Solving Leetcode Interviews in Seconds with AI: Find the Integer Added to Array II
Introduction
In this blog post, we will explore how to solve the LeetCode problem "3132" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like Chatmagic, we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.
Problem Statement
You are given two integer arrays nums1 and nums2. From nums1 two elements have been removed, and all other elements have been increased (or decreased in the case of negative) by an integer, represented by the variable x. As a result, nums1 becomes equal to nums2. Two arrays are considered equal when they contain the same integers with the same frequencies. Return the minimum possible integer x that achieves this equivalence. Example 1: Input: nums1 = [4,20,16,12,8], nums2 = [14,18,10] Output: -2 Explanation: After removing elements at indices [0,4] and adding -2, nums1 becomes [18,14,10]. Example 2: Input: nums1 = [3,5,5,3], nums2 = [7,7] Output: 2 Explanation: After removing elements at indices [0,3] and adding 2, nums1 becomes [7,7]. Constraints: 3 <= nums1.length <= 200 nums2.length == nums1.length - 2 0 <= nums1[i], nums2[i] <= 1000 The test cases are generated in a way that there is an integer x such that nums1 can become equal to nums2 by removing two elements and adding x to each element of nums1.
Explanation
Here's the breakdown of the approach, complexities, and the Python code:
High-level Approach:
- Iterate through all possible pairs of indices to remove from
nums1. - For each pair, calculate the potential value of
xthat makes the remaining elements ofnums1equal tonums2. This is done by trying all possible differences. - Check if the calculated
xworks by verifying if addingxto the remaining elements ofnums1results innums2having the same frequencies.
- Iterate through all possible pairs of indices to remove from
Complexity:
- Runtime Complexity: O(n3), where n is the length of
nums1. - Storage Complexity: O(n)
- Runtime Complexity: O(n3), where n is the length of
Code
from collections import Counter
def solve():
nums1 = list(map(int, input().split()))
nums2 = list(map(int, input().split()))
n1 = len(nums1)
n2 = len(nums2)
min_x = float('inf')
for i in range(n1):
for j in range(i + 1, n1):
remaining_nums1 = []
for k in range(n1):
if k != i and k != j:
remaining_nums1.append(nums1[k])
for num1 in remaining_nums1:
for num2 in nums2:
x = num2 - num1
temp_nums1 = [val + x for val in remaining_nums1]
counter1 = Counter(temp_nums1)
counter2 = Counter(nums2)
if counter1 == counter2:
min_x = min(min_x, x)
print(min_x)
# Read input and execute the solution
solve()