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| Author | : J. Hromkovic |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 280 |
| Release | : 2005-10-11 |
| Genre | : Computers |
| ISBN | : 3540279032 |
Systematically teaches key paradigmic algorithm design methods Provides a deep insight into randomization
| Author | : J. Hromkovic |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 280 |
| Release | : 2005-06-14 |
| Genre | : Computers |
| ISBN | : 3540239499 |
Systematically teaches key paradigmic algorithm design methods Provides a deep insight into randomization
| Author | : Rajeev Motwani |
| Publisher | : Cambridge University Press |
| Total Pages | : 496 |
| Release | : 1995-08-25 |
| Genre | : Computers |
| ISBN | : 1139643134 |
For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.
| Author | : Roberto Tempo |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 363 |
| Release | : 2012-10-21 |
| Genre | : Technology & Engineering |
| ISBN | : 1447146107 |
The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic uncertainty. The approach propounded by this text guarantees a reduction in the computational complexity of classical control algorithms and in the conservativeness of standard robust control techniques. The second edition has been thoroughly updated to reflect recent research and new applications with chapters on statistical learning theory, sequential methods for control and the scenario approach being completely rewritten. Features: · self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; · development of a novel paradigm for (convex and nonconvex) controller synthesis in the presence of uncertainty and in the context of randomized algorithms; · comprehensive treatment of multivariate sample generation techniques, including consideration of the difficulties involved in obtaining identically and independently distributed samples; · applications of randomized algorithms in various endeavours, such as PageRank computation for the Google Web search engine, unmanned aerial vehicle design (both new in the second edition), congestion control of high-speed communications networks and stability of quantized sampled-data systems. Randomized Algorithms for Analysis and Control of Uncertain Systems (second edition) is certain to interest academic researchers and graduate control students working in probabilistic, robust or optimal control methods and control engineers dealing with system uncertainties. The present book is a very timely contribution to the literature. I have no hesitation in asserting that it will remain a widely cited reference work for many years. M. Vidyasagar
| Author | : Sandeep Sen |
| Publisher | : Cambridge University Press |
| Total Pages | : 396 |
| Release | : 2019-05-23 |
| Genre | : Computers |
| ISBN | : 1108576893 |
The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.
| Author | : J. Hromkovic |
| Publisher | : Springer |
| Total Pages | : 277 |
| Release | : 2009-09-02 |
| Genre | : Computers |
| ISBN | : 9783540805427 |
Systematically teaches key paradigmic algorithm design methods Provides a deep insight into randomization
| Author | : Devdatt P. Dubhashi |
| Publisher | : Cambridge University Press |
| Total Pages | : 213 |
| Release | : 2009-06-15 |
| Genre | : Computers |
| ISBN | : 1139480995 |
Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff–Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff–Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.
| Author | : Michael Mitzenmacher |
| Publisher | : Cambridge University Press |
| Total Pages | : 372 |
| Release | : 2005-01-31 |
| Genre | : Computers |
| ISBN | : 9780521835404 |
Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This 2005 textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 189 |
| Release | : 1992-02-01 |
| Genre | : Mathematics |
| ISBN | : 0309047765 |
Some of the hardest computational problems have been successfully attacked through the use of probabilistic algorithms, which have an element of randomness to them. Concepts from the field of probability are also increasingly useful in analyzing the performance of algorithms, broadening our understanding beyond that provided by the worst-case or average-case analyses. This book surveys both of these emerging areas on the interface of the mathematical sciences and computer science. It is designed to attract new researchers to this area and provide them with enough background to begin explorations of their own.
| Author | : M. H. Alsuwaiyel |
| Publisher | : World Scientific |
| Total Pages | : 573 |
| Release | : 2016 |
| Genre | : Mathematics |
| ISBN | : 9814723657 |
"Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) the solution to the formulated problem. One can solve a problem on its own using ad hoc techniques or by following techniques that have produced efficient solutions to similar problems. This requires the understanding of various algorithm design techniques, how and when to use them to formulate solutions, and the context appropriate for each of them. Algorithms: Design Techniques and Analysis advocates the study of algorithm design by presenting the most useful techniques and illustrating them with numerous examples -- emphasizing on design techniques in problem solving rather than algorithms topics like searching and sorting. Algorithmic analysis in connection with example algorithms are explored in detail. Each technique or strategy is covered in its own chapter through numerous examples of problems and their algorithms. Readers will be equipped with problem solving tools needed in advanced courses or research in science and engineering."--Provided by publisher.
