What are Algorithms?

What are Algorithms?

Algorithms are a central concept in computer science and mathematics. They represent a set of systematic procedures meant to perform a few tasks or a specific task. All the instructions are well-defined and, in a sequence, helping to process data, complete a task, or solve a computation task. It is a procedure to solve a computational or mathematical problem that involves recursive operations.

Characteristics of Algorithms

Algorithms have certain well-defined characteristics:

Finiteness: Every algorithm that is defined to perform a task must have fixed steps that will reach the end and produce an output. The process must end after some steps.

Well-defined sequence of steps: Every step in an algorithm must be clear and concise without any ambiguity. Every step should be exactly defined for the specified action.

Inputs: Every algorithm must have one or more inputs that have to be provided before the algorithm starts. The inputs are defined based on the pre-defined range of variables.

Output: An output is the outcome of the series of steps provided for a desired result. There could be one or more outputs resulting from the algorithm processing.

Effectiveness: The algorithm should be so simple, that anyone can trace it out with paper and pencil. The algorithm should be simple and easy to execute.

Types of Algorithms

There are different and several types of algorithms.

Sorting algorithms: The sorting algorithms helps the grouped data to be sorted in ascending or descending manner.

Search algorithms: The search algorithms search and retrieve information / data that are stored in a database. Some examples would be linear search, binary search etc.

Graph algorithms: Graph algorithms are designed to solve the problems relating to graph theory. These algorithms determine the relation between nodes and links.

Dynamic programming algorithms: This algorithm is useful in solving optimization problems. This algorithm breaks complex problems into sub-problems. The results of the sub-problems are stored to avoid redundancy.

Divide and conquer: This algorithm divides the problem into smaller subproblems and solve repetitively and finally combine all the solutions.

Greedy algorithms: Greedy algorithms always make a choice that is best at the moment. It makes a locally optimal choice which the algorithm assumes to be the globally optimal solution.

Backtracking algorithm: A backtracking algorithm uses a brute force method by building solutions to all the problems and then eliminating solutions that fail to satisfy the constraints.

Bubble sort algorithms: This is a sorting algorithm which compares the adjacent elements and swaps if in wrong order.

DATA STRUCTURES

A data structure defines the data organization, processing, retrieval and storage format. Data structures provide the format to manage extensive data for various applications, databases, and operating systems.

Classification of data structures

Data structures are classified into linear data structures and Non-linear data structures.

Linear data structures:

Arrays: An array is the simplest data structure. It is a collection of values or variables of the same memory size. Each data element can be accessed using its index number.

Linked lists: A linked list stores data as a sequence of elements. Each element is referred as a node, which has a orientation to the next node in the list.

Stacks: A stack is an abstract data structure that works on the last in the first out method. The element that is inserted last will be removed first.

Queues: A queue stores data in the data structure sequentially. It uses the first-in-first-out method.

Non-linear data structures

Trees: This is a non-linear data structure representing a hierarchy of data. A single node is termed as the root, and all the other nodes represent the hierarchical relationship.

Graphs: Graphs in data structures represent relationships and structures like transportation networks, computer networks, social networks, etc.

Conclusion: Data structures and algorithms are interrelated and form the backbone of the programming. They are crucial for problem-solving. Data structure provides a framework, while algorithms help to manipulate the data.