency drawbacks of the sort and select method.