Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will: Solve a particular coding problem or improve on the performance of an existing solution Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips Learn the expected performance of an algorithm, and the conditions it needs to perform at its best Discover the impact that similar design decisions have on different algorithms Learn advanced data structures to improve the efficiency of algorithms With Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.

When it comes to writing efficient code, every software professional needs to have an effective working knowledge of algorithms. In this practical book, author George Heineman (Algorithms in a Nutshell) provides concise and informative descriptions of key algorithms that improve coding in multiple languages. Software developers, testers, and maintainers will discover how algorithms solve computational problems creatively. Each chapter builds on earlier chapters through eye-catching visuals and a steady rollout of essential concepts, including an algorithm analysis to classify the performance of every algorithm presented in the book. At the end of each chapter, youâ??ll get to apply what youâ??ve learned to a novel challenge problemâ??simulating the experience you might find in a technical code interview. With this book, you will: Examine fundamental algorithms central to computer science and software engineering Learn common strategies for efficient problem solvingâ??such as divide and conquer, dynamic programming, and greedy approaches Analyze code to evaluate time complexity using big O notation Use existing Python libraries and data structures to solve problems using algorithms Understand the main steps of important algorithms

Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design.

When you have questions about C# 10 or .NET 6, this best-selling guide has the answers you need. C# is a language of unusual flexibility and breadth, and with its continual growth, there's always so much more to learn. In the tradition of O'Reilly's Nutshell guides, this thoroughly updated edition is simply the best one-volume reference to the C# language available today. Organized around concepts and use cases, this comprehensive and complete reference provides intermediate and advanced programmers with a concise map of C# and .NET that also plumbs significant depths. Get up to speed on C#, from syntax and variables to advanced topics such as pointers, closures, and patterns Dig deep into LINQ, with three chapters dedicated to the topic Explore concurrency and asynchrony, advanced threading, and parallel programming Work with .NET features, including regular expressions, networking, assemblies, spans, reflection, and cryptography

Graph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.

If you know basic high-school math, you can quickly learn and apply the core concepts of computer science with this concise, hands-on book. Led by a team of experts, you’ll quickly understand the difference between computer science and computer programming, and you’ll learn how algorithms help you solve computing problems. Each chapter builds on material introduced earlier in the book, so you can master one core building block before moving on to the next. You’ll explore fundamental topics such as loops, arrays, objects, and classes, using the easy-to-learn Ruby programming language. Then you’ll put everything together in the last chapter by programming a simple game of tic-tac-toe. Learn how to write algorithms to solve real-world problems Understand the basics of computer architecture Examine the basic tools of a programming language Explore sequential, conditional, and loop programming structures Understand how the array data structure organizes storage Use searching techniques and comparison-based sorting algorithms Learn about objects, including how to build your own Discover how objects can be created from other objects Manipulate files and use their data in your software

Presents a guide to the R computer language, covering such topics as the user interface, packages, syntax, objects, functions, object-oriented programming, data sets, lattice graphics, regression models, and bioconductor.

The new edition of this classic O’Reilly reference provides clear, detailed explanations of every feature in the C language and runtime library, including multithreading, type-generic macros, and library functions that are new in the 2011 C standard (C11). If you want to understand the effects of an unfamiliar function, and how the standard library requires it to behave, you’ll find it here, along with a typical example. Ideal for experienced C and C++ programmers, this book also includes popular tools in the GNU software collection. You’ll learn how to build C programs with GNU Make, compile executable programs from C source code, and test and debug your programs with the GNU debugger. In three sections, this authoritative book covers: C language concepts and language elements, with separate chapters on types, statements, pointers, memory management, I/O, and more The C standard library, including an overview of standard headers and a detailed function reference Basic C programming tools in the GNU software collection, with instructions on how use them with the Eclipse IDE

In this book, a variety of algoritbms are described that may be of interest to everyone who writes software for 3D-graphics. It is a book that haB been written for programmers at an intermediate level as well aB for experienced software engineers who simply want to have some particular functions at their disposal, without having to think too much about details like special cases or optimization for speed. The programming language we use is C, and that has many advantages, because it makes the code both portable and efficient. Nevertheless, it should be possible to adapt the ideas to other high-level programming languages. The reader should have a reasonable knowledge of C, because sophisticated pro grams with economical storage household and fast sections cannot be written without the use of pointers. You will find that in the long run it is just aB easy to work with pointer variables as with multiple arrays . .Aß the title of the book implies, we will not deal with algorithms that are very computation-intensive such as ray tracing or the radiosity method. Furthermore, objects will always be (closed or not closed) polyhedra, which consist of a certain number of polygons.

This much-needed book on the design of algorithms and data structures for text processing emphasizes both theoretical foundations and practical applications. It is intended to serve both as a textbook for courses on algorithm design, especially those related to text processing, and as a reference for computer science professionals. The work takes a unique approach, one that goes more deeply into its topic than other more general books. It contains both classical algorithms and recent results of research on the subject. The book is the first text to contain a collection of a wide range of text algorithms, many of them quite new and appearing here for the first time. Other algorithms, while known by reputation, have never been published in the journal literature. Two such important algorithms are those of Karp, Miller and Rosenberg, and that of Weiner. Here they are presented together for the fist time. The core of the book is the material on suffix trees and subword graphs, applications of these data structures, new approaches to time-space optimal string-matching, and text compression. Also covered are basic parallel algorithms for text problems. Applications of all these algorithms are given for problems involving data retrieval systems, treatment of natural languages, investigation of genomes, data compression software, and text processing tools. From the theoretical point of view. the book is a goldmine of paradigms for the development of efficient algorithms, providing the necessary foundation to creating practical software dealing with sequences. A crucial point in the authors' approach is the development of a methodology for presenting text algorithms so they can be fully understood. Throughout, the book emphasizes the efficiency of algorithms, holding that the essence of their usefulness depends on it. This is especially important since the algorithms described here will find application in "Big Science" areas like molecular sequence analysis where the explosive growth of data has caused problems for the current generation of software. Finally, with its development of theoretical background, the book can be considered as a mathematical foundation for the analysis and production of text processing algorithms.