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| Author | : Bradley N. Miller |
| Publisher | : Franklin Beedle & Associates |
| Total Pages | : 0 |
| Release | : 2011 |
| Genre | : Algorithms |
| ISBN | : 9781590282571 |
Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms.
| Author | : Russell L. Shackelford |
| Publisher | : |
| Total Pages | : 468 |
| Release | : 1999 |
| Genre | : Computers |
| ISBN | : 9780201636130 |
Introduction to Computing and Algorithms prepares students for the world of computing by giving them a solid foundation in the study of computer science - algorithms. By taking an algorithm-based approach to the subject, this book helps readers grasp overall concepts rather than getting them bogged down with specific syntax details of a programming language that can become obsolete. Students work with algorithms from the start and apply these ideas to real problems that computers can help solve. The benefit of this approach is that students will understand the power of computers as problem-solving tools, learn to think like programmers, and gain an appreciation of the computer science discipline.
| Author | : Thomas H. Cormen |
| Publisher | : MIT Press |
| Total Pages | : 1313 |
| Release | : 2009-07-31 |
| Genre | : Computers |
| ISBN | : 0262258102 |
The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
| Author | : Thomas Mailund |
| Publisher | : Apress |
| Total Pages | : 657 |
| Release | : 2021-07-31 |
| Genre | : Computers |
| ISBN | : 9781484270769 |
Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer. Learning how to program can be very rewarding. It is a special feeling to seeing a computer translate your thoughts into actions and see it solve your problems for you. To get to that point, however, you must learn to think about computations in a new way—you must learn computational thinking. This book begins by discussing models of the world and how to formalize problems. This leads onto a definition of computational thinking and putting computational thinking in a broader context. The practical coding in the book is carried out in Python; you’ll get an introduction to Python programming, including how to set up your development environment. What You Will Learn Think in a computational way Acquire general techniques for problem solving See general and concrete algorithmic techniques Program solutions that are both computationally efficient and maintainable Who This Book Is For Those new to programming and computer science who are interested in learning how to program algorithms and working with other computational aspects of programming.
| Author | : Hemant Jain |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 436 |
| Release | : 2016-10-21 |
| Genre | : |
| ISBN | : 9781539724124 |
This book is about the usage of Data Structures and Algorithms in computer programming. Designing an efficient algorithm to solve a computer science problem is a skill of Computer programmer. This is the skill which tech companies like Google, Amazon, Microsoft, Adobe and many others are looking for in an interview. This book assumes that you are a JAVA language developer. You are not an expert in JAVA language, but you are well familiar with concepts of references, functions, lists and recursion. In the start of this book, we will be revising the JAVA language fundamentals. We will be looking into some of the problems in arrays and recursion too. Then in the coming chapter, we will be looking into complexity analysis. Then will look into the various data structures and their algorithms. We will be looking into a Linked List, Stack, Queue, Trees, Heap, Hash Table and Graphs. We will be looking into Sorting & Searching techniques. Then we will be looking into algorithm analysis, we will be looking into Brute Force algorithms, Greedy algorithms, Divide & Conquer algorithms, Dynamic Programming, Reduction, and Backtracking. In the end, we will be looking into System Design, which will give a systematic approach for solving the design problems in an Interview.
| Author | : John V. Guttag |
| Publisher | : MIT Press |
| Total Pages | : 466 |
| Release | : 2016-08-12 |
| Genre | : Computers |
| ISBN | : 0262529629 |
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
| Author | : Daniel Zingaro |
| Publisher | : No Starch Press |
| Total Pages | : 409 |
| Release | : 2020-12-15 |
| Genre | : Computers |
| ISBN | : 1718500807 |
A hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer. Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies The heap data structure to determine the amount of money given away in a promotion The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?
| Author | : Daniel U. Wilde |
| Publisher | : Prentice Hall |
| Total Pages | : 446 |
| Release | : 1973 |
| Genre | : Computers |
| ISBN | : |
Author is an alumnus of Evanston Township High School, class of 1956.
| Author | : Peter J. Denning |
| Publisher | : MIT Press |
| Total Pages | : 266 |
| Release | : 2019-05-14 |
| Genre | : Computers |
| ISBN | : 0262353423 |
This pocket-sized introduction to computational thinking and problem-solving traces its genealogy centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation. Eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, “computational thinking” has become part of the K–12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview—tracing a genealogy that begins centuries before digital computers and portraying computational thinking as the pioneers of computing have described it. The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as “computers”) who performed complex calculations as teams engaged in CT long before electronic computers. In each chapter, the author identify different dimensions of today's highly developed CT: • Computational Methods • Computing Machines • Computing Education • Software Engineering • Computational Science • Design Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.
| Author | : Martin Erwig |
| Publisher | : MIT Press |
| Total Pages | : 333 |
| Release | : 2017-08-11 |
| Genre | : Computers |
| ISBN | : 0262036630 |
This easy-to-follow introduction to computer science reveals how familiar stories like Hansel and Gretel, Sherlock Holmes, and Harry Potter illustrate the concepts and everyday relevance of computing. Picture a computer scientist, staring at a screen and clicking away frantically on a keyboard, hacking into a system, or perhaps developing an app. Now delete that picture. In Once Upon an Algorithm, Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm. Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie Groundhog Day illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter’s world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; “intractable” problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms. This engaging book explains computation accessibly and shows its relevance to daily life. Something to think about next time we execute the algorithm of getting up in the morning.
