Structures and Algorithms: Annotated Reference with Examples, First Edition

Provides all developers with a core understanding of algorithms that operate on various common, and uncommon data structures.

**Tag(s):**
Algorithms and Data Structures

**Publication date**: 31 Dec 2008

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
112 pages

**Views**: 13,280

Structures and Algorithms: Annotated Reference with Examples, First Edition

Provides all developers with a core understanding of algorithms that operate on various common, and uncommon data structures.

Excerpts from the Preface:

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Granville Barnet wrote: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 off 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.

Granville Barnet wrote: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.

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