Showing posts with label computer science. Show all posts
Showing posts with label computer science. Show all posts

Handbook of Data Structures and Applications (Chapman & Hall/CRC Computer & Information Science Series) Review

Handbook of Data Structures and Applications (Chapman and Hall/CRC Computer and Information Science Series)
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This is a very useful book to have. It looks to me that the book is a compilation of various research papers by different authors. Provides a unique view to Data structures and their applications.

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Although there are many advanced and specialized texts and handbooks on algorithms, until now there was no book that focused exclusively on the wide variety of data structures that have been reported in the literature. The Handbook of Data Structures and Applications responds to the needs of students, professionals, and researchers who need a mainstream reference on data structures by providing a comprehensive survey of data structures of various types.Divided into seven parts, the text begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. The Handbook is invaluable in suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

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Database Systems Concepts with Oracle CD Review

Database Systems Concepts with Oracle CD
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This book is a requirement for a course that I am taking. The book is riddled with errors. You can randomly open any page from the book- and you will find typos and confusing text. On some pages, the powerpoint slides for the book do not even match with the text of the book. This is the sixth edition of this textbook. I consider it inexcusable that the book has hundreds of typos even after being published for more than ten years. As another reviewer pointed out, the additional material that is needed for the book -like SQL schemas- do not even exist in the book's website- contrary to what the book claims. Anybody who is considering this book should think twice. Please get it from the library - go through the book for a week to see what I mean- and dump the book for any book that should be better than this.

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The Fourth edition of Database System Concepts has been extensively revised from the 3rd edition. The new edition provides improved coverage of concepts, extensive coverage of new tools and techniques, and updated coverage of database system internals. This text is intended for a first course in databases at the junior or senior undergraduate, or first-year graduate level. Database System Concepts, 4th ed. offers a complete background in the basics of database design, languages, and system implementations. Concepts are presented using intuitive descriptions, and important theoretical results are covered, but formal proofs are omitted.The fundamental concepts and algorithms covered in Database System Concepts 4th ed. are based on those used in existing commercial or experimental database systems.The authors present these concepts and algorithms in a general setting that is not tied to one particular database system.

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Fundamentals of Data Structures in C++ Review

Fundamentals of Data Structures in C++
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This book treats the subject of algorithm analysis and data structure with great formalism. This book, in my view, is a must for any under grad course. This book lays foundation for a career in systems programming. However, if you only have passing interest in computer science, this is not a book for you.
sunil@liberate.com

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Data Structures and the Standard Template Library Review

Data Structures and the Standard Template Library
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Paid about $100 dollars for this book only to find more bugs than a rainforest; frustrating since the author teaches at the university level but the code resembles that of a novice programmer.
Do yourself a favor and go with another book. You're literally better off
crumpling your cash money and throwing it in the trash rather than buying this disappointment.

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Learning in Graphical Models (NATO Science Series D: (closed)) Review

Learning in Graphical Models (NATO Science Series D: (closed))
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The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.
To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.
What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

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In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject underlies the currentvolume. Two research communities which have used graphical or networkformalisms to particular advantage are the belief networkcommunity and the neural network community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and design, theory and applications. Exact methods, samplingmethods and variational methods are discussed in detail. Audience: A wide cross-section of computationally orientedresearchers, including computer scientists, statisticians, electricalengineers, physicists and neuroscientists.

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Data Structures & Algorithms in Java with CDROM (Mitchell Waite Signature) Review

Data Structures and Algorithms in Java with CDROM (Mitchell Waite Signature)
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I am surprised that most instructors haven't banned this book! It is absolutely one of the best data structures references on the market and with answers provided with the enclosed CD, one perfect "cheat book." Virtually all the standard data structures for an introductory DS&A course are included here with a good explanation behind the rationale used in the implementation of the code. Lafore is a good writer and explains things well, unlike certain authors. The book isn't heavy on the mathematics, which is good for programmers who don't want to get involved with theory. The applets which implement the data structures are particularly nice.
As mentioned in a previous review, trees are not covered well in this book, but most introductory books don't cover them well either. I don't expect to see an analysis of AVL or red-black trees in an introductory book (Cormen's text, which is the standard for grad school, doesn't explain trees well either). In fact, only Schaffer's book does a creditable job of explaining AVL trees but the implementation of the code isn't the greatest. But for linked lists, stacks,queues, and the like, there are few books that are the equal of this one. Buy the book and you'll pass your DS&A class with flying colors!

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Not filled with with obtuse mathematics and difficult proofs, MWSS:Data Structures and Algorithms in Java removes the mystique from DS&A. It does this in two ways. First, the text is written in a straightforward style, making it accessible to anyone. Second, unique new Java demonstration programs, called "Workshop Applets," are provided with the book. These Workshop Applets provide interactive "moving pictures" which the user can control and modify by pressing buttons. The books text describes specific operations the user can carry out with these Workshop Applets, and the applets then reveal the inner workings of an algorithm or data structure.

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Purely Functional Data Structures Review

Purely Functional Data Structures
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Okasaki's slim volume is one of the best expositions on implementing data structures & algorithms in a functional language. After taking an introductory course on functional programming, this would be the book which tells you where to go next.
This book doesn't just present a rehash/rewrite of imperative data structures, only written in a functional language. Instead, Okasaki makes sure to emphasize benefits which only functional programming can bring to the table. For example, many functional data structures can compactly represent not just their current state, but all of their past states as well--a feature called "Persistence". Also, functional newbie programmers might be wondering why lazy vs. strict programming is a big deal, and Okasaki shows clearly where data structures can benefit from either being lazy or being strict.
For the advanced reader, Okasaki also presents several powerful techniques for analyzing the runtime of algorithms, including the so-called "Banker's Method" and the "Physicist's Method" for analyzing amortized algorithms.
I hope that Okasaki comes out with a 2nd edition of this book; there is one missing piece in particular which I really wish he would have included: Although he presents an EXTREMELY lucid description of how to implement Red-Black trees in a functional language, he only presented algorithms for insertion and querying. Of course, deletion from a red-black tree is the hardest part, left here, I suppose, as an exercise to the student. If you want to supply this missing piece yourself, check out a paper by Stefan Kars, "Red-black trees with types", J. Functional Programming 11(4):425-432, July, 2001. It presents deletion routines, but you'll still want to read Okasaki's book first, for unless you're very much smarter than me you won't be able to understand Kars' paper until you read Okasaki's exposition of red black trees.
Finally, this book is not just useful for programmers in functional languages; logic programmers, using prolog or a varient, will also find this book very helpful, because most of the techniques (all of the techniques, really, with the exception perhaps of the lazy programming stuff) can be directly applied in a prolog programming setting as well.
After reading this book and implementing some of the data structures for yourself, you'll be amazed at how fast algorithms can run, even when written in a functional language!

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Most books on data structures assume an imperative language such as C or C++.However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme.This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures.The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages.All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

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C++ Plus Data Structures Review

C++ Plus Data Structures
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I've been in college as teacher and student for almost 40 years, and this is one of the most soul-destroying textbooks in any field I have studied.
When introducing fundamentals, the authors often express themselves poorly and try to compensate through repetition and paraphrase. This confuses the student fresh to the material, since it is not clear whether the same point is being repeated, or developed, or a new one introduced. On the other hand, they simply skirt concepts--exception handling, for example--that need to be explained in detail. Instead of straightforwardly presenting new material, they tiptoe around it through constructs of their own design which are no more easy to learn and whose relation to the essential point is unclear. For example, something like myNode->next->back refers to the node pointed to by the back pointer of the node following myNode, i.e. to myNode itself. Not too mind-boggling. But first they have to lead us through a song and dance about a notation they invented which represents the preceding as back(next(myNode)), which is (a) unnecessary, (b) more complex, and (c) really confusing because (a) it is exactly backwards, and (b) the most deeply nested item is one that dominates the structure. The whole book is like this, a weird combination of aimlessness with a hidden agenda, expressed in patronizing, humorless, finger-wagging.
Other reviewers call it a good reference work; it is not. It is poorly indexed, partly because the explanation of a single concept may appear at intervals through several chapters of context which, though rambling, is a necessary to grasp the meaning.
Note that the enthusiastic reviews of this book come from a software reviewer, someone who finds this the clearest of the _three_ data structure books he's used, and someone who recommends it to advanced programmers, granting that you have to understand C++ classes to follow the book--although the book's task is to _introduce_ C++ classes. Another says that all previous courses just taught C++ coding--but this is a second-semester text! In other words, it's a great book to own if you don't need it.
I think teachers who like this book don't actually read it, just flip through it, checking points, "Lists, yup, linked lists, yup, doubly-linked lists, with circles and arrows on the back, yup, yup, yup, it's all there," because THEY DON'T HAVE TO LEARN ANYTHING FROM IT. I can't see how any teacher could adopt it who tried to read it from the point of view of someone new to the material presented.

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C++ Plus Data Structures is designed for a course in Data Structures w here C++ is the programming language. The book focuses on abstract dat a types as viewed from three different perspective: their specificatio n, their application, and their implementation. The authors stress co mputer science theory and software engineering principles, including m odularization, data encapsulation, information hiding, data abstractio n, object-oriented decomposition, functional decomposition, the analys is of algorithms, and life-cycle software verification methods. Final ly, through classic Dale pedagogy students are offered a clear, easy-t o-understand discussion of important theoretical constructs and their implementation in C++.

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Data Structures and Algorithms in Java, Second Edition Review

Data Structures and Algorithms in Java, Second Edition
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I was looking for a latest edition of a book on data structures using Java. Lafore, Martin etc. are good texts but since collection API has introduced in jdk 1.2, those books seems to be obsolete. This book discusses new API and classes provided by java environment, so we need not to be bogged down by writing the unnecessary code. I feel this book is good for professionals in the industry as well as students of computer science. Algorithms are dealt with in detail with all the mathematical proofs involved, regarding efficiency and complexity. Various data structures are discussed with sufficient number of examples. Its a language specific book on data structures and algorithms, so if you are looking for a general book ar if you are working with any other language you will be dissappointed but surely a good book for java programming language.

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Using the Java programming language, author Adam Drozdek highlights three important aspects of data structures and algorithms. First, the book places special emphasis on the connection between data structures and their algorithms, including an analysis of the algorithms' complexity. Second, the book presents data structures in the context of object-oriented program design, stressing the principle of information hiding in its treatment of encapsulation and decomposition. Finally, the book closely examines data structure implementation. Overall, this practical and theoretical book prepares students with a solid foundation in data structures for future coursesand work in design implementation, testing, or maintenance of virtually any software system.

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Process Mining: Discovery, Conformance and Enhancement of Business Processes Review

Process Mining: Discovery, Conformance and Enhancement of Business Processes
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This book is a must-read for everyone who is interested in Process Mining. It covers the fundamentals and basics of this emerging discipline, and it gives a comprehensive overview of the state of the art in the field.
The book is written by Wil van der Aalst, who has started Process Mining research at the Technical University in Eindhoven about twelve years ago, and who has since been in the center of the developments around this new technology.
Be aware that this book is not a practical handbook that explains how to do Process Mining on a step-by-step basis. Instead, it provides a comprehensive overview about the field of Process Mining as a whole. Although the book does not shy away from technical details, it is easy to read. It provides a very good introduction but also highlights the challenges and complexity of Process Mining when dealing with real-life processes. If you prefer to skip the formal definitions you can still get a good overview because there are many concrete examples.

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The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.


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Classic Data Structures in C++ Review

Classic Data Structures in C++
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Keep this book on your shelf for a long time, you'll look back to it often. The exmaples use STL (standard templete library) so you don't have to worry about the code being useless when it comes to your data struct. Lastly, don't be intimidated by this book; accessible to both the beginner and expert.

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Pattern Recognition, Third Edition Review

Pattern Recognition, Third Edition
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I bought this book to teach my students on the subject. I am a professor in computer engineering and PR was not my research focus. However, there are many topics covered in this book, which have become more applicable in our area of research (VLSI design). We found this book easy to use. The algorithms are clearly described and my students could implement them easily by just reading the specific chapters we need. We think this is an excellent book to teach ourselves how to apply various PR algorithms in our domain.

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Principles of Database & Knowledge-Base Systems, Vol. 1: Classical Database Systems Review

Principles of Database and Knowledge-Base Systems, Vol. 1: Classical Database Systems
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This book goes into the details of database conception and use. It's not completely up to date but tells you everything on relational databases. from theory to the actual used algorithms.

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Classical database technology is oriented toward a well-understood class of applications. Recently the field has attempted to solve the problems associated with new kinds of applications: computer-aided design, software engineering, and others that combine the need to deal with large amounts of data efficiently and the need to support queries in languages that are more expressive than those found in classical database systems. This book attempts to integrate the study of both the new and the classical forms of database systems.

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Knowledge Representation and the Semantics of Natural Language (Cognitive Technologies) Review

Knowledge Representation and the Semantics of Natural Language (Cognitive Technologies)
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This book has a wealth of solid information about knowledge representation (KR). It is focused on Multinets, an advanced type of semantic network. The author brings years of experience to this subject. The book deals thoroughly with many aspects of KR such as relationships, categories, generalization, real vs hypothetical, cardinality, and variability, just to name a few. Although the book is focused on Multinets, the concepts should be relevant to other knowledge representation schemes.
Frequent natural language statements clarify the issues under discussion. For example, here are two of the statements on page 49 that illustrate different types of information that can be communicated by "is".
"'A bachelor is an adult unmarried man.' (Relation EQU), 'The cherry is red.' (Relation PROP), . . ."
In other words, the first "is" links equivalent ideas, the second "is" indicates "red" as a property.
The book also contains numerous, well designed, helpful diagrams.
Unfortunately, the jargon is very thick, and difficult to navigate. For example, from page 20: "As already mentioned, the arcs of the semantic network have to be considered as epistemologically and cognitively justified categories which function as fundamental deep semantic relations in the framework of meaning representation." After reading this sentence several times, I think part of what it means is "the arcs of semantic networks should match actual deep semantic relations". But I'm not sure. I frequently find myself reading sentences several times, then moving on, only partly satisfied.
If you can get past the jargon, the book contains a thorough, solidly supported treatment of the knowledge representation of natural language.
Perhaps someone with more prior experience with knowledge representation would find the book more approachable.
Based on the quantity of information, the strong relevance to knowledge representation and the clarifying examples, I give this a generous 4 star rating. However, I'm worried that some readers will find the jargon so impenetrable that the book would lose most of its value to them.

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The book presents an interdisciplinary approach to knowledge representation and the treatment of semantic phenomena of natural language, which is positioned between artificial intelligence, computational linguistics, and cognitive psychology. The proposed method is based on Multilayered Extended Semantic Networks (MultiNets), which can be used for theoretical investigations into the semantics of natural language, for cognitive modeling, for describing lexical entries in a computational lexicon, and for natural language processing (NLP).Part I deals with fundamental problems of semantic knowledge representation and semantic interpretation of natural language phenomena. Part II provides a systematic description of the representational means of MultiNet, one of the most comprehensive and thoroughly specified collections of relations and functions used in real NLP applications.MultiNet is embedded into a system of software tools comprising a workbench for the knowledge engineer, a semantic interpreter translating natural language expressions into formal meaning structures, and a workbench for the computer lexicographer. The book has been used for courses in artificial intelligence at several universities and is one of the cornerstones for teaching computational linguistics in a virtual electronic laboratory.

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Data Structures: A Pseudocode Approach with C Review

Data Structures: A Pseudocode Approach with C
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I had to buy this book for a computer science course. Thankfully I got a refund on it when I tested out of the course two weeks later. For those who are forced to learn the contents of this book, here is what to expect, as I read the entire book.
Since I was already very familiar with most concepts ( ie. actually programmed them ), I have to say that no book has made me more confused or angry than this one. Ideas that are simple are obscured with inappropriate examples / wordings, so I actually had to read many paragraphs TWICE, to get the point of the author. Even the pictures are far from refined, and the presentation is quite amateur. There were several times I just wanted to throw the book at the wall in disgust.
As a first (and unbelievable) example, from page 2 you are given the "Commandments" of good Pseudocode. One such rule is never to use identifiers such as 'i' or 'j', as they should instead be given an 'english' name such as 'student'. On page 6, we have the first code example, where they proceed to use 'i' and 'j' within the code. Then they explain that, oh yes, 'i' and 'j' is actually a loop tradition in C++! So we're already confused! By page 6. Now, the beautiful thing is that you can read this for yourself right now, with Amazon's page excerpts. This sets the level of presentation for the rest of the book.
My apologies to those who may like this book, but please take time to explore other books before this one. Programming is far easier than is explained in this book. At this level of programming development, a bad experience can be most discouraging. And this book is very, very bad.

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This second edition expands upon the solid, practical foundation established in the first edition of the text. A new four-part organizational structure increases the flexibility of the text, and all material is presented in a straightforward manner accompanied by an array of examples and visual diagrams.

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Information Theory, Inference and Learning Algorithms Review

Information Theory, Inference and Learning Algorithms
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Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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An Introduction to Data Structures and Algorithms (Progress in Theoretical Computer Science) Review

An Introduction to Data Structures and Algorithms (Progress in Theoretical Computer Science)
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If you have cut your teeth on Donald Knuth's classic three volumes, "The Art of Computer Programming", and you want more detail, at a similar level of complexity, then consider Storer's book.
It delves into lists, recursion, trees, graphs, heaps and sets. Like Knuth, Storer thoughtfully supplies an extensive list of questions at the end of each chapter that will greatly deepen your appreciation of the field if you tackle them. Ok, he doesn't give answers, but think of that as greater incentive on your part to solve them. There are almost 400 questions in the book.
The teaching style is similar to Knuth, in that it has all the rigour needed by an algorithm designer like yourself, without drowning you in epsilon-delta ultra rigour like a pure maths text.
Note that the only code fragments are in pseudocode. This should not be a problem for you. I am assuming you are experienced enough that what you need is understanding of an algorithm, and that manually converting it to code is straightforward and a purely secondary issue.
The take home message is that this is excellent for anyone doing serious programming.

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Data structures and algorithms are presented at the college level in a highly accessible format that presents material with one-page displays in a way that will appeal to both teachers and students. The thirteen chapters cover: Models of Computation, Lists, Induction and Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Graphs, Strings, Discrete Fourier Transform, Parallel Computation. Key features: Complicated concepts are expressed clearly in a single page with minimal notation and without the "clutter" of the syntax of a particular programming language; algorithms are presented with self-explanatory "pseudo-code." * Chapters 1-4 focus on elementary concepts, the exposition unfolding at a slower pace. Sample exercises with solutions are provided. Sections that may be skipped for an introductory course are starred. Requires only some basic mathematics background and some computer programming experience. * Chapters 5-13 progress at a faster pace. The material is suitable for undergraduates or first-year graduates who need only review Chapters 1 -4. * This book may be used for a one-semester introductory course (based on Chapters 1-4 and portions of the chapters on algorithm design, hashing, and graph algorithms) and for a one-semester advanced course that starts at Chapter 5. A year-long course may be based on the entire book. * Sorting, often perceived as rather technical, is not treated as a separate chapter, but is used in many examples (including bubble sort, merge sort, tree sort, heap sort, quick sort, and several parallel algorithms). Also, lower bounds on sorting by comparisons are included with the presentation of heaps in the context of lower bounds for comparison-based structures. * Chapter 13 on parallel models of computation is something of a mini-book itself, and a good way to end a course. Although it is not clear what parallel

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