Interview (DSA+SD) Prep Series Part:-3

Mastering Data Structures and Algorithms: A Guide to Array Problems in Interviews

Data Structures and Algorithms (DSA) form the backbone of technical interviews, especially when it comes to arrays. Arrays, a fundamental data structure, are a sequential collection of elements indexed by contiguous integers. Here's an exploration of three renowned array problems commonly encountered in interviews, along with multiple approaches to tackle them effectively.

1. Problem: Two Sum

Given an array of integers nums and an integer target, return the indices of the two numbers that add up to the target.

Approach 1: Brute Force

Iterate through each element i and, for each element, check if there is another element that adds up to the target.

Approach 2: Hash Map

Use a hash map to store elements and their indices. While traversing the array, check if the complement = target - nums[i] is present in the hash map.

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2. Problem: Container With Most Water

Given n non-negative integers height where each height[i] represents the height of a vertical line at position i, find two lines that, together with the x-axis, forms a container that holds the most water.

Approach 1: Brute Force

Consider all possible pairs and calculate the area formed by each pair. Return the maximum area obtained.

Approach 2: Two Pointers

Use two pointers starting from both ends of the array and gradually move them towards the center while keeping track of the maximum area.

3. Problem: Merge Intervals

Given an array of intervals where intervals[i] = [start_i, end_i], merge all overlapping intervals.

Approach 1: Sorting

Sort the intervals based on their start times. Then, iterate through the sorted intervals and merge overlapping intervals.

Approach 2: Stack

Iterate through the intervals and maintain a stack to merge overlapping intervals.

Tips to Solve Array Problems in Interviews: 💻

• Clarify the Problem: Ensure you understand the problem statement and ask clarifying questions if necessary.

• Consider Edge Cases: Pay attention to edge cases like empty arrays, single elements, or negative numbers.

• Brute Force vs. Optimized Approach: Start with a brute force solution and then optimize it gradually.

• Use of Space and Time: Analyze the time and space complexities of your solution and optimize accordingly.

• Test Your Solution: Verify your code with sample test cases to ensure correctness.

Time Complexities to Take Care: ⏰✅✅

• O(N^2): Brute force solutions often lead to quadratic time complexity.

• O(N log N): Sorting algorithms like merge sort or quicksort typically result in this time complexity.

• O(N): Linear time complexity is ideal and achieved in efficient solutions.

Mastering array problems with various approaches and understanding their time complexities can significantly enhance your problem-solving skills

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