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This is the code repository of book "Problem Solving in Data Structures & Algorithms Using Java".

About The Book

• This textbook provides in depth coverage of various Data Structures and Algorithms.
• Concepts are discussed in easy to understand manner.
• Large number of diagrams are provided to grasp concepts easily.
• Time and Space complexities of various algorithms are discussed.
• Helpful for interviews preparation and competitive coding.
• Large number of interview questions are solved.
• Java solutions are provided with input and output.
• Guide you through how to solve new problems in programming interview of various software companies.

Table of Contents

• Chapter 0: How to use this book.
• Chapter 1: Algorithms Analysis
• Chapter 2: Approach to solve algorithm design problems
• Chapter 3: Abstract Data Type & JAVA Collections
• Chapter 4: Searching
• Chapter 5: Sorting
• Chapter 6: Linked List
• Chapter 7: Stack
• Chapter 8: Queue
• Chapter 9: Tree
• Chapter 10: Priority Queue
• Chapter 11: Hash-Table
• Chapter 12: Graphs
• Chapter 13: String Algorithms
• Chapter 14: Algorithm Design Techniques
• Chapter 15: Brute Force Algorithm
• Chapter 16: Greedy Algorithm
• Chapter 17: Divide & Conquer
• Chapter 18: Dynamic Programming
• Chapter 19: Backtracking
• Chapter 20: Complexity Theory

Problem Solving in Data Structures and Algorithms Using Java

By hemant jain.

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Data structures and problem solving using Java

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"Problem Solving in Data Structures & Algorithms" is a series of books about the usage of Data Structures and Algorithms in computer programming. The book is easy to follow and is written for interview preparation point of view. In various books, the examples are solved in various languages like C, C++, Java, C#, Python, VB, JavaScript and PHP. Books Composition This book is designed for interviews so in Chapter 0, various preparation plans are proposed. Then in chapters 1, a brief introduction of the programming language and concept of recursion is explained. A number of problems based on recursion and array are explained. Then in the coming chapter, we will be looking into complexity analysis. Then we will be looking into Sorting & Searching techniques. Then will look into the various data structures and their algorithms. We will be looking into a Linked List, Stack, Queue, Trees, Heap, Hash Table and Graphs. Then we will be looking into algorithm analysis, we will be looking into Brute Force algorithms, Greedy algorithms, Divide & Conquer algorithms, Dynamic Programming, and Backtracking. In the end, we will be looking into System Design, which will give a systematic approach for solving the design problems in an Interview.

• Publication Years 2016 - 2018
• Publication counts 13
• Citation count 0
• Available for Download 0
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Course Lectures

• DSA Tutorial
• Data Structures
• Linked List
• Dynamic Programming
• Binary Tree
• Binary Search Tree
• Divide & Conquer
• Mathematical
• Backtracking
• Branch and Bound
• Pattern Searching

Learn Data Structures and Algorithms | DSA Tutorial

Data Structures and Algorithms (DSA) refer to the study of methods for organizing and storing data and the design of procedures (algorithms) for solving problems, which operate on these data structures. DSA is one of the most important skills that every computer science student must have. It is often seen that people with good knowledge of these technologies are better programmers than others and thus, crack the interviews of almost every tech giant, including companies like Google, Microsoft, Amazon, and Facebook (now Meta). This DSA tutorial aims to help you learn Data Structures and Algorithms (DSA) quickly and easily.

Data Structure & Algorithm Tutorial

Table of Content

DSA Full Form

What is dsa.

How to learn DSA?

Learn Data Structures

Common data structures to learn:, 3. linked lists, 4. matrix/grid, learn algorithms, 1. searching algorithm, 2. sorting algorithm, 3. divide and conquer algorithm, 4. greedy algorithms, 5. recursion, 6. backtracking algorithm, 7. dynamic programming.

• 8. Graph Algorithms:
• 9. Pattern Searching
• 10. Mathematical Algorithms

11. Geometric Algorithms

12. bitwise algorithms, 13. randomized algorithms, 14. branch and bound algorithm, learn about complexities, asymptotic notation, practice problem cheat sheets.

The term DSA stands for  Data Structures and Algorithms , in the context of Computer Science.

Data Structures and Algorithms (DSA) refer to the study of methods for organizing and storing data and the design of procedures (algorithms) for solving problems, which operate on these data structures.

The first and foremost thing is dividing the total procedure into little pieces which need to be done sequentially. The complete process to learn DSA from scratch can be broken into 5 parts:

• Learn atleast one programming language (We leave this to your choice.)
• Learn about Time and Space complexities
• Practice Problems on DSA

Hoping you have learned a programming language of your choice, let us move forward with the next step to learn DSA in this DSA tutorial.

Here comes the most important and the most awaited stage of the roadmap for learning data structure and algorithm – the stage where you start learning about DSA.

The topic of DSA consists of two parts: 

• Algorithms 

Though they are two different things, they are highly interrelated, and it is very important to follow the right track to learn them most efficiently. If you are confused about which one to learn first, we recommend you to go through our detailed analysis on the topic: What should I learn first- Data Structures or Algorithms?

Here we have followed the flow of learning a data structure and then the most related and important algorithms used by that data structure.

Data structures  are essential components that help organize and store data efficiently in computer memory. They provide a way to manage and manipulate data effectively, enabling faster access, insertion, and deletion operations.

Common data structures include  arrays, linked lists, stacks, queues, trees, and graphs  , each serving specific purposes based on the requirements of the problem at hand. Understanding data structures is fundamental for designing efficient algorithms and optimizing software performance.

Array is a linear data structure that stores a collection of elements of the same data type. Elements are allocated contiguous memory, allowing for constant-time access. Each element has a unique index number.

• Traversal : Iterating through the elements of an array.
• Insertion : Adding an element to the array at a specific index.
• Deletion : Removing an element from the array at a specific index.
• Searching : Finding an element in the array by its value or index.
• One-dimensional array : A simple array with a single dimension.
• Multidimensional array : An array with multiple dimensions, such as a matrix.
• Storing data in a sequential manner
• Implementing queues, stacks, and other data structures
• Representing matrices and tables
• Arrays Tutorial
• Top 50 Array Coding Problems for Interviews
• Practice problems on Arrays

A string is a sequence of characters, typically used to represent text. It is considered a data type that allows for the manipulation and processing of textual data in computer programs.

• Concatenation : Joining two strings together.
• Comparison : Comparing two strings lexicographically.
• Substring extraction : Extracting a substring from a string.
• Search : Searching for a substring within a string.
• Modification : Changing or replacing characters within a string.
• Text processing
• Pattern matching
• Data validation
• Database management
• String Tutorial
• Top 50 String Coding Problems for Interviews
• Practice Problems on String

A linked list is a linear data structure that stores data in nodes, which are connected by pointers. Unlike arrays, linked lists are not stored in contiguous memory locations.

• Dynamic : Linked lists can be easily resized by adding or removing nodes.
• Non-contiguous : Nodes are stored in random memory locations and connected by pointers.
• Sequential access : Nodes can only be accessed sequentially, starting from the head of the list.
• Creation : Creating a new linked list or adding a new node to an existing list.
• Traversal : Iterating through the list and accessing each node.
• Insertion : Adding a new node at a specific position in the list.
• Deletion : Removing a node from the list.
• Search : Finding a node with a specific value in the list.
• Singly Linked List : Each node points to the next node in the list.
• Doubly Linked List : Each node points to both the next and previous nodes in the list.
• Circular Linked List : The last node points back to the first node, forming a circular loop.
• Linked lists are used in various applications, including:
• Implementing queues and stacks
• Representing graphs and trees
• Maintaining ordered data
• Memory management
• Linked List Tutorial
• Top 50 Problems on Linked List for Interviews
• Practice problems on Linked Lists

A matrix is a two-dimensional array of elements, arranged in rows and columns. It is represented as a rectangular grid, with each element at the intersection of a row and column.

• Rows : Horizontal lines of elements in a matrix.
• Columns : Vertical lines of elements in a matrix.
• Dimensions : The number of rows and columns in a matrix (e.g., a 3×4 matrix has 3 rows and 4 columns).
• Element Access : Elements can be accessed using row and column indices (e.g., M[2][3] refers to the element in row 2, column 3).
• Image processing
• Data analysis
• Optimization problems
• Matrix/Grid Tutorial
• Top 50 Problems on Matrix/Grid for Interviews
• Practice Problems on Matrix/Grid

Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out) . LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first, comes out last.

• Push : Adds an element to the top of the stack
• Pop : Removes and returns the element at the top of the stack
• Peek : Returns the element at the top of the stack without removing it
• Size : Returns the number of elements in the stack
• IsEmpty : Checks if the stack is empty
• Function calls
• Expression evaluation
• Undo/redo operations
• Stack Tutorial
• Top 50 Problems on Stack for Interviews
• Practice problems on Stack

A Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of “ First in, First out ” ( FIFO ), where the first element added to the queue is the first one to be removed

• Enqueue : Adds an element to the rear of the queue
• Dequeue : Removes an element from the front of the queue
• Peek : Retrieves the front element without removing it
• IsEmpty : Checks if the queue is empty
• IsFull : Checks if the queue is full
• Circular Queue : Last element connects to the first element
• Double-Ended Queue (Deque) : Operations can be performed from both ends
• Priority Queue : Elements are arranged based on priority
• Job scheduling
• Message queuing
• Simulation modeling
• Data buffering
• Queue Tutorial
• Top 50 Problems on Queue for Interviews
• Practice problems on Queue

A Heap is a complete binary tree data structure that satisfies the heap property: for every node, the value of its children is less than or equal to its own value. Heaps are usually used to implement priority queues , where the smallest (or largest) element is always at the root of the tree.

• Insert : Adds a new element to the heap while maintaining heap properties.
• Extract-Max/Extract-Min : Removes the root element and restructures the heap.
• Increase/Decrease-Key : Updates the value of a node and restructures the heap.
• Max-Heap : Root node has the maximum value among its children.
• Min-Heap : Root node has the minimum value among its children.
• Priority queues
• Graph algorithms (e.g., Dijkstra’s algorithm)
• Heap Tutorial
• Top 50 Problems on Heap for Interviews
• Practice Problems on Heap

Hashing is a technique that generates a fixed-size output (hash value) from an input of variable size using mathematical formulas called hash functions . Hashing is used to determine an index or location for storing an item in a data structure, allowing for efficient retrieval and insertion.

• Hash Function : A mathematical function that maps an input to a hash value.
• Hash Table : A data structure that stores key-value pairs, where the key is a hash value and the value is the actual data.
• Collision : When two different keys produce the same hash value.
• Division Method : Divides the input by a constant and uses the remainder as the hash value.
• Mid Square Method: Squares the input and takes the middle digits as the hash value.
• Folding Method : Divides the input into equal-sized blocks and adds them together to get the hash value.
• Multiplication Method : Multiplies the input by a constant and takes the fractional part as the hash value.
• Separate Chaining (Open Hashing) : Stores colliding elements in a linked list at the corresponding hash value.
• Open Addressing (Closed Hashing) : Uses various strategies to find an alternative location for colliding elements within the hash table.
• Efficiently storing and retrieving data in databases and file systems.
• Verifying passwords and digital signatures.
• Distributing requests across multiple servers.
• Generating secure hashes for data integrity and authentication.
• Hash Tutorial
• Practice Problems on Hashing

A tree is a non-linear hierarchical data structure consisting of nodes connected by edges, with a top node called the root and nodes having child nodes. It is used in computer science for organizing data efficiently.

• In-Order : Visit left subtree, current node, then right subtree.
• Pre-Order : Visit current node, left subtree, then right subtree.
• Post-Order : Visit left subtree, right subtree, then current node.
• Classifications of Trees refer to grouping trees based on certain characteristics or criteria. This can involve categorizing trees based on their balance factor, degree of nodes, ordering properties, etc. Below are some important classification of Tree.

• File systems
• XML documents
• Artificial intelligence
• Tree Tutorial
• Top 50 Tree Coding Problems
• Practice problems on Tree

A Graph is a non-linear data structure consisting of a finite set of vertices(or nodes) and a set of edges that connect a pair of nodes.

• Breadth-First Search (BFS) : Visits nodes level by level.
• Depth-First Search (DFS) : Visits nodes recursively, exploring one branch at a time.
• Social networks
• Maps and navigation
• Data mining
• Graph Representation
• Types of graphs
• Graph Tutorial
• Practice problems on Graph

Once you have cleared the concepts of Data Structures , now its time to start your journey through the Algorithms . Based on the type of nature and usage, the Algorithms are grouped together into several categories, as shown below:

Searching algorithms are used to locate specific data within a larger set of data. It helps find the presence of a target value within the data. There are various types of searching algorithms, each with its own approach and efficiency.

• Linear Search : Iterative search from one end to the other.
• Binary Search : Divide-and-conquer search on a sorted array, halving the search space at each iteration.
• Ternary Search : Divide-and-conquer search on a sorted array, dividing the search space into three parts at each iteration.
• Jump Search
• Interpolation Search  
• Exponential Search  
• Practice problems on Searching algorithm

Sorting algorithms are used to arranging the elements of a list in a specific order, such as numerical or alphabetical. It organizes the items in a systematic way, making it easier to search for and access specific elements.

There are a lot of different types of sorting algorithms. Some widely used algorithms are:

Related Topics:

• Sorting Algorithm Tutorial
• Top Sorting Interview Questions and Problems
• Practice problems on Sorting algorithm

Divide and conquer algorithms follow a recursive strategy to solve problems by dividing them into smaller subproblems, solving those subproblems, and combining the solutions to obtain the final solution.

• Divide : Partition the problem into smaller, independent subproblems.
• Conquer : Recursively solve each subproblem.
• Combine : Merge the solutions of the subproblems to obtain the final solution.
• Merge Sort: Divides the array into two halves, sorts each half recursively, and merges the sorted halves.
• Quick Sort: Selects a pivot element, partitions the array into two subarrays based on the pivot, and recursively sorts each subarray.
• Binary Search: Divides the search space in half repeatedly until the target element is found or the search space is exhausted.

Related Articles:

• Divide and Conquer Tutorial
• Practice problems on Divide And Conquer algorithm

As the name suggests, this algorithm builds up the solution one piece at a time and chooses the next piece which gives the most obvious and immediate benefit i.e., which is the most optimal choice at that moment. So the problems where choosing locally optimal also leads to the global solutions are best fit for Greedy.

Some Important Problem of Greedy Algorithms are:

• Greedy Algorithm Tutorial
• Practice problems on Greedy algorithm

Recursion is a programming technique where a function calls itself within its own definition. It is usually used to solve problems that can be broken down into smaller instances of the same problem. For Example: Towers of Hanoi (TOH) , Factorial Calculation and Fibonacci Sequence etc.

• Base Case : Define a condition that stops the recursive calls and provides a solution.
• Recursive Case : Define the steps to break down the problem into smaller instances and make recursive calls.
• Return : Return the solution from the recursive calls and combine them to solve the original problem.

The point which makes Recursion one of the most used algorithms is that it forms the base for many other algorithms such as Tree traversals , Graph traversals , Divide and Conquers Algorithms and Backtracking algorithms .

• Recursion Tutorial
• Recursive Functions
• Tail Recursion
• Top 50 Problems on Recursion Algorithm for Interview
• Practice problems on Recursion algorithm

As mentioned earlier, the Backtracking algorithm is derived from the Recursion algorithm, with the option to revert if a recursive solution fails, i.e. in case a solution fails, the program traces back to the moment where it failed and builds on another solution. So basically it tries out all the possible solutions and finds the correct one.

Some important and most common problems of backtracking algorithms, that you must solve before moving ahead, are:

Related Article:

• Backtracking Tutorial
• Practice problems on Backtracking algorithm

Dynamic Programming is a method used in mathematics and computer science to solve complex problems by breaking them down into simpler subproblems. By solving each subproblem only once and storing the results, it avoids redundant computations, leading to more efficient solutions for a wide range of problems.

Key Concepts:

• Optimal Substructure : The optimal solution to a problem can be constructed from the optimal solutions to its subproblems.
• Overlapping Subproblems : Subproblems are often repeated in the larger problem, leading to redundant computations.
• Memoization / Tabulation : Storing the solutions to subproblems to avoid recomputation.

Some important and most common problems of dynamic programming algorithms, that you must solve before moving ahead, are:

• Tabulation vs Memoization
• How to solve a Dynamic Programming Problem?
• Dynamic Programming Tutorial
• Top 50 Dynamic Programming Coding Problems for Interviews
• Practice problems on Dynamic Programming algorithm

8. Graph Algorithms :

Graph algorithms in data structures and algorithms (DSA) are a set of techniques and methods used to solve problems related to graphs, which are a collection of nodes and edges. These algorithms are designed to perform various operations on graphs, such as searching, traversing, finding the shortest path , and determining connectivity . They are essential for solving a wide range of real-world problems, including network routing, social network analysis, and resource allocation.

• Top 50 Graph Coding Problems for Interviews
• Practice Problem on Graph Algorithms

9 . Pattern Searching

Pattern searching is a fundamental technique in DSA used to find occurrences of a specific pattern within a given text.

Below are some some standard pattern searching algorithms:

• Pattern Searching Tutorial
• Practice Problem on Pattern Searching

10 . Mathematical Algorithms

Mathematical algorithms are a class of algorithms that solve problems related to mathematical concepts. They are widely used in various fields, including Computer graphics, Numerical analysis, Optimization and Cryptography.

• Practice Problem on Mathmatical Algorithm

Geometric algorithms are a class of algorithms that solve problems related to geometry. Geometric algorithms are essential for solving a wide range of problems in computer science, such as:

• Practice Problem on Geometric Algorithms

Bitwise algorithms are algorithms that operate on individual bits of numbers. These algorithms manipulate the binary representation of numbers to perform tasks such as bit manipulation, bitwise logical operations (AND, OR, XOR), shifting bits , and setting or clearing specific bits within a number. Bitwise algorithms are commonly used in low-level programming, cryptography, and optimization tasks where efficient manipulation of individual bits is required.

• Bitwise Algorithms Tutorial
• Practice Problem on Bitwise Algorithms

Randomized algorithms are algorithms that use randomness to solve problems. They make use of random input to achieve their goals, often leading to simpler or more efficient solutions.

Types of Randomized Algorithms:

• Las Vegas : Always produces a correct result, but the running time is random.
• Monte Carlo : May produce an incorrect result with a small probability, but the running time is usually faster.

Important Algorithms that uses Randomization Algorithms:

The Branch and Bound Algorithm is a method used in combinatorial optimization problems to systematically search for the best solution. It works by dividing the problem into smaller subproblems, or branches, and then eliminating certain branches based on bounds on the optimal solution. This process continues until the best solution is found or all branches have been explored.

Standard Problems on Branch and Bound Algorithm:

• Branch and Bound Algorithm Tutorial

In Data Structures and Algorithms (DSA), the main goal is to solve problems effectively and efficiently. To determine the efficiency of a program, we look at two types of complexities:

• Time Complexity : This tells us how much time our code takes to run.
• Space Complexity : This tells us how much memory our code uses.

To compare efficiencies of algorithms, we use asymptotic notation, a mathematical tool that estimates time based on input size without running the code. It focuses on the number of basic operations in the program.

The most commonly used notation for code analysis is Big O Notation, providing an upper limit on the running time or memory usage concerning the input size.

• Understanding Time Complexity with Simple Examples
• Time complexities of different data structures
• How to Analyse Loops for Complexity Analysis of Algorithms
• Practice Questions on Time Complexity Analysis

Curated lists of problems from below articles:

• DSA Roadmap by Sandeep Jain
• SDE SHEET – A Complete Guide for SDE Preparation
• GeeksforGeeks Master Sheet – List of all Cheat Sheets

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