Transitioning to a new job after leaving your previous one can be a challenge for many people. If you wish to work in the IT and software fields, problem-solving skills in algorithms are essential. This course will introduce a method to improve your skills by solving algorithm problems frequently presented in developer recruitment tests.
Problem Description
Problem: Intersection of Two Arrays
Given two arrays, write a function to find the intersection of the two arrays. The intersection refers to the set of elements that exist in both arrays.
Input
- Integer array A (length: m, 1 ≤ m ≤ 10^5)
- Integer array B (length: n, 1 ≤ n ≤ 10^5)
Output
Return an array of the intersection elements sorted in ascending order. If there are no intersections, return an empty array.
Example
Input: A = [1, 2, 2, 1], B = [2, 2] Output: [2] Input: A = [4, 9, 5], B = [9, 4, 9, 8, 4] Output: [4, 9]
Solution Process
Problem Analysis
The task is to find the common elements that exist in both given arrays A and B. After identifying the identical elements among the elements of both arrays, the intersection should be returned, ensuring that each duplicate element is included only once.
Approach
There are several ways to solve this problem, but an efficient method is to use a hash table (in Python, you can use dictionaries or sets). With this approach, you can convert both arrays into sets and easily find the intersection.
Implementation
In step 1, convert both arrays into sets; in step 2, find the intersection between the sets.
In step 3, sort the intersection result and return it.
def intersection(A, B):
# Step 1: Convert arrays to sets
set_A = set(A)
set_B = set(B)
# Step 2: Find intersection
intersection_set = set_A.intersection(set_B)
# Step 3: Sort and convert to list
return sorted(list(intersection_set))
# Example execution
A = [1, 2, 2, 1]
B = [2, 2]
print(intersection(A, B)) # Output: [2]
A = [4, 9, 5]
B = [9, 4, 9, 8, 4]
print(intersection(A, B)) # Output: [4, 9]
Complexity Analysis
Time complexity: O(m + n) – The time taken to convert the two arrays into sets, and the time required to calculate the intersection.
Space complexity: O(m + n) – In the worst case, if both arrays are composed of different elements, space is needed to store the sets.
Conclusion
This algorithm problem frequently appears in coding tests, and can be solved efficiently by utilizing Python’s sets. It is important to systematically approach algorithm problems by dividing them into stages of problem analysis, approach, implementation, and complexity analysis.
Preparing for coding tests may be a challenging journey, but consistent practice and tackling various problems can help build confidence. I hope you can develop the ability to solve algorithm problems alongside your job transition preparation.