# Must-Know Algorithms for Technical Interviews

Are you preparing for a technical interview at a top tech company like FANG? Do you want to ace your coding interview and impress your interviewer with your algorithmic skills? Then you've come to the right place! In this article, we'll cover some of the must-know algorithms for technical interviews that will help you solve complex problems efficiently and effectively.

## What are Algorithms?

Before we dive into the specific algorithms, let's first define what we mean by algorithms. In simple terms, an algorithm is a set of instructions that a computer follows to solve a problem. It's like a recipe that tells the computer what steps to take to achieve a desired outcome. Algorithms are essential in computer science because they allow us to solve complex problems efficiently and effectively.

## Why are Algorithms Important for Technical Interviews?

Now that we know what algorithms are, let's talk about why they are important for technical interviews. Technical interviews at top tech companies like FANG are notoriously challenging, and they often involve complex algorithmic problems. Interviewers want to see how well you can solve these problems and how quickly you can come up with a solution. Knowing the right algorithms can help you solve these problems more efficiently and impress your interviewer with your skills.

## Must-Know Algorithms for Technical Interviews

Without further ado, let's dive into the must-know algorithms for technical interviews. These algorithms are not only commonly asked in technical interviews but also widely used in computer science and software engineering.

### 1. Binary Search

Binary search is a classic algorithm that allows you to search for a specific value in a sorted array. The algorithm works by repeatedly dividing the array in half and comparing the middle element with the target value. If the middle element is equal to the target value, the search is successful. Otherwise, the algorithm narrows down the search to the left or right half of the array and repeats the process until the target value is found or the search space is exhausted.

Binary search is a very efficient algorithm with a time complexity of O(log n), where n is the size of the array. It's widely used in computer science and software engineering for searching and sorting operations.

### 2. Merge Sort

Merge sort is a popular sorting algorithm that works by dividing the array into smaller subarrays, sorting them recursively, and then merging them back together. The algorithm works by repeatedly dividing the array in half until each subarray contains only one element. Then, it merges the subarrays back together in a sorted order.

Merge sort has a time complexity of O(n log n), where n is the size of the array. It's a very efficient algorithm for sorting large datasets and is widely used in computer science and software engineering.

### 3. Quick Sort

Quick sort is another popular sorting algorithm that works by partitioning the array into two subarrays, one with elements smaller than a pivot element and one with elements larger than the pivot element. The algorithm then recursively sorts the two subarrays and combines them back together.

Quick sort has a time complexity of O(n log n) on average but can have a worst-case time complexity of O(n^2) if the pivot element is poorly chosen. Despite this, quick sort is still widely used in computer science and software engineering for sorting operations.

### 4. Depth-First Search

Depth-first search is a graph traversal algorithm that works by exploring as far as possible along each branch before backtracking. The algorithm starts at a root node and explores each of its neighbors recursively until it reaches a leaf node. Then, it backtracks to the previous node and explores its other neighbors until all nodes have been visited.

Depth-first search has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges in the graph. It's widely used in computer science and software engineering for graph traversal and pathfinding operations.

### 5. Breadth-First Search

Breadth-first search is another graph traversal algorithm that works by exploring all the neighbors of a node before moving on to the next level of nodes. The algorithm starts at a root node and explores all its neighbors before moving on to the neighbors of its neighbors and so on.

Breadth-first search has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges in the graph. It's widely used in computer science and software engineering for graph traversal and pathfinding operations.

### 6. Dijkstra's Algorithm

Dijkstra's algorithm is a shortest path algorithm that works by finding the shortest path between a source node and all other nodes in a weighted graph. The algorithm works by maintaining a set of visited nodes and a set of unvisited nodes, and it repeatedly selects the unvisited node with the smallest distance from the source node and updates the distances of its neighbors.

Dijkstra's algorithm has a time complexity of O(V^2), where V is the number of vertices in the graph. However, with the use of a priority queue, the time complexity can be reduced to O(E log V), where E is the number of edges in the graph. Dijkstra's algorithm is widely used in computer science and software engineering for pathfinding and network routing operations.

### 7. A* Algorithm

A* algorithm is another shortest path algorithm that works by finding the shortest path between a source node and a target node in a weighted graph. The algorithm works by maintaining a set of visited nodes and a set of unvisited nodes, and it repeatedly selects the unvisited node with the smallest sum of the distance from the source node and the estimated distance to the target node.

A* algorithm has a time complexity of O(E log V), where E is the number of edges in the graph and V is the number of vertices. It's widely used in computer science and software engineering for pathfinding and game AI operations.

## Conclusion

In conclusion, knowing the right algorithms can make a big difference in your technical interview performance. The algorithms we covered in this article are not only commonly asked in technical interviews but also widely used in computer science and software engineering. By mastering these algorithms, you'll be able to solve complex problems efficiently and effectively, impress your interviewer with your skills, and land your dream job at a top tech company like FANG. So, what are you waiting for? Start practicing these algorithms today and ace your coding interview!

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