Books From My Sky Drive

There is list of books which are in my sky drive.

1. Abraham Silberschatz, Peter B. Galvin, Greg Gagne-Operating System Concepts-Wiley (2012)

2.Addison-Wesley-Algorithms-Data-Structures-and- Problem-Solving-with-C++

3.Borland C++ in 14 days

4.Borland-C++-Unleashed-Charlie-Calvert's

5.C++ Complete Reference

6.  Data Structures - Ellis Horowitz

7. Data Structures and Algorithms in C++

8. data structures in c++ by adam drozdek 

9. Data Structures Using C

10. Data Structures With C - by schaum series.

11.Discrete Math - Mathematics For Computer Science

12.Discrete Mathematics Demystified

13.Discrete-Mathematics-and-Its-Applications-4Th-Ed-Rosen

14.Graham - Knuth - Patashnik - Concrete Mathematics

15.HTML a Beginners Guide

16.HTML XHTML _ CSS Quick Steps

17.Integration And Automation Of Manufacturing Systems

18.James F. Kurose, Keith W. Ross-Computer Networking_ A Top-Down Approach-Addison-Wesley (2013)

19.  Java Data Structures _ Algorithms

20. C++ units ( Summary of E Balagurusamy)

21.  Mark Allen Weiss-Data Structures and Algorithm Analysis in Java, 3rd Edition-Pearson Education Canada (2011)

22.  R S aggarwal verbal reasoning

23.Reverse engineering in computer applications Softice Cracking

24. Robotics Click Here To Download

Data Structures and Algorithms with Object-Oriented Design Patterns in C# Author: Bruno R. Preiss, B.A.Sc., M.A.Sc. Ph.D., P.Eng.

Data Structures and Algorithms with Object-Oriented Design Patterns in C#

Author: Bruno R. Preiss, B.A.Sc., M.A.Sc. Ph.D., P.Eng.

This book is about the fundamentals of data structures and algorithms--the basic elements from which large and complex software artifacts are built. To develop a solid understanding of a data structure requires three things: First, you must learn how the information is arranged in the memory of the computer. Second, you must become familiar with the algorithms for manipulating the information contained in the data structure. And third, you must understand the performance characteristics of the data structure so that when called upon to select a suitable data structure for a particular application, you are able to make an appropriate decision.

This book also illustrates object-oriented design and it promotes the use of common, object-oriented design patterns. The algorithms and data structures in the book are presented in the C# programming language. Virtually all the data structures are presented in the context of a single class hierarchy. This commitment to a single design allows the programs presented in the later chapters to build upon the programs presented in the earlier chapters.

This book does not teach the basics of programming. It is assumed that you have taken an introductory course in programming and that you have learned how to write a program in C#. That is, you have learned the rules of C# syntax and you have learned how to put together C# statements in order to solve rudimentary programming problems. The following paragraphs describe more fully aspects of programming in C# with which you should be familiar.

1. Variables
2. Value Types and Reference Types
3. Parameter Passing
4. Classes and Objects
5. Inheritance
6. Interfaces and Polymorphism
7. Other Features

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Data Structures and Algorithms: Annotated Reference with Examples By Granville Barnett and Luca Del Tongo

Data Structures and Algorithms: Annotated Reference with Examples

By Granville Barnett and Luca Del Tongo

Every book has a story as to how it came about and this one is no different, although we would be lying if we said its development had not been somewhat impromptu. Put simply this book is the result of a series of emails sent back and forth between the two authors during the development of a library for the .NET framework of the same name (with the omission of the subtitle of course!). The conversation started of something like, Why don't we create a more aesthetically pleasing way to present our pseudocode?" After a few weeks this new presentation style had in fact grown into pseudocode listings with chunks of text describing how the data structure or algorithm in question works and various other things about it. At this point we thought, What the heck, let's make this thing into a book!" And so, in the summer of 2008 we began work on this book side by side with the actual library implementation. When we started writing this book the only things that we were sure about with respect to how the book should be structured were:

1. always make explanations as simple as possible while maintaining a moderately fine degree of precision to keep the more eager minded reader happy; and
2. inject diagrams to demystify problems that are even moderately challenging to visualize (. . . and so we could remember how our own algorithms worked when looking back at them); and finally
3. present concise and self explanatory pseudo code listings that can be ported easily to most mainstream imperative programming languages like C++, C#, and Java.

A key factor of this book and its associated implementations is that all algorithms (unless otherwise stated) were designed by us, using the theory of the algorithm in question as a guideline (for which we are eternally grateful to their original creators). Therefore they may sometimes turn out to be worse than the "normal" implementations|and sometimes not. We are two fellows of the opinion that choice is a great thing. Read our book, read several others on the same subject and use what you see fit from each (if anything) when implementing your own version of the algorithms in question. Through this book we hope that you will see the absolute necessity of understanding which data structure or algorithm to use for a certain scenario. In all projects, especially those that are concerned with performance (here we apply an even greater emphasis on real-time systems) the selection of the wrong data structure or algorithm can be the cause of a great deal of performance pain

Therefore it is absolutely key that you think about the run time complexity and space requirements of your selected approach. In this book we only explain the theoretical implications to consider, but this is for a good reason: compilers are very different in how they work. One C++ compiler may have some amazing optimisation phases specically targeted at recursion, another may not, for example. Of course this is just an example but you would be surprised by how many subtle differences there are between compilers. These differences which may make a fast algorithm slow, and vice versa. We could also factor in the same concerns about languages that target virtual machines, leaving all the actual various implementation issues to you given that you will know your language's compiler much better than us...well in most cases. This has resulted in a more concise book that focuses on what we think are the key issues.

One final note: never take the words of others as gospel; verify all that can be feasibly veri¯ed and make up your own mind. We hope you enjoy reading this book as much as we have enjoyed writing it.

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Data Structures and Algorithms with Object-Oriented Design Patterns in Java By Bruno R. Preiss

Data Structures and Algorithms with Object-Oriented Design Patterns in Java

By Bruno R. Preiss

The primary goal of this book is to promote object-oriented design using Java and to illustrate the use of the emerging object-oriented design patterns. Experienced object-oriented programmers find that certain ways of doing things work best and that these ways occur over and over again. The book shows how these patterns are used to create good software designs. In particular, the following design patterns are used throughout the text: singleton, container, enumeration, adapter and visitor.

Virtually all of the data structures are presented in the context of a single, unified, polymorphic class hierarchy. This framework clearly shows the relationships between data structures and it illustrates how polymorphism and inheritance can be used effectively. In addition, algorithmic abstraction is used extensively when presenting classes of algorithms. By using algorithmic abstraction, it is possible to describe a generic algorithm without having to worry about the details of a particular concrete realization of that algorithm.

A secondary goal of the book is to present mathematical tools just in time. Analysis techniques and proofs are presented as needed and in the proper context. In the past when the topics in this book were taught at the graduate level, an author could rely on students having the needed background in mathematics. However, because the book is targeted for second- and third-year students, it is necessary to fill in the background as needed. To the extent possible without compromising correctness, the presentation fosters intuitive understanding of the concepts rather than mathematical rigor.

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Data Structures and Algorithms with Object-Oriented Design Patterns in C# By Bruno R. Preiss

Data Structures and Algorithms with Object-Oriented Design Patterns in C#

By Bruno R. Preiss

Object-Oriented Design

Traditional approaches to the design of software have been either data oriented or process oriented. Data-oriented methodologies emphasize the representation of information and the relationships between the parts of the whole. The actions which operate on the data are of less significance. On the other hand, process-oriented design methodologies emphasize the actions performed by a software artifact; the data are of lesser importance.

It is now commonly held that object-oriented methodologies are more effective for managing the complexity which arises in the design of large and complex software artifacts than either data-oriented or process-oriented methodologies. This is because data and processes are given equal importance. Objects are used to combine data with the procedures that operate on that data. The main advantage of using objects is that they provide both abstraction and encapsulation.

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Data Structures and Algorithms with Object-Oriented Design Patterns in C++ By Bruno R. Preiss

Data Structures and Algorithms with Object-Oriented Design Patterns in C++

By Bruno R. Preiss

This book was motivated by my experience in teaching the course E&CE 250: Algorithms and Data Structures in the Computer Engineering program at the University of Waterloo. I have observed that the advent of object-oriented methods and the emergence of object-oriented design patterns has lead to a profound change in the pedagogy of data structures and algorithms. The successful application of these techniques gives rise to a kind of cognitive unification: Ideas that are disparate and apparently unrelated seem to come together when the appropriate design patterns and abstractions are used.

This paradigm shift is both evolutionary and revolutionary. On the one hand, the knowledge base grows incrementally as programmers and researchers invent new algorithms and data structures. On the other hand, the proper use of object-oriented techniques requires a fundamental change in the way the programs are designed and implemented. Programmers who are well schooled in the procedural ways often find the leap to objects to be a difficult one.

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Data Structures and Algorithms in C++ By Michael T. Goodrich, Roberto Tamassia and David M. Mount

Data Structures and Algorithms in C++

By Michael T. Goodrich, Roberto Tamassia and David M. Mount

This book provides a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation. In terms of the computer science and computer engineering curricula, we have written this book to be primarily focused on the Freshman-Sophomore level Data Structures (CS2) course.

This is a "sister"' book to Goodrich-Tamassia, Data Structures and Algorithms in Java (DSAJ), but uses C++ as the basis language instead of Java. This present book maintains the same general structure as DSAJ, so that CS/CE programs that teach data structures in both C++ and Java can share the same core syllabus, with one course using DSAJ and the other using this book.

While this book retains the same pedagogical approach and general structure as DSAJ, the code fragments have been completely redesigned. Because the C++ language supports almost all of Java's basic constructs, it would be tempting to simply translate the code fragments from Java to the corresponding C++ counterparts. We have been careful, however, to make full use of C++'s capabilities and design code in a manner that is consistent with modern C++ usage. In particular, whenever appropriate, we use elements of C++ that are not part of Java, including templated functions and classes, the C++ Standard Template Library (STL), C++ memory allocation and deallocation (and we discuss the associated tricky issues of writing destructors, copy constructors, and assignment operators), virtual functions and virtual class destructors, stream input and output, and C++'s safe run-time casting. However, we have avoided some of C++'s more arcane or easily misused elements, such as pointer arithmetic.

Highlights of this book include:

• Review of basic features of the C++ programming language
• Introduction to object-oriented design with C++ and design patterns
• Consistent object-oriented viewpoint throughout the book
• Comprehensive coverage of all the data structures taught in a typical CS2 course, including vectors, lists, heaps, hash tables, and search trees
• Detailed explanation and visualization of sorting algorithms
• Coverage of graph algorithms and pattern-matching algorithms for more advanced CS2 courses
• Visual justifications (that is, picture proofs), which make mathematical arguments more understandable for students, appealing to visual learners
• Motivation of algorithmic concepts with Internet-related applications, such as Web browsers and search engines
• Accompanying Web site http://datastructures.net with a special password-protected area for instructors.

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Algorithms and Data Structures in VLSI Design By Christoph Meinel and Thorsten Theobald

Algorithms and Data Structures in VLSI Design

By Christoph Meinel and Thorsten Theobald

One of the main problems in chip design is the huge number of possible combinations of individual chip elements, leading to a combinatorial explosion as chips become more complex. New key results in theoretical computer science and in the design of data structures and efficient algorithms can be applied fruitfully here. The application of ordered binary decision diagrams (OBDDs) has led to dramatic performance improvements in many computer-aided design projects. This textbook provides an introduction to the foundations of this interdisciplinary research area, with an emphasis on applications in computer-aided circuit design and formal verification.

Contents
1. Introduction

2. Basics

2.1 Propositions and Predicates - 2.2 Sets, Relations, and Functions - 2.3 Graphs - 2.4 Algorithms and Data Structures - 2.5 Complexity of Algorithms - 2.6 Hashing - 2.7 Finite Automata and Finite State Machines - 2.8 References

Part I: Data Structures for Switching Functions
3. Boolean Functions

3.1 Boolean Algebras - 3.2 Boolean Formulas and Boolean Functions - 3.3 Switching Functions - 3.3.1 Switching Functions with at most Two Variables - 3.3.2 Subfunctions and Shannon's Expansion - 3.3.3 Visual Representation - 3.3.4 Monotone Switching Functions - 3.3.5 Symmetric Functions - 3.3.6 Threshold Functions - 3.3.7 Partial Switching Functions - 3.4 References

4. Classical Representations

4.1 Truth Tables - 4.2 Two-Level Normal Forms - 4.3 Circuits and Formulas - 4.3.1 Circuits - 4.3.2 - Formulas - 4.4 Binary Decision Trees and Diagrams - 4.4.1 Binary Decision Trees - 4.4.2 Branching Programs - 4.4.3 Read-Once Branching Programs - 4.4.4 Complexity of Basic Operations - 4.5 References

5. Requirements on Data Structures in Formal Circuit Verification

5.1 Circuit Verification - 5.2 Formal Verification of Combinational Circuits - 5.3 Formal Verification of Sequential Circuits - 5.4 References

Part II: OBDDs: An Efficient Data Structure
6. OBDDs - Ordered Binary Decision Diagrams

6.1 Notation and Examples - 6.2 Reduced OBDDs: A Canonical Representation of Switching Functions - 6.3 The Reduction Algorithm - 6.4 Basic Constructions - 6.5 Performing Binary Operations and the Equivalence Test - 6.6 References

7. Efficient Implementation of OBDDs

7.1 Key Ideas - 7.1.1 Shared OBDDs - 7.1.2 Unique Table and Strong Canonicity - 7.1.3 ITE Algorithm and Computed Table - 7.1.4 Complemented Edges - 7.1.5 Standard Triples - 7.1.6 Memory Management - 7.2 Some Popular OBDD Packages - 7.2.1 The OBDD Package of Brace, Rudell, and Bryant - 7.2.2 The OBDD Package of Long - 7.2.3 The CUDD Package: Colorado University Decision Diagrams - 7.3 References

8. Influence of the Variable Order on the Complexity of OBDDs

8.1 Connection Between Variable Order and OBDD Size - 8.2 Exponential Lower Bounds - 8.3 OBDDs with Different Variable Orders - 8.4 Complexity of Minimization - 8.5 References

9. Optimizing the Variable Order

9.1 Heuristics for Constructing Good Variable Orders - 9.1.1 The Fan-In Heuristic - 9.1.2 Die Weight Heurisitic - 9.2 Dynamic Reordering - 9.2.1 The Variable Swap - 9.2.2 Exact Minimization - 9.2.3 Window Permutation - 9.2.4 The Sifting Algorithm - 9.2.5 Block Sifting and Symmetric Sifting - 9.3 Quantitative Statements - 9.4 Outlook - 9.5 References

Part III: Applications and Extensions
10. Analysis of Sequential Systems

10.1 Formal Verification - 10.2 Basic Operators - 10.2.1 Generalized Cofactors - 10.2.2 The Constrain Operator - 10.2.3 Quantification - 10.2.4 The Restrict Operator - 10.3 Reachability Analysis - 10.4 Efficient Image Computation - 10.4.1 Input Splitting - 10.4.2 Output Splitting - 10.4.3 The Transition Relation - 10.4.4 Partitioning the Transition Relation - 10.5 References

11. Symbolic Model Checking11.1 Computation Tree Logic - 11.2 CTL Model Checking - 11.3 Implementations - 11.3.1 The SMV System - 11.3.2 The VIS System - 11.4 References

12. Variants and Extensions of OBDDs12.1 Relaxing the Ordering Restriction - 12.2 Alternative Decomposition Types - 12.3 Zero-Suppressed BDDs - 12.4 Multiple-Valued Functions - 12.4.1 Additional Sinks - 12.4.2 Edge Values - 12.4.3 Moment Decompositions - 12.5 References

13. Transformation Techniques for Optimization13.1 Transformed OBDDs - 13.2 Type-Based Transformations - 13.2.1 Definition - 13.2.2 Circuit Verification - 13.3 Linear Transformations - 13.3.1 Definition - 13.3.2 Efficient Implementation - 13.3.3 Linear Sifting - 13.4 Encoding Transformations - 13.5 References

Bibliography

Index

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Data Structures And Algorithms CSEd Tools

Data Structures And Algorithms

CSEd Tools

This book covers some commonly used data structures (such as arrays, linked-lists, trees, and graphs) and algorithms (such as sorting, searching, and various graph algorithms) that are used in Computer Science. The book is intended primarily for the readers who are already familiar with these data structures and algorithms, but would like to get some information on how to perform some advanced or non-trivial operations on/using them. Hence, for each data structure and algorithm, I have generally given a lighter treatment to the basic issues (since they are generally already covered elsewhere) and given more attention to more advanced issues related to them.

The book is actually still in writing, and I will add more material to it. So make sure to check this Web site from time-to-time to see the latest updates.

Note: This book contains several psuedocodes. Implementing these psuedocodes using a programming language will require rewriting them appropriately using language-specific constructs. The pseudocodes may also make some simplifying assumptions, may not check some boundary conditions such as nil input values, and may not check for invalid input values. The solutions provided here may not be the best possible solutions. In fact, simpler and more practical solutions have often been preferred over theoretically-better but more complicated solutions.

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Data Structures and Algorithms By Alison Cawsey

Data Structures and Algorithms

By Alison Cawsey

This course will have two main sections. The first, shorter section will introduce inheritance and polymorphism in object oriented programming. This is material makes the implementation of some datastructures more elegant and more re-useable. However this section of the course is fairly independent of the next. The second, longer section will look at different sorts of algorithms, and in particular string processing algorithms and graph algorithms.

There are various concepts from Data Structure & Algorithms which will be assumed. The basic idea of an abstract datatype is important. You also need to be familiar with pointers for some of the first section. You should also be able to implement and use a stack and a queue (with or without the help of a textbook).

Why should you want to know about algorithms? There are a number of possible reasons:

• To avoid re-inventing the wheel. As you might expect, for many programming problems, someone has already developed a good algorithm to solve that problem. For many of these algorithms, people have formally analysed their properties, so you can be confident in their correctness and efficiency. For example, we know that merge sort and quick sort both work correctly, and have average case complexity O(n log n), so we can straightforwardly use either algorithm in our programs. We might be able to further choose between these two algorithms, depending on properties of our data (is it already almost sorted?).
• To help when developing your own algorithms. Many of the principles of data-abstraction and algorithm design, illustrated by some of the algorithms discussed here, are important in all programming problem. Not all tasks have ``off-the-shelf'' algorithms available, so you will sometimes have to develop your own. Merge sort illustrates a widely applicable technique: split the problem in two, solve each separately, then combine the results. A knowledge of well known algorithms will provide a source of ideas that may be applied to new tasks.
• To help understand tools that use particular algorithms, so you can select the appropriate tool to use. For example, documentation of various compression programs will tell you that pack uses Huffman coding, compress uses LZW, and Gzip uses the Lempel-Ziv algorithm. If you have at least some understanding of these algorithms you will know which is likely to be better and how much reduction in file size you might expect for a given type of file (e.g., does the file involve a lot of repetition of common sequences of characters).
• Because they're neat. Many surprisingly simple and elegant algorithms have been invented. If you have even a slightly Mathematical disposition you should find them interesting in their own right.

For all these reasons it is useful to have a broad familiarity with a range of algorithms, and to know what they may be used for.

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Data Structures and Algorithms Course By John Morris

Data Structures and Algorithms Course

By John Morris

This course will focus on data structures and algorithms for manipulating them. Data structures for storing information in tables, lists, trees, queues and stacks will be covered. Some basic graph and discrete transform algorithms will also be discussed.

Good Programs
There are a number of facets to good programs: they must

• run correctly
• run efficiently
• be easy to read and understand
• be easy to debug and
• be easy to modify.

The first of these is obvious - programs which don't run correctly are clearly of little use. "Efficiently" is usually understood to mean in the minimum time - but occasionally there will be other constraints, such as memory use, which will be paramount. As will be demonstrated later, better running times will generally be obtained from use of the most appropriate data structures and algorithms, rather than through "hacking", i.e. removing a few statements by some clever coding - or even worse, programming in assembler!

This course will focus on solving problems efficiently: you will be introduced to a number of fundamental data structures and algorithms (or procedures) for manipulating them.

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Dictionary of Algorithms and Data Structures

Dictionary of Algorithms and Data Structures

This web site is hosted in part by the Software Quality Group of the Software Diagnostics and Conformance Testing Division, Information Technology Laboratory.

This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann's function. Problems include traveling salesman and Byzantine generals. Some entries have links to implementations and more information. Index pages list entries by area and by type. The two-level index has a total download 1/20 as big as this page.

Don't use this site to cheat. Teachers, contact us if we can help.

To define or correct terms, please contact Paul E. Black. We need help in automata theory, combinatorics, parallel or randomized algorithms, heuristics, and quantum computing. We do not include algorithms particular to business data processing, communications, operating systems or distributed algorithms, programming languages, AI, graphics, or numerical analysis: it is tough enough covering "general" algorithms and data structures. However, if you want to tackle one of these areas, we'll consider including them.

Some terms with a leading variable, such as n-way, m-dimensional, or p-branching, are under k-. You may find terms dealing with hardware, the computer industry, slang, etc., in the Free On-Line Dictionary Of Computing or in A Glossary of Computer Oriented Abbreviations and Acronyms.

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Download free Data Structures and Algorithms ebooks

Download free Data Structures and Algorithms ebooks

This posting provides you free Datastructures and algorithms ebook site links which teaches you to study datastructures through different languages, various alogrithms, complexities, alogrithms directories, graph theories, computer datastructures etc.

You can get free downloads in datastructures, alogrithms, etc. Following are the free ebooks dowloads written by different authors.

1. Algorithmic Information Theory By G J Chaitin
2. Algorithms and Complexity by Herbert S. Wilf
3. Algorithms and Data Structures in VLSI Design By Christoph Meinel and Thorsten Theobald
4. Algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani
5. Algorithms for Modular Elliptic Curves By J. E. Cremona
6. Algorithms for programmers By Jorg Arndt
7. Art of Programming Contest - C Programming Tutorials Data Structures Algorithms
8. Average Case Analysis of Algorithms on Sequences by Wojciech Szpankowski
9. Combinatorial Algorithms By Jeff Erickson
10. Computer Programming Algorithms Directory
11. Data Structure in Java By Sandra Andersen
12. Data Structures And Algorithms - CSEd Tools
13. Data Structures and Algorithms By Alison Cawsey
14. Data Structures and Algorithms By John Morris
15. Data Structures and Algorithms in C++ By Michael T. Goodrich, Roberto Tamassia and David M. Mount
16. Data Structures and Algorithms with Object-Oriented Design Patterns in C# By Bruno R.Preiss
17. Data Structures and Algorithms with Object-Oriented Design Patterns in C++ By Bruno R. Preiss
18. Data Structures and Algorithms with Object-Oriented Design Patterns in Java By Bruno R. Preiss
19. Data Structures By Peter M Williams
20. Data Structures:All Chapters From Wikibooks, the open-content textbooks collection
21. Dictionary of Algorithms and Data Structures
22. Efficient Algorithms for Sorting and Synchronization By Andrew Tridgell
23. Foundations of Computer By Lawrence C Paulson
24. Graph-Theoretic Algorithms By Therese Biedl
25. Java Data Structures (2nd edition)
26. Object Oriented Datastructures using Java By Nell Dale, Daniel T. Joyce and Chip Weems
27. Planning Algorithms By Steven M. LaValle
28. Problems on Algorithms By William Gasarch and Ian Parberry
29. Sorting and Searching Algorithms: A Cookbook By Thomas Niemann
30. The FXT library: Fast transforms and low level algorithms

Algorithms and Data Structures

Algorithms and Data Structures

Algorithms and Data Structures is written by N. Wirth. This book starts with a chapter on data structure for two reasons. First, one has an intuitive feeling that data precede algorithms: you must have some objects before you can perform operations on them. Second, and this is the more immediate reason, this book assumes that the reader is familiar with the basic notions of computer programming. Traditionally and sensibly, however, introductory programming courses concentrate on algorithms operating on relatively simple structures of data. Hence, an introductory chapter on data structures seems appropriate....

The second chapter treats sorting algorithms. It displays a variety of different methods, all serving the same purpose. Mathematical analysis of some of these algorithms shows the advantages and disadvantages of the methods, and it makes the programmer aware of the importance of analysis in the choice of good solutions for a given problem. The partitioning into methods for sorting arrays and methods for sorting files (often called internal and external sorting) exhibits the crucial influence of data representation on the choice of applicable algorithms and on their complexity. The space allocated to sorting would not be so large were it not for the fact that sorting constitutes an ideal vehicle for illustrating so many principles of programming and situations occurring in most other applications. It often seems that one could compose an entire programming course by deleting examples from sorting only.....

Third Chapter discusses recursive algorithms and fourth chapter is included with dynamic information structures. Finally fifth chapter is for hashing.

Contents
1 Fundamental Data Structures
1.1 Introduction
1.2 The Concept of Data Type
1.3 Primitive Data Types
1.4 Standard Primitive Types
1.4.1 Integer types
1.4.2 The type REAL
1.4.3 The type BOOLEAN
1.4.4 The type CHAR
1.4.5 The type SET
1.5 The Array Structure
1.6 The Record Structure
1.7 Representation of Arrays, Records, and Sets
1.7.1 Representation of Arrays
1.7.2 Representation of Recors
1.7.3 Representation of Sets
1.8 The File (Sequence)
1.8.1 Elementary File Operators
1.8.2 Buffering Sequences
1.8.3 Buffering between Concurrent Processes
1.8.4 Textual Input and Output
1.9 Searching
1.9.1 Linear Search
1.9.2 Binary Search
1.9.3 Table Search
1.9.4 Straight String Search
1.9.5 The Knuth-Morris-Pratt String Search
1.9.6 The Boyer-Moore String Search
Exercises
2 Sorting
2.1 Introduction
2.2 Sorting Arrays
2.2.1 Sorting by Straight Insertion
2.2.2 Sorting by Straight Selection
2.2.3 Sorting by Straight Exchange
2.3 Advanced Sorting Methods
2.3.1 Insertion Sort by Diminishing Increment
2.3.2 Tree Sort
2.3.3 Partition Sort
2.3.4 Finding the Median
2.3.5 A Comparison of Array Sorting Methods
2.4 Sorting Sequences
2.4.1 Straight Merging
2.4.2 Natural Merging
2.4.3 Balanced Multiway Merging
2.4.4 Polyphase Sort
2.4.5 Distribution of Initial Runs
Exercises
6
3 Recursive Algorithms
3.1 Introduction
3.2 When Not to Use Recursion
3.3 Two Examples of Recursive Programs
3.4 Backtracking Algorithms
3.5 The Eight Queens Problem
3.6 The Stable Marriage Problem
3.7 The Optimal Selection Problem
Exercises
4 Dynamic Information Structures
4.1 Recursive Data Types
4.2 Pointers
4.3 Linear Lists
4.3.1 Basic Operations
4.3.2 Ordered Lists and Reorganizing Lists
4.3.3 An Application: Topological Sorting
4.4 Tree Structures
4.4.1 Basic Concepts and Definitions
4.4.2 Basic Operations on Binary Trees
4.4.3 Tree Search and Insertion
4.4.4 Tree Deletion
4.4.5 Analysis of Tree Search and Insertion
4.5 Balanced Trees
4.5.1 Balanced Tree Insertion
4.5.2 Balanced Tree Deletion
4.6 Optimal Search Trees
4.7 B-Trees
4.7.1 Multiway B-Trees
4.7.2 Binary B-Trees
4.8 Priority Search Trees
Exercises
5 Key Transformations (Hashing)
5.1 Introduction
5.2 Choice of a Hash Function
5.3 Collision handling
5.4 Analysis of Key Transformation
Exercises
Appendices
A The ASCII Character Set
B The Syntax of Oberon
Index

You can download or read this data structures and algorithm book from the following link.

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Data Structures and Algorithms: Annotated Reference with Examples By Granville Barnett and Luca Del Tongo

Data Structures and Algorithms: Annotated Reference with Examples

By Granville Barnett and Luca Del Tongo

Every book has a story as to how it came about and this one is no different, although we would be lying if we said its development had not been somewhat impromptu. Put simply this book is the result of a series of emails sent back and forth between the two authors during the development of a library for the .NET framework of the same name (with the omission of the subtitle of course!). The conversation started of something like, Why don't we create a more aesthetically pleasing way to present our pseudocode?" After a few weeks this new presentation style had in fact grown into pseudocode listings with chunks of text describing how the data structure or algorithm in question works and various other things about it. At this point we thought, What the heck, let's make this thing into a book!" And so, in the summer of 2008 we began work on this book side by side with the actual library implementation. When we started writing this book the only things that we were sure about with respect to how the book should be structured were:

1. always make explanations as simple as possible while maintaining a moderately fine degree of precision to keep the more eager minded reader happy; and
2. inject diagrams to demystify problems that are even moderately challenging to visualize (. . . and so we could remember how our own algorithms worked when looking back at them); and finally
3. present concise and self explanatory pseudo code listings that can be ported easily to most mainstream imperative programming languages like C++, C#, and Java.

A key factor of this book and its associated implementations is that all algorithms (unless otherwise stated) were designed by us, using the theory of the algorithm in question as a guideline (for which we are eternally grateful to their original creators). Therefore they may sometimes turn out to be worse than the "normal" implementations|and sometimes not. We are two fellows of the opinion that choice is a great thing. Read our book, read several others on the same subject and use what you see fit from each (if anything) when implementing your own version of the algorithms in question. Through this book we hope that you will see the absolute necessity of understanding which data structure or algorithm to use for a certain scenario. In all projects, especially those that are concerned with performance (here we apply an even greater emphasis on real-time systems) the selection of the wrong data structure or algorithm can be the cause of a great deal of performance pain

Therefore it is absolutely key that you think about the run time complexity and space requirements of your selected approach. In this book we only explain the theoretical implications to consider, but this is for a good reason: compilers are very different in how they work. One C++ compiler may have some amazing optimisation phases specically targeted at recursion, another may not, for example. Of course this is just an example but you would be surprised by how many subtle differences there are between compilers. These differences which may make a fast algorithm slow, and vice versa. We could also factor in the same concerns about languages that target virtual machines, leaving all the actual various implementation issues to you given that you will know your language's compiler much better than us...well in most cases. This has resulted in a more concise book that focuses on what we think are the key issues.

One final note: never take the words of others as gospel; verify all that can be feasibly veri¯ed and make up your own mind. We hope you enjoy reading this book as much as we have enjoyed writing it.

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Computational Modeling and Complexity Science By Allen Downey

Computational Modeling and Complexity Science

By Allen Downey

This book is about data structures and algorithms, intermediate programming in Python, complexity science and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower. An algorithm is an mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure (nested dictionaries) that is a good implementation of a graph, and several graph algorithms that use this data structure.

Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python. Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation.

Philosophy of science: The models and results I will present raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology.

There are two kinds of computational models:
Continuous: Many computational models compute discrete approximations of equations that are continuous in space and time. For example, to compute the trajectory of a planet, you could describe planetary motion using differential equations and then compute a numerical approximation of the position of the planet at discrete points in time. The fields of numerical methods and scientific computing tend to focus on these kinds of models.

Discrete: Discrete models include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system. Complexity science is an interdiscipinary field—at the intersection of mathematics, computer science and physics—that focuses on these kinds of models.

And that’s what this book is about.

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Information Retrieval By C. J. van Rijsbergen

Information Retrieval

By C. J. van Rijsbergen

Introduction
Information retrieval is a wide, often loosely-defined term but in these pages I shall be concerned only with automatic information retrieval systems. Automatic as opposed to manual and information as opposed to data or fact. Unfortunately the word information can be very misleading. In the context of information retrieval (IR), information, in the technical meaning given in Shannon's theory of communication, is not readily measured (Shannon and Weaver). In fact, in many cases one can adequately describe the kind of retrieval by simply substituting 'document' for 'information'. Nevertheless, 'information retrieval' has become accepted as a description of the kind of work published by Cleverdon, Salton, Sparck Jones, Lancaster and others. A perfectly straightforward definition along these lines is given by Lancaster: 'Information retrieval is the term conventionally, though somewhat inaccurately, applied to the type of activity discussed in this volume. An information retrieval system does not inform (i.e. change the knowledge of) the user on the subject of his inquiry. It merely informs on the existence (or non-existence) and whereabouts of documents relating to his request.' This specifically excludes Question-Answering systems as typified by Winograd and those described by Minsky]. It also excludes data retrieval systems such as used by, say, the stock exchange for on-line quotations....

The structure of the book

The introduction presents some basic background material, demarcates the subject and discusses loosely some of the problems in IR. The chapters that follow cover topics in the order in which I would think about them were I about to design an experimental IR system. They begin by describing the generation of machine representations for the information, and then move on to an explanation of the logical structures that may be arrived at by clustering. There are numerous methods for representing these structures in the computer, or in other words, there is a choice of file structures to represent the logical structure, so these are outlined next. Once the information has been stored in this way we are able to search it, hence a discussion of search strategies follows. The chapter on probabilistic retrieval is an attempt to create a formal model for certain kinds of search strategies. Lastly, in an experimental situation all of the above will have been futile unless the results of retrieval can be evaluated. Therefore a large chapter is devoted to ways of measuring the effectiveness of retrieval. In the final chapter I have indulged in a little speculation about the possibilities for IR in the next decade.

The two major chapters are those dealing with automatic classification and evaluation. I have tried to write them in such a way that each can be read independently of the rest of the book (although I do not recommend this for the non-specialist).

Outline

• Chapter 2: Automatic Text Analysis - contains a straightforward discussion of how the text of a document is represented inside a computer. This is a superficial chapter but I think it is adequate in the context of this book.
• Chapter 3: Automatic Classification - looks at automatic classification methods in general and then takes a deeper look at the use of these methods in information retrieval.
• Chapter 4: File Structures - here we try and discuss file structures from the point of view of someone primarily interested in information retrieval.
• Chapter 5: Search Strategies - gives an account of some search strategies when applied to document collections structured in different ways. It also discusses the use of feedback.
• Chapter 6: Probabilistic Retrieval - describes a formal model for enhancing retrieval effectiveness by using sample information about the frequency of occurrence and co-occurrence of index terms in the relevant and non-relevant documents.
• Chapter 7: Evaluation - here I give a traditional view of the measurement of effectiveness followed by an explanation of some of the more promising attempts at improving the art. I also attempt to provide foundations for a theory of evaluation.
• Chapter 8: The Future - contains some speculation about the future of IR and tries to pinpoint some areas of research where further work is desperately needed.

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Algorithms for Compiler Design By Kakde

                                                           

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