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Ankur in DSF

Implementing Circular Queue in Java with Arrays

A circular queue is a fixed-size queue where the storage array is treated as a ring. When elements are dequeued from the front, those positions become available again for future insertions — eliminating the wasted space problem of linear array queues. The queue follows the FIFO (First In, First Out) principle: elements are added at the rear and removed from the front. This post presents a circular queue implementation in Java using a static array-backed design, demonstrating enqueue, dequeue, peek, and display operations through a menu-driven console program.

1 min read
Ankur in DSF

Implementing Doubly Linked List in Java

A Doubly Linked List (DLL) is a linked data structure where each node holds a value and two pointers: one pointing to the next node and one pointing to the previous node. This bidirectional linkage enables forward and reverse traversal — something a singly linked list cannot do without extra memory. A doubly-linked list: each node stores a value, a link to the next node, and a link to the previous node. In this post, we implement a DLL in Java that supports insertion at the first position, last position, after a given position, and before a given position — as well as deletion, search, and both forward and reverse display.

1 min read
Ankur in DSF

Implementation of Circular Queue in Java with Array

A Circular Queue (also called a Ring Buffer) is a queue data structure that treats its underlying array as if it were connected end-to-end in a circle. Unlike a linear queue, once the rear pointer reaches the end of the array, it wraps back to index 0 — allowing reuse of freed slots without shifting elements. This post implements a true circular queue in Java using an array, with proper wraparound logic for both the front and rear pointers. How a Circular Queue Works Both front and rear start at -1 (empty state). Enqueue: Advance rear with wraparound: rear = (rear + 1) % size. The queue is full when (rear + 1) % size == front. Dequeue: Advance front with wraparound: front = (front + 1) % size. Reset both to -1 when the last element is removed. Circular property: Elements can be added to index 0 again after front has moved past it.

1 min read
Ankur in DSF

Implementing Binary Search Tree in Java

A Binary Search Tree (BST) is a node-based binary tree data structure where each node stores a key, and every node in the left subtree holds a key strictly smaller than the parent, while every node in the right subtree holds a key strictly greater. This ordering property makes BSTs excellent for fast lookup, insertion, and deletion — all in O(log n) time on average. In this post, we implement a BST in Java that supports insertion, search, and three traversal orders: inorder, preorder, and postorder. BST Properties One node is designated the root of the tree. Each internal node contains a key and has at most two child subtrees. The left subtree of a node contains only keys strictly less than the node's key. The right subtree of a node contains only keys strictly greater than the node's key. Each subtree is itself a valid BST.

1 min read
Ankur in DSF

Evaluating Postfix Expression with Java

Postfix notation (also called Reverse Polish Notation or RPN) is a mathematical notation where every operator follows all of its operands. For example, the infix expression 3 + 4 becomes 3 4 + in postfix. This eliminates the need for parentheses and operator precedence rules, making it ideal for stack-based evaluation. In this post, we implement a Java program that evaluates a postfix expression using a stack. The program reads the expression as a string and produces the integer result.

1 min read
Ankur in AOAD

Program to Implement KMP Algorithm in Java

In this post, we’ll explore how to implement the Knuth-Morris-Pratt (KMP) string matching algorithm in Java. The KMP algorithm is an efficient way to search for a substring within a given string. It improves upon the brute-force approach by skipping over unnecessary comparisons after partial matches. What is the Knuth-Morris-Pratt (KMP) Algorithm? The KMP algorithm searches for a substring (pattern) in a larger string (text) efficiently by avoiding redundant comparisons. The key idea behind KMP is that when a mismatch happens, it uses previously gathered information about the pattern to shift the search window instead of starting from scratch. The algorithm uses two main components: Failure Function (Prefix Table): It helps determine how much the pattern should shift when a mismatch occurs. Search Process: Once the failure function is computed, the algorithm searches through the text to find the pattern.

1 min read
Ankur in AOAD

Program to Implement K-Naive Algorithm in Java

In this post, we’ll implement the K-Naive (Brute-Force) string matching algorithm in Java. This is the simplest form of string search, where we check for the presence of a pattern in a text by checking one by one at every possible position. What is the K-Naive (Brute-Force) Algorithm? The Brute-Force algorithm searches for a substring (pattern) within a larger string (text) by checking for a match at every position in the text. It is simple to understand and implement but can be inefficient for large inputs.

1 min read
Ankur in AOAD

Java Program for Longest Common Subsequence (LCS) Problem

Learn how to solve the Longest Common Subsequence (LCS) problem in Java using dynamic programming. Includes a CC diagram, annotated DP table, traceback explanation, sample output, and a step-by-step output walkthrough.

1 min read
Ankur in AOAD

Implementation of Graph Coloring Algorithm in Java

Learn how to implement the Graph Coloring (m-colouring backtracking) algorithm in Java. Includes a CC diagram, annotated code with descriptive variable names, sample output, and a step-by-step explanation.

1 min read
Ankur in AOAD

Implementing Sum of Subset by Backtracking in Java

The subset sum problem is a classic problem in computer science and is often solved using backtracking. The goal is to find subsets of elements selected from a given set whose sum adds up to a given number K. Understanding the Problem Given a set of n positive integers and a value m, the task is to determine all possible subsets whose sum is equal to m. This problem can be efficiently solved using backtracking. Approach: Backtracking Backtracking is a general algorithmic technique that incrementally builds candidates to a solution and abandons a candidate as soon as it is determined that the candidate cannot lead to a valid solution. In the subset sum problem, we: Start with an empty subset. Add elements one by one while keeping track of the sum. If the sum equals m, print the subset. If the sum exceeds m, backtrack and try another possibility.

1 min read