Given an array nums of distinct positive integers, return the number of tuples (a, b, c, d) such that a * b = c * d where a, b, c, and d are elements of nums, and a != b != c != d.
Example 1:
Input: nums = [2,3,4,6] Output: 8 Explanation: There are 8 valid tuples: (2,6,3,4) , (2,6,4,3) , (6,2,3,4) , (6,2,4,3) (3,4,2,6) , (4,3,2,6) , (3,4,6,2) , (4,3,6,2)
Example 2:
Input: nums = [1,2,4,5,10] Output: 16 Explanation: There are 16 valid tuples: (1,10,2,5) , (1,10,5,2) , (10,1,2,5) , (10,1,5,2) (2,5,1,10) , (2,5,10,1) , (5,2,1,10) , (5,2,10,1) (2,10,4,5) , (2,10,5,4) , (10,2,4,5) , (10,2,5,4) (4,5,2,10) , (4,5,10,2) , (5,4,2,10) , (5,4,10,2)
Constraints:
1 <= nums.length <= 10001 <= nums[i] <= 104nums are distinct.In this approach, we will use a hashmap (dictionary) to keep track of products of each pair. The key will be the product, and the value will be the number of ways a particular product can be formed. We will iterate over each unique pair of numbers in the array, compute their product, and store the count of each product in the hashmap. For every pair, the count in the hashmap will give the number of tuples that can be formed with this product. For every new product count, the number of tuples formed can be calculated using combinatorial arithmetic logic (multiplying counts of previously seen pairs forming the same product).
This C solution uses an array to simulate a hashmap we utilize as a product counter. We iterate over all pairs, compute the product, and check how many pairs have previously resulted in the same product. For each such pair, there are 8 tuples that can be formed.
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The time complexity is O(N^2), where N is the length of the array, as we check all pairs. The space complexity is O(U), where U is the maximum possible product of two numbers, due to the product counter array.
This approach aims to reduce time complexity by first sorting the array, and leveraging the two-pointer technique to find pairs with the same product more efficiently. Once the array is sorted, for each pair, the pointers can be adjusted based on whether the product is less than or greater than the target product for current analysis.
This solution uses sorting followed by a two-pointer method to find pairs. By iterating each pair and adjusting the pointers based on comparisons with the target product, the solution becomes more efficient in certain conditions than a simple brute-force.
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The time complexity is O(N^3) in the worst case, factoring in sorting and two-pointer traversal per pair. Space complexity is O(1) apart from sorting requirements.
| Approach | Complexity |
|---|---|
| Hashmap Based Approach | The time complexity is O(N^2), where N is the length of the array, as we check all pairs. The space complexity is O(U), where U is the maximum possible product of two numbers, due to the product counter array. |
| Two Pointers Approach | The time complexity is O(N^3) in the worst case, factoring in sorting and two-pointer traversal per pair. Space complexity is O(1) apart from sorting requirements. |
Tuple with Same Product - Leetcode 1726 - Python • NeetCodeIO • 11,618 views views
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