Dijkstra's Algorithm Dijkstra’s Algorithm is a method used to find the shortest path between nodes in a graph, particularly in weighted graphs where each edge has a numerical weight assigned to it. The algorithm operates by iteratively selecting the vertex with the lowest tentative distance from a source node and updating the distances of its […]

Minimum Spanning Tree (MST) A Minimum Spanning Tree (MST) is a fundamental concept in graph theory and optimization. It is a subset of the edges of a connected, undirected graph that forms a tree (a connected graph with no cycles) and connects all the vertices together while minimizing the total sum of edge weights. In

Graph Algorithms Graph Algorithms refer to a set of computational techniques and methods designed to analyze, manipulate, and solve problems related to graphs. A graph is a mathematical structure consisting of vertices (nodes) and edges (connections) that link pairs of vertices. Graph algorithms operate on this data structure to uncover patterns, properties, and relationships within

Backtracking Algorithms Backtracking Algorithms are a type of algorithmic technique used to systematically search for solutions to a problem by exploring various potential configurations and backtracking from paths that lead to dead ends. The primary characteristic of backtracking algorithms is their ability to incrementally build a solution candidate, exploring different choices at each step, and

Binary Search Tree (BST) A Binary Search Tree (BST) is a hierarchical data structure used for efficient searching, insertion, and deletion operations. In a binary search tree, each node contains a key value and has at most two children: a left child and a right child. The key property of a binary search tree is

Greedy Algorithms Greedy Algorithms are problem-solving techniques used in computer science to find optimal solutions by making locally optimal choices at each step. These algorithms aim to solve optimization problems by selecting the best available option at the current stage, without considering the consequences of that choice in the future. The key features of greedy

Divide and Conquer Algorithm Divide and Conquer is a fundamental algorithmic paradigm used to solve problems by breaking them down into smaller subproblems, solving the subproblems independently, and then combining their solutions to obtain the final solution. The Divide and Conquer approach typically involves three steps: Divide: Break the problem into smaller, more manageable subproblems

A Complete Guide to Dynamic Programming Dynamic Programming is a problem-solving technique based on the principle of overlapping subproblems and optimal substructure. The fundamental idea behind dynamic programming is to solve each subproblem only once and store its solution, avoiding redundant computations. This approach leads to significant efficiency gains, especially for problems with overlapping subproblems,

Recurrence Relation in Algorithm A Recurrence Relation is a mathematical equation that defines a sequence recursively, where each term is defined in terms of preceding terms. It typically consists of two parts: an initial condition or base case that specifies the first few terms of the sequence, and a recurrence relation that describes how subsequent

Pseudocode Conventions Pseudocode is a high-level description of an algorithm that uses natural language mixed with some programming language-like constructs. It provides a step-by-step outline of the algorithm’s logic without being tied to any specific programming language syntax. Pseudocode allows algorithm designers to express their ideas in a concise and understandable manner before actual coding