Algorithmic Essentials: 6 Must-Knows for Developers

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In the ever-evolving realm of software development, grasping fundamental algorithms is like having a powerful toolkit that empowers developers to create efficient and sturdy solutions. This blog takes a deep dive into the core algorithms that serve as the foundation for solving computational problems, offering a comprehensive guide for developers looking to boost their skills and comprehension.

Whether you’re a seasoned coder or just starting your programming journey, these essential algorithms are the basic components that can enhance your problem-solving abilities.

Now, let’s begin learning these 6 algorithms to help you with your coding challenges and improve your problem solving skills.

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1. Sorting Algorithm

Sorting algorithm helps developers to arrange the order of items in a list. These algorithms play a crucial role in organizing information efficiently. We added a list of 4 sorting algorithms for you to check out.

1. Bubble Sort
2. MergeSort
3. QuickSort
4. HeapSort

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2. Searching Algorithm

Searching algorithm finds an element in a data set. It is widely used and searching algorithms are necessary for a developer. Search engines like Google extensively use searching algorithms to retrieve relevant information from the vast amount of data available on the internet. Additionally, other industries, such as finance, healthcare, and logistics, utilize searching algorithms to efficiently locate specific data points or records.

1. Binary Search
2. Breadth-First Search (BFS)
3. Depth-First Search (DFS)

3. Dynamic Programming

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Dynamic Programming (DP) is an algorithmic technique for solving a complex problem by breaking it down into simpler sub-problems and solving each sub-problem only once. This technique is particularly useful for optimization problems, where the goal is to find the best solution among a set of possible solutions. There are some key principles of dynamic programming. These include:

1. Optimal Substructure: A problem has an optimal substructure if its optimal solution can be constructed from the optimal solutions of its subproblems.
2. Overlapping Subproblems: The problem can be broken down into subproblems, and the same subproblems are solved multiple times.

4. Recursion Algorithm

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Recursion is a programming technique where a function calls itself in its own definition. This approach allows solving complex problems by breaking them down into simpler instances. In a recursive algorithm, the problem is divided into smaller subproblems, and the solution is computed by solving these subproblems recursively. There are 3 key elements of recursive algorithms.

1. Base Case: Every recursive algorithm must have a base case, a condition that stops the recursion. Without a base case, the function would keep calling itself indefinitely, leading to a stack overflow.
2. Divide and Conquer: The problem is divided into smaller, more manageable subproblems. Each recursive call works on a smaller portion of the original problem.
3. Self-Calling: The function calls itself with a modified version of the original problem. This is often the key characteristic of recursive algorithms.

Note : The concept of self-calling, which is characteristic of recursive algorithms, is distinct from dynamic programming, although both involve breaking down complex problems into simpler subproblems.

5. Divide and Conquer

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Divide and conquer is another problem-solving technique that, like recursion and dynamic programming, involves breaking down a problem into smaller, more manageable subproblems.

The fundamental idea is to divide the problem into non-overlapping subproblems, conquer each subproblem by solving it recursively or iteratively, and then combine the solutions of the subproblems to obtain the solution for the original problem.

There are 3 main steps of the divide and conquer approach:

1. Divide: Divide the original problem into sub-problems
2. Conquer: Solve each sub-problem one at a time, recursively.
3. Combine: Put the solutions to the sub-problems together to get the solution to the whole problem.

6. Hashing

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Hashing is a technique or process that uses a hash function to map keys and values into a hash table. The process of applying a hash function to data is known as “hashing,” and the output is often referred to as a “hash code” or “hash value.” Hashing is done to allow for quicker access to elements.

Key points of hashing :

1. Hash Function: A hash function takes an input (or “key”) and produces a fixed-size string of characters, which is typically a hash code. The goal of a good hash function is to evenly distribute inputs across the range of possible hash codes, minimizing the likelihood of collisions (such as two different inputs producing the same hash code).
2. Hash Table: A hash table is a data structure that uses hash functions to map keys to indexes in an array. Each index in the array is called a “bucket.”
3. Collisions: Collisions occur when two different inputs produce the same hash code. Handling collisions is a critical aspect of designing effective hashing algorithms.
4. Applications: Hashing is widely used in multiple applications, such as: Database indexing, cryptography, and distributed systems.
5. Hashing Algorithms: Different hash functions are suitable for different purposes. Some common hash functions include SHA-256 for cryptographic applications and MurmurHash or DJB2 for general-purpose hashing. ( Check Okta for more information towards the usage of hashing algorithms. )