Book Description
Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Engineering, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective. This book offers theoretical analysis techniques as well as algorithmic design patterns and experimental methods for the engineering of algorithms.
Market: Computer Scientists; Programmers.
Customer Reviews:
Egh! Good science bad english.......2006-10-25
I'm a grad. student using this book, and understanding the written language amounts to same sensation as you would get during root canal. While examples are concise, pseudo-code is excellent, despite the few actual examples in Java, it covers vast range of topics rather well. But for crying out loud, hire an editor for the next edition that will break down paragraph long sentences and introduce some readability to the text! For that reason I tend to gravitate to my undergrad books for my current studies.
An excellent textbook.......2005-03-15
After researching a variety of alternatives, I settled upon using Algorithm Design by Goodrich and Tamassia to teach a junior-level algorithms course and the experience has been quite positive.
While instructor material grows a bit sparse in the last half or third of the book, it's still quite useful. The slides are great and many of the problems have solutions available. Moreover, the solutions tend to be correct (I've only found two mistakes, which may be covered in the up-to-date errata).
The layout of the sections and chapters is quite natural and easy to adapt to your own course, although the last third of the book is essentially an assortment of topics that didn't fit in anywhere else. I would advise bringing some of those topics in to the course early on as diversions from the main material, which admittedly can get a little dry.
For the most part, the book is well written with interesting and adequete end of chapter problems. There are relatively few Java-based examples, but we skipped over them anyhow -- the pseudo-code is quite acceptable. Goodrich and Tamassia give a broad survey of topics, but cover them in enough depth and with enough rigor for an undergraduate course where CLR(S) would be overkill. I recommend it.
A good introduction text to algorithms.......2002-12-18
I would not consider this book as the ultimate book for algorithms as the title suggests. :-) However, it is a very readable book, and filled with brief, but concise observations. Do not get me wrong. This book also has very detailed explanations of fundamental data structures and algorithms.
Also, the best part of the book is that it lists good references for further readings. I loved this book. I would suggest this book to others. Math hints at the back of the books are useful, and some excercises are quite brain challenging. I think it is a great practice for students, but it is not so fun when students get it for assignments. :-)
Good Fundamentals.......2002-07-01
There are many good books with this title or similar ones. This is probably one of the better ones for your bookshelf and for use in academia. The examples are written in Java (a current language) and they are easy to read. The presentation is clean and illustrative. The authors have a good track record for expertise and papers published, and you get the sense that it is more real-world than most similar books.
Book Description
The field of multidimensional data structures is large and growing very quickly. Here, for the first time, is a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets.
The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Along with the hundreds of worked exercises and hundreds of illustrations, the result is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programming, image processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design.
* First comprehensive work on multidimensional data structures available, a thorough and authoritative treatment.
* An algorithmic rather than mathematical approach, with a liberal use of examples that allows the readers to easily see the possible implementation and use.
* Each section includes a large number of exercises and solutions to self-test and confirm the reader's understanding and suggest future directions.
* Written by a well-known authority in the area of spatial data structures who has made many significant contributions to the field.
The author's website includes: Spatial Index Demos
Customer Reviews:
As good as it gets.......2007-09-27
The best possible scenario for a reader buying a tech book is to have
1) a single author, rather than an "editor" so the conceptual presentation and perspective of the product is consistent throughout
2) an author that knows the field inside out and can speak and think about it naturally with no hint in the presentation that he or she has hit upon a topic they're shaky with, and needs to resort to "high concept hand waving" to skate over the subject.
3) an author whose command of language is first rate - precise without being pedantic, and whose tone and level of exposition remains consistent throughout.
4) an author who spares himself nothing in terms of effort, cuts no corners and leaves nothing out for the student to "fill in" when explaining difficult concepts.
In this book, you get all that and more.
It's an encyclopedia of multi-d DS, written by a top researcher in the field, and addresses the subject matter at every level, from the panoramic to the implementation details. This book is on par with Jim Gray's near-perfect Transaction Processing.
If you think you don't need to know the subject matter in this book, you might want to think again. If you're developing anything that needs to find, index or classify information of any sort, graphic, text or otherwise and you're developing the basic technology, then this book is going to pay you the following dividends:
-save you time by getting you firmly grounded in the field,
-confirm and elevate your existing approach,
-make you aware of approaches, concepts and results that you just can't live in ignorance of and succeed.
A true classic, seminal and authoritative.......2007-01-18
Hanan Samet, the world-reknown authority on multi-dimensional data, has written a comprehensive and stunningly beautiful book. The illustrations that appear in the margins of almost every page serve to wonderfully augment the text and convey the essence of the topic under discussion. If you enjoy the clarity and broad coverage of Knuth's classics, or the elegance and wonder of Tufte's monographs, you will love this book.
Samet has distilled a lifetime of work understanding the algorithms of others and inventing major new algorithms and data structures into this very readable survey. The annotated bibliography and multiple indexes are amazing accomplishments in their own right. The book is very reasonably priced, making it accessible. This delightful book deserves to be on the bookshelf of every computer science scholar and programmer. X. Hao is right: this is truly a masterpiece.
The best book on spatial, multidimensional, and metric data structures.......2006-11-21
The most complete book on the subject to date. In addition, to the huge amount of information covered, it also contains a thorough bibliography with over 2000 entries. The author uses an algorithmic approach with plenty of pseudo-code without resorting to complicated mathematical formulae. Clear explanations are given with more than 450 figures illustrating the ideas. The result is a wonderful place to explore spatial, multidimensional, and metric data structures on one's own or as part of a class. It has more than 1200 exercises that test the readers' understanding of the covered material, while many also develop the material in the text further. Solutions are provided to most of the exercises and also contain detailed pseudo code for many of the representations. The book is easily accessible to a wide range of readers who need not be programmers or computer scientists. Sample pages for the opening discussion in each of the book's four chapters are available at the publisher's web site.
This book goes far beyond Hanan Samet's previous books containing completely new material such as a thorough discussion of image- and object-based representations, as well as an entire chapter on high-dimensional and metric data representations which together comprise almost two-thirds of the book. In addition, the new book expands considerably the discussion of point data in his out of print book titled "The Design and Analysis of Spatial Data Structures," which though still contains some material that is not in the new book. The new book has no overlap with his other out of print book titled "Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS".
To summarize, this is another wonderful book from the most respected authority in the field. From novice to expert, everyone can learn something from this true masterpiece.
Encyclopedia of Spatial , Multidimensional, and Metric Data Structures and Algorithms.......2006-09-02
A stunning 1000 page encyclopedia of spatial, multidimensional, and metric data structures and algorithms presented in the Knuth tradition. The general coverage is broader than an older, now out of print and expensive: "Design and Analysis of Spatial Data Structures". In a surprise, the new book is not only the size of a telephone directory, but it has double the number of useful pages. 4 extensive chapters cover data structures and algorithms for: points, objects and images, intervals and small rectangles, and the same data types in higher +dimensions. Within each chapter, the algorithms and clearly presented and are accompanied by an extensive use of figures. The algorithms which run from the expected to the exotic are summarized by the table of contents at the publisher's web site. Unexpected algorithms are also covered including: nearest neighbor finding which is useful for clustering applications, image pyramids, and object pyramids or hierarchies such as R-trees.
The book has a textbook flavor with exercises at the end of each section where specifics are left for the student; however, solutions and pseudo-code for many of the exercises are in a 300+ page appendix maintaining the book as a useful reference. This book is comprehensive, inexpensive, and in my mind - a must have.
Average customer rating:
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Random Iterative Models (Stochastic Modelling and Applied Probability)
Marie Duflo
Manufacturer: Springer
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ASIN: 3540571000 |
Book Description
The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multiaccess broadcast channels, self-learning of neural networks ...). This book provides an up-to-date view of a wide range of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning ...Mathematicians (researchers and also students) and engineers will find here a self-contained account of many approaches to those theories.
Book Description
This book offers a well-balanced presentation on designing algorithms, complexity analysis of algorithms, and computational complexity that is accessible to mainstream computer science students who have a background in college algebra and discrete structures.
Customer Reviews:
Foundations of Algorithms Using C++ Pseudocode.......2007-02-18
Received the book in just four days...so it was prompt service and the book was in excellent condition when it arrived. Overall, I'm extremely satisfied with the service provided and the condition of the book.
Awful Textbook.......2006-10-27
I'm currently taking a course on Algorithm Analysis, and we're using this as our textbook. At first glance this book seems like a tome of information, and extremely thorough. However, once you start to read it you realize exactly how many errors and typos made it all the way to the Third Edition. On the bright side, the appendices cover discrete mathematics very well.
Here's two samples of the errors that have poked through:
pg. 7, has a grievous error in exchangesort:
for(i=0; i
<=1;i++)
That's obviously incorrect. It should be i
<=(n-1).
pg. 50, recursive binary search:
else if(x == array[mid])
Again, that should be else if(x
<= array[mid]), otherwise the code does the same thing as the previous if statement.
So frankly, skip this book. It contains errors that even a neophyte programmer ought to be able to catch.
For beginners? Sure........2003-10-07
I attend the school in which these two professors received their tenureship, and I must say, that this book is an absolute disappointment. They state that the reader "require no knowledge of calculus, only College Algebra" yet they go through the theories with little to no explanation, and assuming that the reader knows caculus. The book does a pitiful job of explaining chained matrix multiplication, graph theory, dynamic programming, Diskstra's algorithms, et al. It's NOT the best book, we're using the 3rd edition and it is just as bad as the 2nd edition. There are tons of typos and errors alike.
really good for beginners.......2003-01-04
In fact, it doesn't have C++ but C pseudocode; other than that, the book is marvelous in terms of being an introduction to the analysis of algorithms. The code is very clear and almost always has commentaries where they should be. Another point to remark is that the authors frequently encourage students to deepen in advanced concepts by presenting concrete bibliography. A very good reference book, I must say.
Dr. Jihad M. Jaam.......2002-07-21
A very good text[book] either for students or teachers.
Well written, and easy to understand. The concepts of
algorithms are well presented.
I like very much this textbook and so happy to discover it.
I teach algorithms for computer science students at Qatar
university, I used many different textbooks, but really
this is the one that I admire.
However, I regret to not have an instructor's manual for
it. I encourge deeply the authors to prepare one.
Average customer rating:
- A work of outstanding mathematical scholarship
- Excellent Reference
|
Algorithmic Number Theory, Vol. 1: Efficient Algorithms (Foundations of Computing)
Eric Bach , and
Jeffrey Shallit
Manufacturer: The MIT Press
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Prime Numbers: A Computational Perspective
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ASIN: 0262024055 |
Book Description
Algorithmic Number Theory provides a thorough introduction to the design and analysis of algorithms for problems from the theory of numbers. Although not an elementary textbook, it includes over 300 exercises with suggested solutions. Every theorem not proved in the text or left as an exercise has a reference in the notes section that appears at the end of each chapter. The bibliography contains over 1,750 citations to the literature. Finally, it successfully blends computational theory with practice by covering some of the practical aspects of algorithm implementations.
The subject of algorithmic number theory represents the marriage of number theory with the theory of computational complexity. It may be briefly defined as finding integer solutions to equations, or proving their non-existence, making efficient use of resources such as time and space. Implicit in this definition is the question of how to efficiently represent the objects in question on a computer. The problems of algorithmic number theory are important both for their intrinsic mathematical interest and their application to random number generation, codes for reliable and secure information transmission, computer algebra, and other areas.
Publisher's Note: Volume 2 was not written. Volume 1 is, therefore, a stand-alone publication.
Customer Reviews:
A work of outstanding mathematical scholarship.......2002-05-25
This book is a valuable reference -- a real work of mathematical scholarship concerning problems from elementary number theory, such as primality testing, square roots mod p, quadratic residues, polynomial factoring, and generation of random primes -- algorithms for which efficient solutions are known.
Related algorithms such as the lattice reduction algorithm of Lenstra, Lenstra, and Lovasz, and elliptic curve point counting over finite fields are not covered.
Three outstanding features of this book are:
1) The extensive chapter end notes that provide a comprehensive review of the history and state of the art for each topic addressed in the book. These notes are so detailed that they are like having a mini book within a book. Anyone doing research in the field would do well to own this book for this reason alone.
2) Exhaustive bibliography, all together there are over 1750 bibliographic entries.
3) Applications of the RH and ERH(Riemann Hypothesis and Extended Riemann Hypothesis). I know of no other single reference that covers the consequences of these conjectures being true in terms of primality testing, quadratic non-residue testing, primitive root finding and so on.
The algorithms are presented in pseudo code and practical implementation remarks are reserved for the notes section of each chapter.
Recommended for upper level undergraduates and all the way on up to faculty.
As a bonus the book is a real pleasure to view due to the excellent job done in the layout and typesetting.
I look forward to volume two which will focus on algorithms for intractable problems for which efficient (polynomial time) algorithms are NOT known such as factoring and the discrete log problem.
Excellent Reference.......1999-10-13
Bach and Shallit have done a wonderful job of preparing a survey of Number Theoretic Algorithms. After covering the basic mathematical material and complexity theory background, the book plunges in to discuss computation in (Z/(n)) and various algorithms in Finite Fields.
The part of the book that I like best are the last two chapters which deal with prime numbers and algorithms for primality testing. The authors have done an exhaustive survey of this area. Proofs of the correctness of the algorithms are wonderfully concise and lucid. The second volume [not published yet] will discuss problems for which efficient algorithms are currently unknown for example factoring, discrete log etc. The authors also promise coverage of the Adleman, Huang proof that Primes \in ZPP.
Exercises have been chosen carefully, and most of the solutions are available as an appendix (for the others references are given). Finally the bibliography is *huge* with close to 2000 citations. Overall an excellent book for reference and for a one stop introduction to the wonderful area of Algorithmic Number Theory.
Book Description
‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models.
From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks.
In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Book Description
Data stream algorithms as an active research agenda emerged only over the past few years, even though the concept of making few passes over the data for performing computations has been around since the early days of Automata Theory. The data stream agenda now pervades many branches of Computer Science including databases, networking, knowledge discovery and data mining, and hardware systems. Industry is in synch too, with Data Stream Management Systems (DSMSs) and special hardware to deal with data speeds. Even beyond Computer Science, data stream concerns are emerging in physics, atmospheric science and statistics. Data Streams: Algorithms and Applications focuses on the algorithmic foundations of data streaming. In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Data Streams: Algorithms and Applications surveys the emerging area of algorithms for processing data streams and associated applications. An extensive bibliography with over 200 entries points the reader to further resources for exploration.
Average customer rating:
- A difficult but worthwhile mathematics text
- Great book... some reviewers simply don't get it.
- smug math book
- Concrete Math is fun
- I wish every book were written like this!
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Concrete Mathematics: A Foundation for Computer Science (2nd Edition)
Ronald L. Graham ,
Donald E. Knuth , and
Oren Patashnik
Manufacturer: Addison-Wesley Professional
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ASIN: 0201558025 |
Customer Reviews:
A difficult but worthwhile mathematics text.......2007-06-25
This book's title can be misleading. I would say it is more of an advanced textbook on the mathematics that is a foundation for computer science than a foundational book on the mathematics of computer science. I think this misreading of the title and thus the book's content is what is behind much of the heartache that readers have when trying to tackle it. This book expands on the "Mathematical Preliminaries" portion of "The Art of Computer Programming" series of books by Knuth, and thus this book has a style much like that series of books. The book is complete and clear, but it is also densely packed with lots of theory and proofs and will require much effort and time to understand well. It is really not meant to be an applied mathematics textbook at all. I show the table of contents next. Note that there are exercises at the conclusion of each chapter with solutions in the back of the book. However, most of the exercises are not so simple that you can just glimpse at the solution and figure out how to get from A to B. I recommend it if you have the time. It can really bring out thoughts and the beauty of mathematics that you may not have considered before.
1. Recurrent Problems.
The Tower of Hanoi.
Lines in the Plane.
The Josephus Problem.
Exercises.
2. Sums.
Notation.
Sums and Recurrences.
Manipulation of Sums.
Multiple Sums.
General Methods.
Finite and Infinite Calculus.
Infinite Sums.
Exercises.
3. Integer Functions.
Floors and Ceilings.
Floor/Ceiling Applications.
Floor/Ceiling Recurrences.
'mod': The Binary Operation.
Floor/Ceiling Sums.
Exercises.
4. Number Theory.
Divisibility.
Factorial Factors.
Relative Primality.
'mod': The Congruence Relation.
Independent Residues.
Additional Applications.
Phi and Mu.
Exercises.
5. Binomial Coefficients.
Basic Identities.
Basic Practice.
Tricks of the Trade.
Generating Functions.
Hypergeometric Functions.
Hypergeometric Transformations.
Partial Hypergeometric Sums.
Mechanical Summation.
Exercises.
6. Special Numbers.
Stirling Numbers.
Eulerian Numbers.
Harmonic Numbers.
Harmonic Summation.
Bernoulli Numbers.
Fibonacci Numbers.
Continuants.
Exercises.
7. Generating Functions.
Domino Theory and Change.
Basic Maneuvers.
Solving Recurrences.
Special Generating Functions.
Convolutions.
Exponential Generating Functions.
Dirichlet Generating Functions.
Exercises.
8. Discrete Probability.
Definitions.
Mean and Variance.
Probability Generating Functions.
Flipping Coins.
Hashing.
Exercises.
9. Asymptotics.
A Hierarchy.
O Notation.
O Manipulation.
Two Asymptotic Tricks.
Euler's Summation Formula.
Final Summations.
Exercises.
A. Answers to Exercises.
B. Bibliography.
Great book... some reviewers simply don't get it........2007-06-20
I have the First Edition and came here to look into the Second Edition. There are several negative reviews and basically those folks have fundamental misunderstandings. So I'll add my review.
First, what kind of book is it? It is not an introductory-level math book with lots and lots of repetition. It is a book on hard math, done in a concise manner by brilliant teachers who assume students are very comfortable with calculus, probability, etc. You really cannot afford to skip around and dabble as if this were an introductory algebra course or something. (I'm not being elitist. I did not attend Stanford and don't consider myself a math genius and am not making this a "we versus the unwashed masses" issue, as I have really struggled with the material myself.)
Second, what is the book about? Several reviewers have theories on where the "Concrete" part of the title comes from, but the bottom line is that it's a book on the discrete math that you need to know for theoretical computer science. (For example, discrete calculus versus the continuous calculus we all learned in school.) Any Analysis of Algorithms course, for example, will confront you with recurrence equations and lots of discrete math.
Third, how is the book organized? At first, it appears rather disjoint. The authors have a sort of, "Hey, look at that flower," and "hey, look under this rock" kind of approach as you walk down a path but the path itself isn't really spelled out. None-the-less, the book does build step-by-step from examples of recurrence equations (Towers of Hanooi, Josephus) in Chapter 1, to Generating Functions in Chapter 7.
Perhaps they could have made the path more explicit, but I can't see how they'd organize it much differently. They could throw entire chapters into Appendices, but things build on each other in such a way that you'd simply have to skip around from the main chapters to the Appendix anyhow.
Fourth, what other books cover this material? I'm not well-qualified to talk about the entire universe of books, but I must say that the three Analysis of Algorithm books I have for my current class definitely give only the very basics of this material and really only present two possibilities: 1) fiddle around with the equation, possibly using a graphic representation, until you see a pattern and make a guess, then prove it by induction, or 2) if your algorithm is one specific class, plug some numbers into this 3-part formula and if one of the parts applies an answer will pop out for you. Concrete Math is gives you many powerful tools to solve such problems.
Fifth, what is the flavor of the book? The authors have an informal writing style -- outside of the very formal math and proofs -- and the book has marginal notes that were contributed by the "beta-tester students" as the book was being written.
Some reviewers have criticized the marginal notes, and I simply have to shake my head and be glad I don't have to work alongside them. Yes, many of the notes are puns or other humor, but those are a nice break from the heavy math. And many of the notes provide great hints and perspectives from students who are also learning the material. I wish all technical books had such notes, but only a Knuth could get a publisher to go to the trouble.
So that's my review. An excellent book that's very intense and covers a hard, very technical topic. It's like learning math from algebra to differential equations all over again in a different language, and perhaps the negative reviewers simply never understood this. If you're not a programmer who needs to rigorously analyze algorithms, skip the book unless you simply want to learn for the joy of learning.
smug math book.......2007-03-20
This is one of those math books where the authors enjoy making inexplicable leaps between equations that really don't follow in a smooth logical fashion one from another. They are terrible at explaining things. They like to boast that they are from Stanford, (subtext: implied: if you are not from Stanford you probably wouldn't understand it anyway, you poor pitiful low-income commoner). I absolutely hate the tone of this book. And the side comments in the margins are inane, weak and mostly irritating. I threw it aside in disgust and went back to Warren Weaver.
Concrete Math is fun.......2006-02-21
This book is great. It is the funnest math book I have worked with, and I appreciate the intensity of the mathematics -- something that is falling out of the norm in computer science. The book is also a great source of fantastic combinatorics.
I wish every book were written like this!.......2005-12-14
This book is perhaps one of the most beautifully written books I have ever read. All the proofs presented here are elegant. When reading the proofs in this book, you can feel that one sentence logically and smoothly follows from the previous sentence. This is partly because of the elegant and effective notations adopted by the authors. [Note: Donald Knuth, one of the authors, has been one of the biggest proponents of good mathematical notations. See his book titled "Mathematical Writing".]
Other reviewers have provided a summary of this book. So, I will only say that every computer scientist and combinatorialist should read at least chapters 1, 2, 5, 7, and 9. Chapter 5 is very highly recommended. Trust me: once you have mastered these chapters, you will be able to do things your colleagues just can't. Even just familiarizing yourself with the notations in this book will help you produce proofs that you probably won't be able to otherwise. [Great ideas are of course always important in every proof - but without good notations, you probably won't be able to come up with the ideas in the first place.]
There is pretty much nothing bad about this book that I am aware of. I will just say though that it takes a lot of time and effort to acquire mastery of the material. As for my own story, I started reading chapter 1 and 2 when I just got interested in discrete mathematics. It took me about 1/2 year (part time) to get through this. I came back to this book again when I took a course on "generatingfunctionology". I found that chapter 5 and 7 were indispensable. I was also forced to reread chapter 2 again because the lecturer, as most people do, just waived his hands when it comes to manipulating sums and binomial coefficients. However, all the effort that I put in paid off in the end as I could solve problems in the final exam which all my other friends could not.
In summary, I strongly recommend this book to every computer scientist and combinatorialist. I will finally remark that, if you are serious about learning concrete mathematics, you will probably find that generating functions pop up pretty much everywhere. To understand these beasts, I highly recommend Sedgewick and Flajolet's "Introduction to Analysis of Algorithms" and "Analytic Combinatorics" (not yet published, but next-to-final draft is available at Flajolet's web site), and Wilf's "Generatingfunctionology".
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Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design (Iee Circuits, Devices and Systems)
Patrick Gaydecki
Manufacturer: Instn Electrical Engineers
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ASIN: 0852964315 |
Book Description
An excellent introductory text, this book covers the basic theoretical, algorithmic and real-time aspects of digital signal processing (DSP). Detailed information is provided on off-line, real-time and DSP programming and the reader is effortlessly guided through advanced topics such as DSP hardware design, FIR and IIR filter design and difference equation manipulation. There are many practical examples illustrated throughout plus an accompanying CD which provides programs that demonstrate equations discussed in the text, enabling the reader to incorporate algorithms into their own DSP programs.
Customer Reviews:
Just the Best.......2006-07-18
This book is just fantastic - all the really hard stuff about DSP is explained clearly and simply, with loads of software snippets to show how the equations work. The book even shows how to build your own DSP system and program it. I always thought this stuff was for geniuses, but the book shows how mere mortals can do the same stuff. JUST GREAT!
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Algorithmic Foundation of Multi-Scale Spatial Representation
Zhilin Li
Manufacturer: CRC
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Cartographic Science: A Compendium of Map Projections, with Derivations
ASIN: 0849390729 |
Book Description
With the widespread use of GIS, multi-scale representation has become an important issue in the realm of spatial data handling. However, no book to date has systematically tackled the different aspects of this discipline. Emphasizing map generalization, Algorithmic Foundation of Multi-Scale Spatial Representation addresses the mathematical basis of multi-scale representation, specifically, the algorithmic foundation. Using easy-to-understand language, the author focuses on geometric transformations, with each chapter surveying a particular spatial feature. After an introduction to the essential operations required for geometric transformations as well as some mathematical and theoretical background, the book describes algorithms for a class of point features/clusters. It then examines algorithms for individual line features, such as the reduction of data points, smoothing (filtering), and scale-driven generalization, followed by a discussion of algorithms for a class of line features including contours, hydrographic (river) networks, and transportation networks. The author also addresses algorithms for individual area features, a class of area features, and various displacement operations. The final chapter briefly covers algorithms for 3-D surfaces and 3-D features. Providing a thorough treatment of low-level algorithms, Algorithmic Foundation of Multi-Scale Spatial Representation supplies the mathematical groundwork for multi-scale representations of spatial data.
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A book that fills a gap. .......2007-01-25
This book has a unique role in fullfilling one of the missing areas in Cartography. With a wide coverage, it is well suited for a textbook at undergraduate and graduate levels.
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