Enter the captivating world of Mathematics and Computing with "Introduction to Mathematics for Computing: Algorithms and Data Structures." This comprehensive guide is designed for non-technical enthusiasts, providing an accessible and engaging introduction to essential mathematical concepts for computing. Dive into six insightful chapters that introduce you to the foundations of mathematical structures in computing, discrete mathematics and algorithms, linear algebra and calculus, probability and statistics, optimisation, and Boolean algebra. Explore sets, sequences, functions, graphs, counting principles, and more. Learn about data structures, algorithms, and optimisation techniques used in computing. The book's practice questions, exercises, and projects reinforce the concepts learned, ensuring a solid understanding of these essential topics. Written in accessible and straightforward language, "Introduction to Mathematics for Computing: Algorithms and Data Structures" is the perfect resource for anyone eager to explore the exciting world of Mathematics and Computing. Start your journey today!

Each number is the catalogue of a specific school or college of the University.

This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application. This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises.

Thoroughly revised for a one-semester course, this well-known and highly regarded book is an outstanding text for undergraduate discrete mathematics. It has been updated with new or extended discussions of order notation, generating functions, chaos, aspects of statistics, and computational biology. Written in a lively, clear style, the book is unique in its emphasis on algorithmics and the inductive and recursive paradigms as central mathematical themes. It includes a broad variety of applications, not just to mathematics and computer science, but to natural and social science as well.

This book constitutes the refereed proceedings of the 12th Algorithms and Data Structures Symposium, WADS 2011, held in New York, NY, USA, in August 2011. The Algorithms and Data Structures Symposium - WADS (formerly "Workshop on Algorithms and Data Structures") is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. The 59 revised full papers presented in this volume were carefully reviewed and selected from 141 submissions. The papers present original research on the theory and application of algorithms and data structures in all areas, including combinatorics, computational geometry, databases, graphics, parallel and distributed computing.

This volume contains three keynote papers and 51 technical papers from contributors around the world on topics in the research and development of database systems, such as Data Modelling, Object-Oriented Databases, Active Databases, Data Mining, Heterogeneous Databases, Distributed Databases, Parallel Query Processing, Multi-Media Databases, Transaction Management Systems, Document Databases, Temporal Databases, Deductive Databases, User Interface, and Advanced Database Applications.

This practical, applications-oriented book describes essential tools for efficiently handling massive amounts of data.