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* Reference Manual for working people
Data Structures and Algorithms For GATE: Solutions to all previous GATE quest...CareerMonk Publications
printf("The value of X is: %d\n", *ptr);
This dereferences the pointer and gives us the value stored at the address it points to, which is the value of .
So in summary:
- Pointers hold the address of other variables.
- We assign addresses to pointers using the address-of operator &.
- We access the value at the address a pointer points to using the indirection operator *.
This document contains information about pushdown automata (PDA) and examples of using PDA to represent certain formal languages. It provides examples of drawing PDA for languages of strings over an alphabet where the number of as equals the number of bs and for strings that represent legal arithmetic expressions over an alphabet of integer, operators and parentheses. Links to external resources on the topic are also included.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses NP-complete problems and their properties. Some key points:
- NP-complete problems have an exponential upper bound on runtime but only a polynomial lower bound, making them appear intractable. However, their intractability cannot be proven.
- NP-complete problems are reducible to each other in polynomial time. Solving one would solve all NP-complete problems.
- NP refers to problems that can be verified in polynomial time. P refers to problems that can be solved in polynomial time.
- A problem is NP-complete if it is in NP and all other NP problems can be reduced to it in polynomial time. Proving a problem is NP-complete involves showing
This document provides an introduction to NP-completeness, including: definitions of key concepts like decision problems, classes P and NP, and polynomial time reductions; examples of NP-complete problems like satisfiability and the traveling salesman problem; and approaches to dealing with NP-complete problems like heuristic algorithms, approximation algorithms, and potential help from quantum computing in the future. The document establishes NP-completeness as a central concept in computational complexity theory.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
This document summarizes a presentation on graph coloring. Graph coloring involves assigning colors to the vertices of a graph such that no two adjacent vertices have the same color. It has applications in problems like channel assignment. The document defines key terms like k-coloring, chromatic number, and k-chromatic graphs. It also discusses the NP-complete nature of the graph coloring problem and summarizes basic greedy and Welsh-Powell algorithms for graph coloring.
Not just consider one operation, but a sequence of operations on a given data structure.
Average cost over a sequence of operations.
Probabilistic analysis:
Average case running time: average over all possible inputs for one algorithm (operation).
If using probability, called expected running time.
Amortized analysis:
No involvement of probability
Average performance on a sequence of operations, even some operation is expensive.
Guarantee average performance of each operation among the sequence in worst case.
Data Structures and Algorithms For GATE: Solutions to all previous GATE quest...CareerMonk Publications
printf("The value of X is: %d\n", *ptr);
This dereferences the pointer and gives us the value stored at the address it points to, which is the value of .
So in summary:
- Pointers hold the address of other variables.
- We assign addresses to pointers using the address-of operator &.
- We access the value at the address a pointer points to using the indirection operator *.
This document contains information about pushdown automata (PDA) and examples of using PDA to represent certain formal languages. It provides examples of drawing PDA for languages of strings over an alphabet where the number of as equals the number of bs and for strings that represent legal arithmetic expressions over an alphabet of integer, operators and parentheses. Links to external resources on the topic are also included.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses NP-complete problems and their properties. Some key points:
- NP-complete problems have an exponential upper bound on runtime but only a polynomial lower bound, making them appear intractable. However, their intractability cannot be proven.
- NP-complete problems are reducible to each other in polynomial time. Solving one would solve all NP-complete problems.
- NP refers to problems that can be verified in polynomial time. P refers to problems that can be solved in polynomial time.
- A problem is NP-complete if it is in NP and all other NP problems can be reduced to it in polynomial time. Proving a problem is NP-complete involves showing
This document provides an introduction to NP-completeness, including: definitions of key concepts like decision problems, classes P and NP, and polynomial time reductions; examples of NP-complete problems like satisfiability and the traveling salesman problem; and approaches to dealing with NP-complete problems like heuristic algorithms, approximation algorithms, and potential help from quantum computing in the future. The document establishes NP-completeness as a central concept in computational complexity theory.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
This document summarizes a presentation on graph coloring. Graph coloring involves assigning colors to the vertices of a graph such that no two adjacent vertices have the same color. It has applications in problems like channel assignment. The document defines key terms like k-coloring, chromatic number, and k-chromatic graphs. It also discusses the NP-complete nature of the graph coloring problem and summarizes basic greedy and Welsh-Powell algorithms for graph coloring.
Not just consider one operation, but a sequence of operations on a given data structure.
Average cost over a sequence of operations.
Probabilistic analysis:
Average case running time: average over all possible inputs for one algorithm (operation).
If using probability, called expected running time.
Amortized analysis:
No involvement of probability
Average performance on a sequence of operations, even some operation is expensive.
Guarantee average performance of each operation among the sequence in worst case.
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a connected weighted undirected graph. It finds a subset of edges that forms a tree including every vertex where the total weight is minimized. A minimum spanning tree is a subgraph that is a tree covering all vertices using the minimum total cost of edges. Prim's algorithm works by growing this tree one edge at a time, each time adding the minimum cost edge that connects the tree to new vertices until all vertices are included.
The document discusses sorting algorithms. It begins by defining sorting as arranging data in logical order based on a key. It then discusses internal and external sorting methods. For internal sorting, all data fits in memory, while external sorting handles data too large for memory. The document covers stability, efficiency, and time complexity of various sorting algorithms like bubble sort, selection sort, insertion sort, and merge sort. Merge sort uses a divide-and-conquer approach to sort arrays with a time complexity of O(n log n).
The document discusses three sorting algorithms: bubble sort, selection sort, and insertion sort. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order. Selection sort finds the minimum element and swaps it into the sorted portion of the array. Insertion sort inserts elements into the sorted portion of the array, swapping as needed to put the element in the correct position. Both selection sort and insertion sort have a time complexity of O(n^2) in the worst case.
The document discusses natural language processing (NLP) and provides examples of practical NLP problems and solutions. It describes a scenario where a company called Tweet-a-Toddy receives thousands of tweets per day that need categorizing. Potential solutions discussed include text classification, entity identification, information extraction, sentiment analysis, and using regular expressions.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
(1) Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller subproblems and storing the results of already solved subproblems. (2) It is applicable to problems where subproblems overlap and solving them recursively would result in redundant computations. (3) The key steps of a dynamic programming algorithm are to characterize the optimal structure, define the problem recursively in terms of optimal substructures, and compute the optimal solution bottom-up by solving subproblems only once.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
In most of the algorithms analyzed until now, we have been looking and studying problems solvable in polynomial time. The polynomial time algorithm class P are algorithms that on inputs of size n have a worst case running time of O(n^k) for some constant k. Thus, informally, we can say that the Non-Polynomial (NP) time algorithms are the ones that cannot be solved in O(n^k) for any constant k
.
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
This document discusses NP-hard and NP-complete problems. It begins by defining the classes P, NP, NP-hard, and NP-complete. It then provides examples of NP-hard problems like the traveling salesperson problem, satisfiability problem, and chromatic number problem. It explains that to show a problem is NP-hard, one shows it is at least as hard as another known NP-hard problem. The document concludes by discussing how restricting NP-hard problems can result in problems that are solvable in polynomial time.
Application of Stack For Expression Evaluation by Prakash Zodge DSY 41.pptxPrakash Zodge
In short...The stack organization is very effective in evaluating arithmetic expressions. Expressions are usually represented in what is known as Infix notation, in which each operator is written between two operands (i.e., A + B)....
The document discusses time and space complexity analysis of algorithms. Time complexity measures the number of steps to solve a problem based on input size, with common orders being O(log n), O(n), O(n log n), O(n^2). Space complexity measures memory usage, which can be reused unlike time. Big O notation describes asymptotic growth rates to compare algorithm efficiencies, with constant O(1) being best and exponential O(c^n) being worst.
This document discusses Python data types and handling data in Python. It covers the different types of numbers in Python including integers, floating point numbers, and complex numbers. It also discusses Boolean, string, and tuple data types. Methods for accessing and slicing strings are provided along with examples. Mutable and immutable objects are defined, with lists and tuples given as examples. Operator precedence in Python is also discussed.
This presentation discusses several sorting algorithms: insertion sort, merge sort, quick sort, and counting sort. Insertion sort iterates through a list and inserts each element into the sorted position. Merge sort and quick sort are divide-and-conquer algorithms that split the list into halves, sort the halves, and merge the results. Counting sort works by counting the number of objects that have each distinct key value. The algorithms are analyzed and their time and space complexities are compared.
This document is the preface to a book titled "Data Structures and Algorithms Made Easy" by Narasimha Karumanchi. It introduces the objective of the book, which is to provide solutions to algorithmic problems with different complexities rather than focus on theorems and proofs. The book contains approximately 700 problems covering topics relevant for competitive exams and interviews. For each problem, multiple solutions with varying complexities are provided to illustrate different approaches. The preface recommends reading the entire book to gain a full understanding of the topics covered. It also notes that while efforts were made to correct errors, readers should check the author's website for any updates or corrections.
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a connected weighted undirected graph. It finds a subset of edges that forms a tree including every vertex where the total weight is minimized. A minimum spanning tree is a subgraph that is a tree covering all vertices using the minimum total cost of edges. Prim's algorithm works by growing this tree one edge at a time, each time adding the minimum cost edge that connects the tree to new vertices until all vertices are included.
The document discusses sorting algorithms. It begins by defining sorting as arranging data in logical order based on a key. It then discusses internal and external sorting methods. For internal sorting, all data fits in memory, while external sorting handles data too large for memory. The document covers stability, efficiency, and time complexity of various sorting algorithms like bubble sort, selection sort, insertion sort, and merge sort. Merge sort uses a divide-and-conquer approach to sort arrays with a time complexity of O(n log n).
The document discusses three sorting algorithms: bubble sort, selection sort, and insertion sort. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order. Selection sort finds the minimum element and swaps it into the sorted portion of the array. Insertion sort inserts elements into the sorted portion of the array, swapping as needed to put the element in the correct position. Both selection sort and insertion sort have a time complexity of O(n^2) in the worst case.
The document discusses natural language processing (NLP) and provides examples of practical NLP problems and solutions. It describes a scenario where a company called Tweet-a-Toddy receives thousands of tweets per day that need categorizing. Potential solutions discussed include text classification, entity identification, information extraction, sentiment analysis, and using regular expressions.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
(1) Dynamic programming is an algorithm design technique that solves problems by breaking them down into smaller subproblems and storing the results of already solved subproblems. (2) It is applicable to problems where subproblems overlap and solving them recursively would result in redundant computations. (3) The key steps of a dynamic programming algorithm are to characterize the optimal structure, define the problem recursively in terms of optimal substructures, and compute the optimal solution bottom-up by solving subproblems only once.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
In most of the algorithms analyzed until now, we have been looking and studying problems solvable in polynomial time. The polynomial time algorithm class P are algorithms that on inputs of size n have a worst case running time of O(n^k) for some constant k. Thus, informally, we can say that the Non-Polynomial (NP) time algorithms are the ones that cannot be solved in O(n^k) for any constant k
.
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
This document discusses NP-hard and NP-complete problems. It begins by defining the classes P, NP, NP-hard, and NP-complete. It then provides examples of NP-hard problems like the traveling salesperson problem, satisfiability problem, and chromatic number problem. It explains that to show a problem is NP-hard, one shows it is at least as hard as another known NP-hard problem. The document concludes by discussing how restricting NP-hard problems can result in problems that are solvable in polynomial time.
Application of Stack For Expression Evaluation by Prakash Zodge DSY 41.pptxPrakash Zodge
In short...The stack organization is very effective in evaluating arithmetic expressions. Expressions are usually represented in what is known as Infix notation, in which each operator is written between two operands (i.e., A + B)....
The document discusses time and space complexity analysis of algorithms. Time complexity measures the number of steps to solve a problem based on input size, with common orders being O(log n), O(n), O(n log n), O(n^2). Space complexity measures memory usage, which can be reused unlike time. Big O notation describes asymptotic growth rates to compare algorithm efficiencies, with constant O(1) being best and exponential O(c^n) being worst.
This document discusses Python data types and handling data in Python. It covers the different types of numbers in Python including integers, floating point numbers, and complex numbers. It also discusses Boolean, string, and tuple data types. Methods for accessing and slicing strings are provided along with examples. Mutable and immutable objects are defined, with lists and tuples given as examples. Operator precedence in Python is also discussed.
This presentation discusses several sorting algorithms: insertion sort, merge sort, quick sort, and counting sort. Insertion sort iterates through a list and inserts each element into the sorted position. Merge sort and quick sort are divide-and-conquer algorithms that split the list into halves, sort the halves, and merge the results. Counting sort works by counting the number of objects that have each distinct key value. The algorithms are analyzed and their time and space complexities are compared.
This document is the preface to a book titled "Data Structures and Algorithms Made Easy" by Narasimha Karumanchi. It introduces the objective of the book, which is to provide solutions to algorithmic problems with different complexities rather than focus on theorems and proofs. The book contains approximately 700 problems covering topics relevant for competitive exams and interviews. For each problem, multiple solutions with varying complexities are provided to illustrate different approaches. The preface recommends reading the entire book to gain a full understanding of the topics covered. It also notes that while efforts were made to correct errors, readers should check the author's website for any updates or corrections.
1. Perform Linear Search and Binary Search on an array.
Descriptions of the programs:
Read and array of type integer.
Input element from user for searching.
Search the element by passing the array to a function and then returning the position of the element from the function else return -1 if the element is not found.
Display the positions where the element has been found.
2. Implement sparse matrix using array.
Description of program:
Read a 2D array from the user.
Store it in the sparse matrix form, use array of structures.
Print the final array.
3. Create a linked list with nodes having information about a student and perform.
Description of the program:
Insert a new node at specified position.
Delete of a node with the roll number of student specified.
Reversal of that linked list.
4. Create doubly linked list with nodes having information about an employee and perform Insertion at front of doubly linked list and perform deletion at end of that doubly linked list.
5. Create circular linked list having information about a college and perform Insertion at front perform Deletion at end.
6. Create a stack and perform Pop, Push, Traverse operations on the stack using Linear Linked list.
7. Create a Linear Queue using Linked List and implement different operations such as Insert, Delete, and Display the queue elements.
This document contains C++ code that implements bubble sort, insertion sort, and selection sort algorithms to sort arrays of random numbers. It generates random arrays, copies the arrays, then applies each sorting algorithm to a separate array and measures the time taken. It displays the original and sorted arrays. The functions for the sorting algorithms and auxiliary functions like generating and copying arrays are also defined.
This document contains code snippets for various operations on linked lists and polynomials in C programming language. It includes 9 questions covering topics like:
1. Counting characters, words, digits in a string
2. Squeezing a string by removing spaces
3. Swapping values using pointers
4. Comparing two strings
5. Concatenating two strings
6. Multiplying two matrices
7. Reversing a string
8. Performing insertion, deletion and traversal on singly linked lists
9. Implementing polynomial addition and multiplication by representing polynomials as linked lists
For each question, the C code to implement the operation is provided along with sample input/output.
The document contains a data structures lab manual with experiments on various data structure topics like arrays, stacks, queues, linked lists, and binary search trees. It includes C programs and explanations for inserting and deleting elements from arrays, stacks and queues. It also includes programs for matrix operations, sparse matrix representation, linear and binary searches. The experiments cover basic operations on common data structures.
Lab manual data structure (cs305 rgpv) (usefulsearch.org) (useful search)Make Mannan
This document provides a lab manual for experiments on data structures. It includes 20 experiments covering topics like arrays, matrices, recursion, strings, stacks, queues, linked lists, trees, graphs and sorting algorithms. Each experiment contains the aim, introduction, source code, sample output and questions. The experiments provide hands-on practice with commonly used data structures and algorithms.
Programs are complete in best of my knowledge with zero compilation error in IDE Bloodshed Dev-C++. These can be easily portable to any versions of Visual Studio or Qt. If you need any guidance please let me know via comments and Always Enjoy Programming.
VTU 1ST SEM PROGRAMMING IN C & DATA STRUCTURES SOLVED PAPERS OF JUNE-2015 & ...vtunotesbysree
The document contains solved question papers from June 2015 and December 2015 for Programming in C & Data Structures examinations. It includes questions ranging from basic C programming concepts like data types, operators, decision making and looping statements to more advanced topics such as arrays, strings, structures, files and pointers. For each question, the relevant concept is explained and examples are provided. Solutions for some programming problems involving simple calculations, palindrome checks and file handling are also presented.
The document provides an introduction to the C programming language. It discusses C's history, origins in the development of UNIX, data types, variables, constants, operators, input/output functions, conditional statements, and loops. It also provides 10 examples of C programs covering topics like calculating sums, finding prime and palindrome numbers, temperature conversion, and linear/binary search.
This document is a lab manual for an introduction to computer programming course using C++. It contains 11 chapters that provide examples and exercises to help students learn programming concepts like variables, operators, control flow, functions, arrays, pointers, structures, and file input/output. Each chapter contains multiple programming questions and examples with the full code provided. The objective is to give students hands-on practice with programming to become familiar with design, coding, and problem solving in C++.
C is a procedural programming language. It was developed in the early 1970s and is still widely used. The document provides an overview of key aspects of C including data types, variables, constants, operators, control statements like if/else, and functions. It also discusses C programming concepts like low-level vs high-level languages, header files, comments, escape sequences, and more. The document serves as a useful introduction and reference for someone learning the basics of the C programming language.
This document provides code for a program that converts infix expressions to postfix expressions. It includes functions for pushing and popping elements in a stack, determining operator precedence, and converting an infix string to postfix by processing each character. The main function gets an infix expression from the user, calls the conversion function, and prints the infix and postfix expressions. The program supports operators like +, -, *, /, %, ^ and parentheses.
Data structures allow for the organization and storage of data. There are linear and non-linear data structures. Linear structures include arrays, stacks, queues, and linked lists. Arrays store elements in contiguous memory locations. Stacks and queues follow first-in last-out and first-in first-out rules respectively. Linked lists connect nodes using pointers. Non-linear structures include trees and graphs which emulate hierarchical and network-like connections. Common operations on data structures include traversing, searching, insertion, and deletion.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
The document discusses relational database management systems and their advantages over traditional file processing systems. It describes some key disadvantages of file processing systems like data redundancy, difficulty in accessing data, integrity problems, and security issues. It then explains some core components and concepts of relational database management systems like data independence, data models, entity-relationship diagrams, relational algebra, relational calculus, SQL, and integrity constraints. The document provides an overview of relational database management systems and their design and querying capabilities.
This document provides an overview of the C++ Data Structures lab manual. It covers topics like C++ review, implementation of various data structures like stack, queue, linked list, binary tree, graph. It also discusses sorting and searching techniques, file input/output, functions, classes, templates and exercises for students to practice implementing different data structures and algorithms. The instructor's contact details are provided at the beginning.
This document discusses data structures and their applications. It defines key terms like data, data item, entity, attribute, field, record, and file. It explains that a data structure is a logical organization of data that specifies the data elements and operations that can be performed on them. Common operations include traversing, searching, inserting, and deleting. The choice of data structure depends on how frequently certain operations will be performed. Real-life data manipulation requires storage, retrieval, and transformation of user data.
This document provides information about Dream Valley College for Girls Centre for Educational Excellence. It includes an index and presentation on data structures covering topics like arrays, linked lists, queues, trees, and graphs. The presentation was presented by Harish Sir and includes definitions, examples, and diagrams to explain each data structure concept.
Data Structures and Algorithms Made Easy in Java ( PDFDrive ).pdfAbdurraufSharifaiGar
This document is the preface of a book titled "Data Structures and Algorithms Made Easy in Java" by Narasimha Karumanchi. It provides acknowledgements to various individuals who helped with the creation and editing of the book. It also contains a brief message from the author encouraging readers to fully read and understand the content of the book in order to help with interview preparation, competitive exams, and more. The book aims to teach data structures and algorithms through problems and solutions rather than just theory.
Elements of Computer Networking: An Integrated Approach (Concepts, Problems a...CareerMonk Publications
Salient Features of Book:
All the concepts are discussed in a lucid, easy to understand manner.
A reader without any basic knowledge in computers can comfortably follow this book.
Helps to build logic in the students which becomes stepping stone for understanding computer networking protocols.
Interview questions collected from the actual interviews of various Software companies (and past compititive examinations like GATE) will help the students to be successful in their campus interviews.
Hundreds of solved problems help the students of various universities do well in their examinations like B.C.A, B.Sc, M.Sc, M.C.A, B.E, B.Tech, M.Tech, etc.
Works like a handy reference to the Software professionals.
Table of Contents (Chapters):
1) Organization of Chapters
2) Introduction
3) Networking Devices
4) OSI and TCP/IP Models
5) LAN Technologies
6) ARP and RARP
7) IP Addressing
8) Network Routing
9) TCP and UDP
10) TCP Error Control
11 )TCP Flow Control
12) TCP Congestion Control
13) Session layer
14) Presentation layer
15) Network Security
16) Application Layer Protocols
17) Miscellaneous Concepts
"Peeling Design Patterns: For Beginners and Interviews" by Narasimha Karumanchi and Prof. Sreenivasa Rao Meda is a book that presents design patterns in simple and straightforward manner with a clear-cut explanation. This book will provide an introduction to the basics and covers many real-time design interview questions. It comes handy as an interview and exam guide for computer scientists.
Salient Features of Book:
Readers without any background in software design will be able to understand it easily and completely.
Presents the concepts of design patterns in simple and straightforward manner with a clear-cut explanation.
After reading the book, readers will be in a position to come up with better designs than before and participate in design discussions which happen in their daily office work.
The book provides enough real-time examples so that readers get better understanding of the design patterns and also useful for the interviews. We mean, the book should cover design interview questions.
Table of Contents:
Introduction
UML Basics
Design Patterns Introduction
Creational Patterns
Structural Patterns
Behavioral Patterns
Glossary and Tips
Design Interview Questions
Miscellaneous Concepts
This book provides an introduction to design patterns, which are common solutions to recurring problems in software design. The book aims to help readers learn design patterns to improve their skills and prepare for interviews. It covers fundamental design pattern concepts, categorizes patterns, and explains each pattern with examples. In addition, it includes tips, UML basics, and common design interview questions. The book recommends multiple readings to fully understand patterns and how they can be applied.
Table of contents [data structure and algorithmic thinking with python]CareerMonk Publications
This document is the preface of a book on data structures and algorithms by Narasimha Karumanchi. It introduces the book's objectives of helping readers learn fundamental data structures and algorithms through practice problems of varying complexities. The preface emphasizes understanding problem-solving over theory and encourages readers to think of multiple solutions to problems. It also acknowledges those who helped with the book's creation and provides an overview of the book's organization into chapters covering topics like recursion, linked lists, trees and sorting algorithms.
Sample chapters [data structure and algorithmic thinking with python]CareerMonk Publications
This document is the preface of a book on data structures and algorithms by Narasimha Karumanchi. It introduces the book's purpose of helping beginners learn fundamental data structures and algorithms concepts through practice problems of varying complexities. The preface emphasizes understanding problem-solving over theory and encourages readers to think of multiple solutions to problems. It also acknowledges those who helped with the book's creation and provides an overview of the book's organization into chapters covering topics like recursion, linked lists, trees and sorting algorithms.
The document introduces TheManageMentor (TMM), Asia's first knowledge network for management professionals. TMM provides daily knowledge nuggets on best practices and ideas sourced from journals and websites. It also offers features like a knowledge helpdesk, cross-functional learning opportunities, and assessments to help professionals improve. TMM has over 15,000 articles and 90,000 hours of research to support its large network of management professionals across Asia.
This document is the preface to a book titled "Data Structures and Algorithms Made Easy" by Narasimha Karumanchi. It introduces the purpose and structure of the book. The book aims to help readers prepare for interviews and exams by focusing on solving problems of varying complexities for each data structures topic. It contains over 700 algorithm problems and their solutions. The preface recommends reading the entire book to fully understand the topics, and provides information on how to contact the author with any corrections or suggestions.
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This document provides an introduction to data structures and algorithms. It discusses key concepts like variables, data types, data structures, abstract data types, algorithms, and analysis of algorithms. The goal of algorithm analysis is to compare algorithms in terms of their running time and space usage. Commonly used rates of growth for analyzing running time include constant, logarithmic, linear, quadratic, and exponential time. Algorithm analysis helps determine which solutions are most efficient.
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This document provides an overview of programming basics including:
- Variables are used to store and represent data in programs. Primitive data types define the kind of values variables can hold.
- Data structures are specialized formats for organizing and storing data to allow efficient manipulation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs.
- Abstract data types combine data structures with their associated operations. Common ADTs include linked lists, stacks, queues, trees and graphs.
- Memory is organized as an array of bytes addressed by integers. Variables are allocated contiguous memory and identified by their address.
- Pointers are variables that store the address of other variables. They allow accessing and modifying the data at those
This document provides information about books on interview questions for .NET and SQL Server positions. It includes sample questions from the books. The author makes the books available at low costs and also provides information on how to purchase them from different locations in India or online. Career counseling services are also offered for those seeking guidance on their technical career paths.
This document provides information about purchasing books on .NET interview questions and SQL Server interview questions written by Shivprasad Koirala. It discusses where the books can be purchased in India and other countries. It also provides sample questions from the books and information about how to contact the author. Career counseling services are offered for software professionals and details are provided about a small career path institute run by the author in Mumbai, India.
This document provides information about purchasing books on .NET interview questions and SQL Server interview questions written by Shivprasad Koirala. It discusses where the books can be purchased in India and other countries. It also provides sample questions from the books and information about how to contact the author. Career counseling services are offered for software professionals and details are provided about a small career path institute run by the author in Mumbai, India.
This document provides information about books on interview questions for .NET and SQL Server positions. It discusses that the PDF contains sample questions from the books, which sell for low costs. It encourages purchasing the full books for more in-depth information and includes contact details for buying options in India, Pakistan, the UK, and online. It also mentions other books and career counseling services available from the author.
Sym model of communication - A communication model created by ArvindArvind Bhardwaj [AB]
The document introduces the SYM Model of Communication. SYM stands for Someone, You, Me and represents reversing the typical order of priority in communication from the speaker's perspective to considering others first. Specifically, the model advises giving first importance to "someone" not involved in the communication, second to the intended audience "you", and third to the speaker "me". The model can be applied to both written and verbal communication contexts. Examples are provided to illustrate how applying the SYM approach can lead to more effective and unbiased communication that creates a win-win environment by understanding other perspectives.
Similar to Data Structures and Algorithms made Easy (Cover Page) (20)
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All the concepts are discussed in a lucid, easy to understand manner
A reader without any basic knowledge in computers can comfortably follow this book
Coders/Programmers are in demand, but to land the job, you must demonstrate knowledge of those things expected by today's employers. This guide sets you up for success. Not only does it provide the most commonly asked interview questions and answers, but it also offers insight into the interview process in today's marketplace. This book is a comprehensive guide for experienced and first-time programmers alike.
The book is specifically designed for freshers, who despite being brilliant at the technical aspects of the interview, tend to fail when it comes to soft skills and HR interviews. The book provides readers with a relevant blueprint when it comes to planning for pre-interview preparation. It provides candidates with guidelines on the preparation of their resumes and the format that should be followed.
Table of Contents
Organization of Chapters 17
Getting Ready 22
Group Discussions 37
Operating System Concepts 54
C/C++/Java Interview Questions 81
Scripting Languages 157
Bitwise Hacking 194
Concepts of Computer Networking 203
Database Management Systems 256
Brain Teasers 271
Algorithms Introduction 274
Recursion and Backtracking 285
Linked Lists 290
Stacks 322
Queues 336
Trees 345
Priority Queues and Heaps 397
Graph Algorithms 407
Sorting 417
Searching 441
Hashing 466
String Algorithms 473
Algorithms Design Techniques 479
Greedy Algorithms 482
Divide and Conquer Algorithms 486
Dynamic Programming 489
Basics of Design Patterns 496
Non-Technical Help 505
Quantitative Aptitude Concepts 511
Basics of Cloud Computing 524
Miscellaneous Concepts 539
Career Options 559
This document provides information about a book on design patterns. The book is written in Java and covers common interview questions related to design patterns without requiring any prior software design experience. It serves as a reference guide for professionals. The author, Narasimha Karumanchi, has industry experience at Amazon and previously worked at IBM, Mentor Graphics, and Microsoft. He has a B.Tech and M.Tech in Computer Science. The book is published by CareerMonk Publications.
This document provides an overview of an approach to learning data structures and algorithms that focuses on enumerating possible solutions to problems with different complexities, rather than just presenting theorems and proofs. It is intended to help with interview, course, and competition exam preparation by improving thinking skills. All code examples are written in C but are not language-specific. It recommends completely reading each chapter, which presents required theory and related problems, and then practicing applying techniques to new questions in order to analyze multiple solutions.
Chapter 1 Introduction 9
Variables 9
Data types 9
System defined data types (Primitive data types) 10
User defined data types 10
Data Structure 10
Abstract Data Types (ADT’s) 11
Memory and Variables 11
Size of a Variable 12
Address of a Variable 12
Pointers 13
Declaration of Pointers 13
Pointers Usage 13
Pointer Manipulation 14
Arrays and Pointers 15
Dynamic Memory Allocation 15
Function Pointers 16
Parameter Passing Techniques 16
Actual and Formal Parameters 16
Semantics of Parameter Passing 17
Language Support for Parameter Passing Techniques 17
Pass by Value 17
Pass by Result 18
Pass by Value-Result 19
Pass by Reference (aliasing) 20
Pass by Name 21
Binding 22
Binding Times 22
Static Binding (Early binding) 22
Dynamic Binding (Late binding) 23
Scope 23
Static Scope 23
Dynamic Scope 24
Storage Classes 25
Auto Storage Class 25
Extern storage class 26
Register Storage Class 31
Static Storage Class 31
Storage Organization 32
Static Segment 32
Stack Segment 33
Heap Segment 35
Shallow Copy versus Deep Copy 36
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* Reference Manual for working people
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
HCL Notes and Domino License Cost Reduction in the World of DLAU
Data Structures and Algorithms made Easy (Cover Page)
1. D A TA S T R U C T U R E S
AND
ALGORITHMS
MADE EASY
Success Key for:
• Campus Preparation
• Degree/Masters Course Preparation
• Instructor’s
• GATE Preparation
• Big Job hunters: Microsoft, Google, Amazon, Yahoo,
Facebook, Adobe, IBM Labs and many more
• Reference Manual for Working People
ROOM NUMBER
M-Tech, IIT Bombay
.
CareerMonk Publications