{"title":"Algorithms and data structures Books","description":"","products":[{"product_id":"the-master-algorithm-9780141979243","title":"The Master Algorithm","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e''Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be'' Walter Isaacson, author of \u003c\/b\u003e\u003ci\u003e\u003cb\u003eSteve Jobs\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/i\u003eSociety is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In \u003ci\u003eThe Master Algorithm\u003c\/i\u003e, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find ''The Master Algorithm'' - a universal learner capable of deriving all knowledge from data.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003ePedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be -- Walter Isaacson, author of Steve Jobs and The Innovators\u003cbr\u003eMachine learning is a fascinating world never before glimpsed by outsiders. Pedro Domingos initiates you to the mysterious languages spoken by its five tribes, and invites you to join in his plan to unite them, creating the most powerful technology our civilization has ever seen -- Sebastian Seung, Professor, Princeton, and author of 'Connectome'\u003cbr\u003eMachine learning, known in commercial use as predictive analytics, is changing the world. This riveting, far-reaching, and inspiring book introduces the deep scientific concepts to even non-technical readers, and yet also satisfies experts with a fresh, profound perspective that reveals the most promising research directions. It's a rare gem indeed -- Eric Siegel, founder of Predictive Analytics World and author of 'Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die'\u003cbr\u003eWith terms like 'Machine Learning' and 'Big Data' regularly making headlines, there is no shortage of hype-filled business books on the subject. There are also textbooks that are too technical to be accessible. For those in the middle-from executives to college students-this is the ideal book, showing how and why things really work without the heavy math. Unlike other books that proclaim a bright future, this one actually gives you what you need to understand the changes that are coming -- Peter Norvig, Director of Research, Google and coauthor of 'Artificial Intelligence: A Modern Approach'\u003cbr\u003e[The Master Algorithm] does a good job of examining the field's five main techniques...The subject is meaty and the author ... has a knack for introducing concepts at the right moment * Economist *\u003cbr\u003eMachine learning is the single most transformative technology that will shape our lives over the next fifteen years. This book is a must-read-a bold and beautifully written new framework for looking into the future -- Geoffrey Moore, author of 'Crossing the Chasm'\u003cbr\u003eThis is an incredibly important and useful book. Machine learning is already critical to your life and work, and will only become more so. Finally, Pedro Domingos has written about it in a clear and understandable fashion -- Thomas H. Davenport, Distinguished Professor, Babson College and author of 'Competing on Analytics and Big Data @ Work'\u003cbr\u003eStarting with the audacious claim that all knowledge can be derived from data by a single 'master algorithm,' Domingos takes the reader on a fast-paced journey through the brave new world of machine learning. Writing breezily but with deep authority, Domingos is the perfect tour guide from whom you will learn everything you need to know about this exciting field, and a surprising amount about science and philosophy as well -- Duncan Watts, Principal Researcher, Microsoft Research, and author of 'Six Degrees and Everything Is Obvious *Once You Know the Answer'\u003cbr\u003eA delightful book by one of the leading experts in the field. If you wonder how AI will change your life, read this book -- Sebastian Thrun, Research Professor, Stanford, Google Fellow and Inventor of the Self-Driving Car\u003cbr\u003eAn exhilarating venture into groundbreaking computer science * Booklist (starred review) *\u003cbr\u003eDomingos writes with verve and passion, and the book has a strong narrative * New Scientist *\u003cbr\u003ePedro Domingos is a man with a quest, and a hypothesis, which is likely - one day - to change the world . . . Domingos is a genial and amusing guide . . . This is a highly inclusive book, aimed at a wide range of readers from the merely curious to those who might be interested in pursuing a career in the field . . . Descriptions and discussions are presented with a commendable lack of jargon and the examples are clear and accessible -- John Gilbey * Times Higher Education *\u003cbr\u003eWonderfully erudite, humorous, and easy to read. * KDNuggets *\u003cbr\u003e\u003ci\u003eThe Master Algorithm\u003c\/i\u003e does a good job of examining the field's five main techniques...The subject is meaty and the author...has a knack for introducing concepts at the right moment * Economist *","brand":"Penguin Books Ltd","offers":[{"title":"Default Title","offer_id":48732498886999,"sku":"9780141979243","price":10.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780141979243.jpg?v=1719997150"},{"product_id":"machine-learning-for-signal-processing-9780198714934","title":"Machine Learning for Signal Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDescribes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis book provides an excellent pathway for gaining first-class expertise in machine learning. It provides both the technical background that explains why certain approaches, but not others, are best practice in real world problems, and a framework for how to think about and approach new problems. I highly recommend it for people with a signal processing background who are seeking to become an expert in machine learning. * Alex 'Sandy' Pentland, Toshiba Professor of Media Arts and Sciences, Massachusetts Institute of Technology, *\u003cbr\u003eOver the past decade in signal processing, machine learning has gone from a disparate research field known only to people working on topics such as speech and image processing, to permeating all aspects of it. With this book, Prof. Little has taken an important step in unifying machine learning and signal processing. As a whole, this book covers many topics, new and old, that are important in their own right and equips the reader with a broader perspective than traditional signal processing textbooks. In particular, I would highlight the combination of statistical modeling, convex optimization, and graphs as particularly potent. Machine learning and signal processing are no longer separate, and there is no doubt in my mind that this is the way to teach signal processing in the future. * Mads Christensen, Full Professor in Audio Processing, Aalborg University, Denmark, *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Mathematical Foundations 2: Optimization 3: Random Sampling 4: Statistical Modelling and Inference 5: Probabalistic Graphical Models 6: Statistical Machine Learning 7: Linear-Gaussian Systems and Signal Processing 8: Discrete Signals: Sampling, Quantization and Coding 9: Nonlinear and Non-Gaussian Signal Processing 10: Nonparametric Bayesian Machine Learning and Signal Processing","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732768600407,"sku":"9780198714934","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"poems-that-solve-puzzles-9780198853732","title":"Poems That Solve Puzzles","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAlgorithms are the hidden methods that computers apply to process information and make decisions. Nowadays, our lives are run by algorithms. They determine what news we see. They influence which products we buy. They suggest our dating partners. They may even be determining the outcome of national elections. They are creating, and destroying, entire industries. Despite mounting concerns, few know what algorithms are, how they work, or who created them.Poems that Solve Puzzles tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and inspirational events behind the genesis of the world''s most important algorithms. Professor Chris Bleakley recounts tales of ancient lost inscriptions, Victorian steam-driven contraptions, top secret military projects, penniless academics, hippy dreamers, tech billionaires, superhuman artificial intelligences, cryptocurrencies, and quantum computing. Along the way, the book explains, with the aid of clear examples and illustrations, how the most influential algorithms work.Compelling and impactful, Poems that Solve Puzzles tells the story of how algorithms came to revolutionise our world.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003ePoems that Solve Puzzles is a thorough investigation into the history of algorithms...It is an enjoyable read for anyone curious about how algorithms developed and were implemented throughout history.' * Notices of the American Mathematical Society *\u003cbr\u003ePoems that Solve Puzzles: The History and Science of Algorithms is an informative and entertaining book. It is appropriate for a wide swath of readers, from people who are interested in learning about what \"blockchain\" is without having to do any math to students and instructors in the mathematical sciences who need more examples of how these academic topics make important contributions to the technologically complex world we live in. * Ron Buckmire, Occidental College, Mathematical Association of America *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e0: Introduction 1: Ancient Algorithms 2: Ever Expanding Circles 3: Computer Dreams 4: Weather Forecasts 5: Artificial Intelligence Emerges 6: Needles in Haystacks 7: The Internet 8: Googling the Web 9: Facebook and Friends 10: America's Favourite Quiz Show 11: Mimicking the Brain 12: Superhuman Intelligence 13: Next Steps","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732814868823,"sku":"9780198853732","price":31.34,"currency_code":"GBP","in_stock":false}]},{"product_id":"datadriven-modeling-scientific-computation-9780199660346","title":"DataDriven Modeling  Scientific Computation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCombining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThe book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science * John Francis, University of Worcester *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eI BASIC COMPUTATIONS AND VISUALIZATION; II DIFFERENTIAL AND PARTIAL DIFFERENTIAL EQUATIONS; III COMPUTATIONAL METHODS FOR DATA ANALYSIS; IV SCIENTIFIC APPLICATIONS","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732877422935,"sku":"9780199660346","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"realworld-algorithms-a-beginners-guide-9780262035705","title":"RealWorld Algorithms A Beginners Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAn introduction to algorithms for readers with no background in advanced mathematics or computer science, emphasizing examples and real-world problems.\u003c\/b\u003e\u003cp\u003eAlgorithms are what we do in order not to have to do something. Algorithms consist of instructions to carry out tasks—usually dull, repetitive ones. Starting from simple building blocks, computer algorithms enable machines to recognize and produce speech, translate texts, categorize and summarize documents, describe images, and predict the weather. A task that would take hours can be completed in virtually no time by using a few lines of code in a modern scripting program. This book offers an introduction to algorithms through the real-world problems they solve. The algorithms are presented in pseudocode and can readily be implemented in a computer language.\u003c\/p\u003e\u003cp\u003eThe book presents algorithms simply and accessibly, without overwhelming readers or insulting their intelligence. Readers should be comfortable with mathematica\u003c\/p\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733446275415,"sku":"9780262035705","price":40.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262035705.jpg?v=1720000112"},{"product_id":"trading-at-the-speed-of-light-9780691217789","title":"Trading at the Speed of Light","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Winner of the Bronze Medal in Business Technology, Axiom Business Book Awards\"\u003cbr\u003e\"I loved this book. . . . \u003ci\u003eTrading at the Speed of Light\u003c\/i\u003e is an amazing, detailed account of why material reality matters for virtual outcomes, and conversely, in the financial markets. Everybody with the slightest interest in modern finance should read it.\"\u003cb\u003e---Diane Coyle, \u003ci\u003eEnlightened Economist\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48735982715223,"sku":"9780691217789","price":19.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691217789.jpg?v=1723810428"},{"product_id":"the-inglorious-years-9780691206158","title":"The Inglorious Years","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"A welcome addition to the growing literature on the digital economy and change.\" * Choice *\u003cbr\u003e\"Stimulating.\" * Paradigm Explorer *","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48735995822423,"sku":"9780691206158","price":27.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691206158.jpg?v=1723810437"},{"product_id":"discrete-quantum-walks-on-graphs-and-digraphs-9781009261685","title":"Discrete Quantum Walks on Graphs and Digraphs","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDiscrete quantum walks are quantum analogues of classical random walks. They are an important tool in quantum computing and a number of algorithms can be viewed as discrete quantum walks, in particular Grover''s search algorithm. These walks are constructed on an underlying graph, and so there is a relation between properties of walks and properties of the graph. This book studies the mathematical problems that arise from this connection, and the different classes of walks that arise. Written at a level suitable for graduate students in mathematics, the only prerequisites are linear algebra and basic graph theory; no prior knowledge of physics is required. The text serves as an introduction to this important and rapidly developing area for mathematicians and as a detailed reference for computer scientists and physicists working on quantum information theory.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; 1. Grover search; 2. Two reflections; 3. Applications; 4. Averaging: 5. Covers and embeddings; 6. Vertex-face walks; 7. Shunts; 8. 1-Dimensional walks; References; Glossary; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738023407959,"sku":"9781009261685","price":60.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"probabilistic-numerics-9781107163447","title":"Probabilistic Numerics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProbabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters'' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Computational methods for solving numerical problems lie at the heart of many of the technological advances in science and engineering over the last five decades, and underpin fields as diverse as artificial intelligence, climate modelling, and epidemiology. This impressive text rethinks numerical problems through the lens of probabilistic inference and decision making. This fresh perspective opens up a new chapter in this field, and suggests new and highly efficient methods. A landmark achievement!' Zoubin Ghahramani, University of Cambridge\u003cbr\u003e'This beautiful book is both timely and important with deep roots in powerful early exposition in numerical analysis. In this stunning and comprehensive new book, early developments from Kac and Larkin have been comprehensively built upon, formalised and extended by including modern day machine learning, numerical analysis and the formal Bayesian statistical methodology. Probabilistic Numerical methodology is of enormous importance for this age of data-centric science and Hennig, Osborne and Kersting are to be congratulated in providing us with this definitive volume.' Mark Girolami, University of Cambridge and The Alan Turing Institute\u003cbr\u003e'Numerical analysis is at the very heart of digital computing: every result of a computation on a digital computer is a only finite-precision representation of the true mathematical quantity where the precision is the tradeoff between computation time and accuracy. This book presents an in-depth overview of both the past and present of the newly emerging area of probabilistic numerics, where recent advances in probabilistic machine learning are used to develop principled improvements which are both faster and more accurate than classical numerical analysis algorithms. A must-read for every algorithm developer and practitioner in optimization!' Ralf Herbrich, Hasso Plattner Institute\u003cbr\u003e'Probabilistic Numerics spans from the intellectual fireworks of the dawn of a new field to its practical algorithmic consequences. It is precise but accessible and rich in wide-ranging, principled examples. This convergence of ideas from diverse fields in lucid style is the very fabric of good science.' Carl Edward Rasmussen, University of Cambridge\u003cbr\u003e'An important read for anyone who has thought about uncertainty in numerical methods; an essential read for anyone who hasn't …' John Cunningham, Columbia University\u003cbr\u003e'This is a rare example of a textbook that essentially founds a new field, re-casting numerics on stronger, more general foundations. A tour de force.' David Duvenaud, University of Toronto\u003cbr\u003e'The idea of applying probabilistic inference to the problem of numerical analysis must appear bold, possibly outrageous, even to an entrenched Bayesian statistician. Many in machine learning are now familiar with the application of Bayesian methods to problems that involve randomness, say, the estimation of quantities from noisy data. But to apply the 'calculus of uncertainty' to unknown mathematical facts, where the uncertainty arises only from our lack of knowledge, opens up a universe of new possibilities. This elegant idea is at the core of Probabilistic Numerics, and the authors succeed in demonstrating its potential to transform the way we think about computation itself. And that's not even considering what would happen if we were to apply probabilistic numerics to the numerical problems that arise from probabilistic numerics itself!' Thore Graepel, Senior Vice President, Altos Labs\u003cbr\u003e'… the machine learning background of the authors comes through clearly in the book … I thoroughly recommend it.' Chris J. Oates, SIAM Review\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction; 1. Mathematical background; 2. Integration; 3. Linear algebra; 4. Local optimisation; 5. Global optimisation; 6. Solving ordinary differential equations; 7. The frontier; Solutions to exercises; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738239676759,"sku":"9781107163447","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107163447.jpg?v=1723811850"},{"product_id":"game-theory-basics-9781108824231","title":"Game Theory Basics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eGame theory is the science of interaction. This textbook, derived from courses taught by the author and developed over several years, is a comprehensive, straightforward introduction to the mathematics of non-cooperative games. It teaches what every game theorist should know: the important ideas and results on strategies, game trees, utility theory, imperfect information, and Nash equilibrium. The proofs of these results, in particular existence of an equilibrium via fixed points, and an elegant direct proof of the minimax theorem for zero-sum games, are presented in a self-contained, accessible way. This is complemented by chapters on combinatorial games like Go; and, it has introductions to algorithmic game theory, traffic games, and the geometry of two-player games. This detailed and lively text requires minimal mathematical background and includes many examples, exercises, and pictures. It is suitable for self-study or introductory courses in mathematics, computer science, or econo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This looks like a fine introduction to game theory, inter alia emphasizing methods for computing equilibria, and mathematical aspects in general. Especially worthy of note is the chapter devoted to correlated equilibria, a topic of central importance not normally covered in introductory texts.' Robert Aumann, The Hebrew University of Jerusalem\u003cbr\u003e'This book is a delightful adventure into the mathematics of game theory. Without any heavy apparatus, it lets us into the secrets of a whole range of exciting results that are usually thought too advanced for the common herd. It is not only undergraduate students who will benefit from reading this book. Professional game theorists will find it very useful too.' Ken Binmore, University College London\u003cbr\u003e'Bernhard von Stengel's book will enable students to become intimately familiar with game theoretic reasoning, which is mathematical by nature. The text comes at the right time: Game theory has become so popular in economics and political science that teachers could be tempted to put the cart before the horse. Here, the basic noncooperative game models are studied gradually and thoroughly, in a unified way, while providing the algorithms that can be used to solve interactive decision problems.' Françoise Forges, Université Paris-Dauphine\u003cbr\u003e'This is a rather reader-friendly, engaging, and polished superior creation. It illustrates, explains, motivates every definition, theorem, proof. Interesting and unique choice of topics, such as a delightful introductory chapter on combinatorial games. Highly recommended.' Aviezri Fraenkel, Weizmann Institute of Science, Israel\u003cbr\u003e'A masterful presentation of mathematical game theory in all its beauty and elegance, from basic notions to advanced techniques. It fills the gaps left by the many textbooks that cover concepts and applications, but devote only the bare minimum to the mathematical tools and insights, without which game theory would not have become the success it is today.' Sergiu Hart, The Hebrew University of Jerusalem\u003cbr\u003e'Game Theory is the child of mathematicians, as this textbook demonstrates through self-contained, elegant proofs of all seminal Theorems. The lively and rigorous exposition of carefully selected models, such as bargaining, combinatorial and congestion games (the latter two rarely the stuff of textbooks) explains its success far beyond mathematics. To reach deep results on both sides of the theory, Bernhard von Stengel's marvellous learning tool uses uncompromising, yet accessible mathematics and chooses examples to maximal effect.' Hervé Moulin, University of Glasgow\u003cbr\u003e'This will become a classic textbook on non-cooperative game theory. It is very useful for mathematicians, computer scientists, and economic theorists. Each chapter has a clear learning structure, with motivating examples and a central main theorem. The author's long teaching experience and expertise in game theory is apparent on every page.' Abraham Neyman, The Hebrew University of Jerusalem\u003cbr\u003e'Attractively covers of a lot of important material, in particular for students of mathematics and computer science.' Eva Tardos, Cornell University\u003cbr\u003e'This book is a gem. The presentation is clear and well structured, often with nice geometric illustrations. It moves step by step from basics to powerful concepts, methods and results. It is ideal for students of mathematics, computer science and economics who are curious about what game theory is and how it can be used.' Jörgen Weibull, Stockholm School of Economics\u003cbr\u003e'This excellent text develops with clarity and precision the basic concepts and mathematical tools of game theory, enhanced by well-motivated examples, exercises, and practical applications.' Robert Wilson, Stanford University\u003cbr\u003e'An exceptionally lucid introduction to the fundamentals of game theory, enlivened by examples that are sure to captivate students.' Peyton Young, University of Oxford\u003cbr\u003e'This is a rigorous, yet accessible introduction to mathematical non-cooperative game theory. In addition to the coverage of the basic concepts and results, it includes special and advanced topics and applications usually not contained in game theory textbooks, such as combinatorial games, congestion games and inspection games. The special emphasis on algorithmic and computational techniques make this textbook, just like its author, a valuable bridge between game theory and computer sciences.' Shmuel Zamir, The Hebrew University of Jerusalem\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Nim and Combinatorial Games; 2. Congestion Games; 3. Games in Strategic Form; 4. Game Trees with Perfect Information; 5. Expected Utility; 6. Mixed Equilibrium; 7. Brouwer's Fixed-Point Theorem; 8. Zero-Sum Games; 9. Geometry of Equilibria in Bimatrix Games; 10. Game Trees with Imperfect Information; 11. Bargaining; 12. Correlated Equilibrium.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738337325399,"sku":"9781108824231","price":35.14,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108824231.jpg?v=1723811944"},{"product_id":"control-systems-and-reinforcement-learning-9781316511961","title":"Control Systems and Reinforcement Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of ''deep'' or ''Q'', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Control Systems and Reinforcement Learning is a densely packed book with a vivid, conversational style. It speaks both to computer scientists interested in learning about the tools and techniques of control engineers and to control engineers who want to learn about the unique challenges posed by reinforcement learning and how to address these challenges. The author, a world-class researcher in control and probability theory, is not afraid of strong and perhaps controversial opinions, making the book entertaining and attractive for open-minded readers. Everyone interested in the \"why\" and \"how\" of RL will use this gem of a book for many years to come.' Csaba Szepesvári, Canada CIFAR AI Chair, University of Alberta, and Head of the Foundations Team at DeepMind\u003cbr\u003e'This book is a wild ride, from the elements of control through to bleeding-edge topics in reinforcement learning. Aimed at graduate students and very good undergraduates who are willing to invest some effort, the book is a lively read and an important contribution.' Shane G. Henderson, Charles W. Lake, Jr. Chair in Productivity, Cornell University\u003cbr\u003e'Reinforcement learning, now the de facto workhorse powering most AI-based algorithms, has deep connections with optimal control and dynamic programing. Meyn explores these connections in a marvelous manner and uses them to develop fast, reliable iterative algorithms for solving RL problems. This excellent, timely book from a leading expert on stochastic optimal control and approximation theory is a must-read for all practitioners in this active research area.' Panagiotis Tsiotras, David and Andrew Lewis Chair and Professor, Guggenheim School of Aerospace Engineering, Georgia Institute of Technology\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; Part I. Fundamentals Without Noise: 2. Control crash course; 3. Optimal control; 4. ODE methods for algorithm design; 5. Value function approximations; Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains; 7. Stochastic control; 8. Stochastic approximation; 9. Temporal difference methods; 10. Setting the stage, return of the actors; A. Mathematical background; B. Markov decision processes; C. Partial observations and belief states; References; Glossary of Symbols and Acronyms; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738559852887,"sku":"9781316511961","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316511961.jpg?v=1720049468"},{"product_id":"expert-oracle-rac-performance-diagnostics-and-tuning-9781430267096","title":"Expert Oracle RAC Performance Diagnostics and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eExpert Oracle RAC Performance Diagnostics and Tuning\u003c\/em\u003e provides comprehensive coverage of the features, technology and principles for testing and tuning RAC databases. The book takes a deep look at optimizing RAC databases by following a methodical approach based on scientific analysis rather than using a speculative approach, twisting and turning knobs and gambling on the system.\u003c\/p\u003e\u003cp\u003eThe book starts with the basic concepts of tuning methodology, capacity planning, and architecture. Author \u003cstrong\u003eMurali Vallath\u003c\/strong\u003e then dissects the various tiers of the testing implementation, including the operating system, the network, the application, the storage, the instance, the database, and the grid infrastructure. He also introduces tools for performance optimization and thoroughly covers each aspect of the tuning process, using many real-world examples, analyses, and solutions from the field that provide you with a solid, practical, and replicable approach to tuning a RAC en\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Methodology\u003c\/p\u003e\u003cp\u003e2. Capacity Planning and Architecture\u003c\/p\u003e\u003cp\u003e3. Testing for Availability\u003c\/p\u003e\u003cp\u003e4. Testing for Scalability\u003c\/p\u003e\u003cp\u003e5. Real Application Testing\u003c\/p\u003e\u003cp\u003e6. Tools and Utilities\u003c\/p\u003e\u003cp\u003e7. SQL Tuning\u003c\/p\u003e\u003cp\u003e8. Parallel Query Tuning\u003c\/p\u003e\u003cp\u003e9. Tuning the Database\u003c\/p\u003e\u003cp\u003e10. Tuning Recovery\u003c\/p\u003e\u003cp\u003e11. Tuning Oracle Net\u003c\/p\u003e\u003cp\u003e12. Tuning Storage Subsystem\u003c\/p\u003e\u003cp\u003e13. Tuning Global Cache\u003c\/p\u003e\u003cp\u003e14. Tuning the Cluster Interconnect\u003c\/p\u003e\u003cp\u003e15. Optimization of Distributed Workload\u003c\/p\u003e\u003cp\u003e16. Tuning the Oracle Clusterware\u003c\/p\u003e\u003cp\u003e17. Enqueues, Waits and Latches\u003c\/p\u003e\u003cp\u003e18. Problem Diagnostics\u003c\/p\u003e\u003cp\u003eA. The SQL Scripts Used in This Book\u003c\/p\u003e\u003cp\u003eBibliography\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48739150987607,"sku":"9781430267096","price":54.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"complete-guide-to-open-source-big-data-stack-9781484221488","title":"Complete Guide to Open Source Big Data Stack","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book describes the creation of an actual generic open source big data stack, which is an integrated stack of big data components--each of which serves a specific function like storage, resource management, or queueing. Each component has a big data heritage and community to support it. It can support big data in that it is able to scale, and it is a distributed and robust system.\u003c\/p\u003e\u003cp\u003eIn the\u003ci\u003e Complete Guide to Open Source Big Data Stack, \u003c\/i\u003eMike Frampton begins by creating a private cloud and then by installing and examining Apache Brooklyn. After that he will use each chapter to introduce one piece of the big data stacksharing how to source the software and then how to install it. He will then show how it works by simple example. Step by step and chapter by chapter, Frampton will create a real big data stack. \u003c\/p\u003eThe goal of this book is to show how a big data stack might be created and what components might be used. It attempts to do this with currently available Apa\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: The Big Data Stack Overview.- Chapter 2: Cloud Storage.- Chapter 3: Apache Brooklyn.- Chapter 4: Apache Mesos.- Chapter 5: Stack Storage Options.- Chapter 6: Processing.- Chapter 7: Streaming.- Chapter 8: Frameworks.- Chapter 9: Visualization.- Chapter 10: The Big Data Stack.- \u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739662528855,"sku":"9781484221488","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484221488.jpg?v=1720052848"},{"product_id":"the-ultimate-guide-to-functions-in-power-query-9781484297537","title":"The Ultimate Guide to Functions in Power Query","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is a complete guide to using functions in Power Query and is designed to help users of all skill levels learn and master its various functions.   The Ultimate Guide to Functions in Power Query begins with an introduction to Power Query and an overview of the different types of functions available, along with detailed explanations of how to use each of them. You'll see how to leverage power functions to process and transform large datasets from various sources and learn advanced techniques such as creating custom functions and using conditional statements. The book also covers best practices for using functions, including tips on how to optimize query performance and troubleshoot common errors. Using practical example applications, Author Omid Motamedisedeh demonstrates how to optimize your data processing workflows, saving time and boosting productivity.   By the end of the book, readers will have a deep understanding of Power Query functions and be ableto apply their knowled\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: Introduction to Power Query.- Chapter 2: Data Types.- Chapter 3: Number Functions.- Chapter 4: Text Functions.- Chapter 5: Date and Time Functions.- Chapter 6: List Functions.- Chapter 7: Record Functions.- Chapter 8: Table Functions.- Chapter 9: Extracting from Data Sources.- Chapter 10: Other Functions.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739669377367,"sku":"9781484297537","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484297537.jpg?v=1723812254"},{"product_id":"modern-x86-assembly-language-programming-9781484296028","title":"Modern X86 Assembly Language Programming","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1  X86-Core Architecture.- Chapter 2  X86-64 Core Programming (Part 1).- Chapter 3  X86-64 Core Programming (Part 2).- Chapter 4  X86-64 Core Programming (Part 3).- Chapter 5  AVX Programming - Scalar Floating-Point.- Chapter 6 Run-Time Calling Conventions.- Chapter 7 Introduction to X86-AVX SIMD Programming.- Chapter 8  AVX Programming  Packed Integers.- Chapter 9  AVX Programming  Packed Floating Point.- Chapter 10  AVX2 Programming  Packed Integers.- Chapter 11  AVX2 Programming  Packed Floating Point (Part 1).- Chapter 12  AVX2 Programming  Packed Floating Point (Part 2).- Chapter 13  AVX-512 Programming  Packed Integers.- Chapter 14  AVX-512 Programming  Packed Floating Point (Part 1).- Chapter 15  AVX-512 Programming  Packed Floating Point (Part 2).- Chapter 16  Advanced Assembly Language Programming.-  Chapter 17  Assembly Language Optimization and Development Guidelines.  Appendix A  Source Co\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eChapter 1 – X86-Core Architecture \u003c\/b\u003e\u003cbr\u003eChapter Goal: Explains the core architecture of an x86-64 processor. Topics discussed include fundamental data types, registers, status flags, memory addressing modes, and other important architectural subjects. Understanding of this material is necessary for the reader to successfully comprehend the book’s subsequent chapters.Historical overviewData typesFundamental data typesNumerical data typesSIMD data typesMiscellaneous data typesStringsBit fields and bit stringsX86-64 processor internal architectureOverviewGeneral-purpose registersInstruction pointerRFLAGSFloating-point and SIMD registersMXCSR RegisterInstruction operandsMemory addressingCondition codesDifferences between x86-32 and x86-64\u003cbr\u003e\u003cb\u003eChapter 2 – X86-64 Core Programming (Part 1) \u003c\/b\u003eChapter Goal: Introduces the fundamentals of x86-64 assembly language programming. The programming examples illustrate essential x86-64 assembly language programming concepts including integer arithmetic, bitwise logical operations, and shift instructions. This chapter also explains basic assembler usage and x86-64 assembly language syntax.Assembler basicsInstruction syntaxAssembler directivesModern X86 Assembly Language Programming, Third Edition Page 2 of 7Daniel Kusswurm – F:\\ModX86Asm3E\\Proposal\\ModernX86Asm3e_Outline (proposal).docxMASM vs. NASMSource code overviewFile and function naming conventionsInteger arithmeticInteger (32-bit) addition and subtraction Bitwise logical operations Shift operations Integer (64-bit) addition and subtraction Integer multiplication and division \u003cbr\u003e\u003cb\u003eChapter 3 – X86-64 Core Programming (Part 2) \u003c\/b\u003eChapter Goal: Explores additional core x86-64 assembly language programming concepts. Topics discussed include advanced integer arithmetic, memory addressing modes, and condition codes. This chapter also covers important x86-64 assembly language programming concepts including proper stack use and for-loops.Simple stack arguments Mixed-type integer arithmetic Memory addressing Condition codes Assembly language for-loops \u003cbr\u003e\u003cb\u003eChapter 4 – X86-64 Core Programming (Part 3) \u003c\/b\u003eChapter 4 explains how to exercise core x86-64 assembly language programming data constructs including arrays and structures. It also describes how to use common x86-64 string processing instructions.Arrays1D integer array arithmetic calculations 1D integer array arithmetic calculations using multiple arrays2D integer arrays StringsOverview of x86 string instructionsCounting characters String\/array compare String\/array copy String\/array reversal Assembly language structures \u003cbr\u003e\u003cb\u003eChapter 5 – Scalar Floating-Point \u003c\/b\u003eChapter 5 teaches the reader how to perform scalar floating-point arithmetic and other operations using assembly language. It also outlines the calling convention requirements for scalar floating-point arguments and return values.Floating-point programming conceptsSingle-precision floating-point arithmeticTemperature conversions Cone volume\/surface area calculation Double-precision floating-point arithmetic\u003cbr\u003eSphere volume\/surface area calculation Floating-point compares and conversionsFloating-point compares using VUCOMIS[S|D] Floating-point compares using VCMPS[S|D] Floating-point conversions Floating-point arraysArray mean\/standard deviation calculation \u003cbr\u003e\u003cb\u003eChapter 6 – Assembly Language Calling Conventions \u003c\/b\u003eChapter 6 formally defines the calling run-time conventions for x86-64 assembly language functions. The first section explains the requirements for Windows and Visual C++ while the second section covers Linux and GNU C++.Calling convention requirements for Windows and Visual C++Stack frames (Ch06_01)Using non-volatile general-purpose registers Using non-volatile SIMD registers Calling external functions Calling convention requirements for Linux and GNU C++Stack arguments Using non-volatile general-purpose registers Calling external functions \u003cbr\u003e\u003cb\u003eChapter 7 – Advanced Vector Extensions \u003c\/b\u003eChapter 7 introduces Advanced Vector Extensions (AVX). It begins with a discussion of AVX architecture and related topics. Chapter 7 also explains elementary SIMD programming concepts. Understanding of this material is necessary for the reader to comprehend the AVX, AVX2, and AVX-512 programming examples in subsequent chapters.X86-AVX architecture overviewAVXAVX2AVX-512Merge masking and zero maskingEmbedded broadcastsInstruction level roundingSIMD programing conceptsBasic arithmeticWraparound vs. saturated arithmeticPack floating-pointPack integerProgramming differences between x86-SSE and x86-AVX\u003cbr\u003e\u003cb\u003eChapter 8 – AVX Programming – Packed Integers \u003c\/b\u003eChapter 8 spotlights packed integer arithmetic and other operations using AVX. It also describes how to code packed integer calculating functions using arrays and the AVX instruction set. Integer arithmeticAddition and subtraction Multiplication Bitwise logical operations Arithmetic and logical shifts Integer array algorithmsPixel minimum and maximum Pixel mean \u003cbr\u003e\u003cb\u003eChapter 9 – AVX Programming – Packed Floating Point \u003c\/b\u003eChapter 9 demonstrates packed floating-point arithmetic and other operations using AVX. This chapter also explains how to use AVX instructions to perform calculations with floating-point arrays and matrices.Floating-point arithmeticBasic arithmetic operations Compares Conversions Floating-point arraysArray mean and standard deviation Array square roots and compares Floating-point matricesMatrix column means \u003cbr\u003e\u003cb\u003eChapter 10 – AVX2 Programming – Packed Integers \u003c\/b\u003eChapter 10 describes AVX2 integer programming using x86-64 assembly language. This chapter also elucidates the coding of common image processing algorithms using the AVX2 instruction set.Integer arithmeticBasic operations Size promotions Image processingPixel clipping RGB to grayscale Pixel conversions Image histogram \u003cbr\u003e\u003cb\u003eChapter 11 – AVX2 Programming – Packed Floating Point (Part 1) \u003c\/b\u003eChapter 11 teaches the reader how to enhance the performance of universal floating-point calculations using x86-64 assembly language and the AVX2 instruction set. The reader will also learn how to accelerate these types of calculations using fused-multiply-add (FMA) instructions.Floating-Point ArraysLeast squares with FMA Floating-Point MatricesMatrix multiplication F32 Matrix multiplication F64 \u003cbr\u003eMatrix (4x4) multiplication F32 Matrix (4x4) multiplication F64 Matrix (4x4) vector multiplication F32 Matrix (4x4) vector multiplication F64 Covariance matrix F64 \u003cbr\u003e\u003cb\u003eChapter 12 – AVX2 Programming – Packed Floating Point (Part 2) \u003c\/b\u003eChapter 12 is a continuation of the previous chapter. It explicates the coding of advanced algorithms including matrix inversion and convolutions using AVX2 and FMA instructions.Advanced Matrix OperationsMatrix inverse F32 Matrix inverse F64 Signal Processing1D convolution F32 variable-size kernel 1D convolution F64 variable-size kernel 1D convolution F32 fixed-size kernel 1D convolution F64 fixed-size kernel \u003cbr\u003e\u003cb\u003eChapter 13 – AVX-512 Programming – Packed Integers \u003c\/b\u003eChapter 13 highlights packed integer arithmetic and other operations using x86-64 assembly language and AVX-512. It also discusses how to code frequently used image processing algorithms using the AVX-512 instruction set.Integer ArithmeticAddition and subtraction Masked addition and subtraction Image ProcessingPixel clipping Image statistics Image histogram \u003cbr\u003e\u003cb\u003eChapter 14 – AVX-512 Programming – Packed Floating Point (Part 1) \u003c\/b\u003eChapter 14 explains basic operations using packed floating-point operands and the AVX-512 instruction set. It also teaches the reader how to code common floating-point algorithms using x86-64 assembly language and AVX-512.Floating-point arithmeticFloating-point arithmetic Floating-point compares Floating-point arithmetic and mask registers Floating-point matricesCovariance matrix Matrix multiplication F32 Matrix multiplication F64 Matrix (4x4) vector multiplication F32 Matrix (4x4) vector multiplication F64 (Ch14_08)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eChapter 15 – AVX-512 Programming – Packed Floating Point (Part 2) \u003c\/b\u003eChapter 15 is a continuation of the previous chapter. It illustrates the coding of advanced algorithms using AVX-512 and FMA instructions.Signal Processing1D convolution F32 variable-size kernel 1D convolution F64 variable-size kernel 1D convolution F32 fixed-size kernel 1D convolution F64 fixed-size kernel \u003cbr\u003e\u003cb\u003eChapter 16 – Advanced Instructions and Optimization Guidelines \u003c\/b\u003eChapter 16 demonstrates the use of advanced x86-64 assembly language instructions. It also discusses guidelines that the reader can exploit to improve the performance of their assembly language code.Advanced instructionsCPUID instruction – processor information CPUID instruction – AVX, AVX2, FMA, and AVX-512 detection Integer non-temporal memory loads and stores Floating-point non-temporal memory stores SIMD text processing Processor microarchitecture overviewX86-64 assembly language optimization guidelines\u003cbr\u003e\u003cb\u003eAppendix A – Source Code and Development Tools \u003c\/b\u003eAppendix A describes how to download, install, and execute the source code. It also includes some brief usage notes about the software development tools used to create the source code examples.Source codeDownload instructionsSetup and configurationExecuting a source code exampleSoftware development tools for WindowsMicrosoft Visual StudioMASMSoftware development tools for LinuxGNU makeGNU C++ compilerNASMBenchmarking notes\u003cbr\u003e\u003cb\u003eAppendix B – References and Additional Resources \u003c\/b\u003eAppendix B contains a list of references that were consulted during the writing of this book. It also lists supplemental resources that the reader can consult for additional x86-64 assembly language programming information.X86-64 assembly language programming references\u003cbr\u003eAlgorithm referencesC++ referencesX86 processor software utilities and librariesAdditional resources\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739669803351,"sku":"9781484296028","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484296028.jpg?v=1720052863"},{"product_id":"an-introduction-to-mathematical-cryptography-9781493917105","title":"An Introduction to Mathematical Cryptography","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“This book explains the mathematical foundations of public key cryptography in a mathematically correct and thorough way without omitting important practicalities. … I would like to emphasize that the book is very well written and quite clear. Topics are well motivated, and there are a good number of examples and nicely chosen exercises. To me, this book is still the first-choice introduction to public-key cryptography.” (Klaus Galensa, Computing Reviews, March, 2015)\u003c\/p\u003e“This is a text for an upper undergraduate\/lower graduate course in mathematical cryptography. … It is very well written and quite clear. Topics are well-motivated, and there are a good number of examples and nicely chosen exercises. … An instructor of a fairly sophisticated undergraduate course in cryptography who wants to emphasize public key cryptography should definitely take a look at this book.” (Mark Hunacek, MAA Reviews, October, 2014)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48739724296535,"sku":"9781493917105","price":56.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781493917105.jpg?v=1720053001"},{"product_id":"deep-learning-with-pytorch-9781617295263","title":"Deep Learning with PyTorch","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eEvery other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. 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They were organized into the following topical sections: Cartograms and Intersection Graphs, Geometric Graph Theory, Clustering, Quality Metrics, Arrangements, A Low Number of Crossings, Best Paper in Track 1, Morphing and Planarity, Parameterized Complexity, Collinearities, Topological Graph Theory, Best Paper in Track 2, Level Planarity, Graph Drawing Contest Report, and Poster Abstracts.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eCartograms and Intersection Graphs.- \u003c\/b\u003eStick Graphs with Length Constraints.- Representing Graphs and Hypergraphs by Touching Polygons in 3D.- Optimal Morphs of Planar Orthogonal Drawings II.- Computing Stable Demers Cartograms.- \u003cb\u003eGeometric Graph Theory.-\u003c\/b\u003e Bundled Crossings Revisited.- Crossing Numbers of Beyond-Planar Graphs.- On the 2-Colored Crossing Number.- Minimal Representations of Order Types by Geometric Graphs.- Balanced Schnyder woods for planar triangulations: an experimental study with applications to graph drawing and graph separators.- \u003cb\u003eClustering.- \u003c\/b\u003eA Quality Metric for Visualization of Clusters in Graphs.-\u003cb\u003e \u003c\/b\u003eMulti-level Graph Drawing using Infomap Clustering.- On Strict (Outer-)Confluent Graphs.- \u003cb\u003eQuality Metrics.- \u003c\/b\u003eOn the Edge-Length Ratio of Planar Graphs.- Node Overlap Removal Algorithms: A Comparative Study.- Graphs with large total angular resolution.- \u003cb\u003eArrangements.- \u003c\/b\u003eComputing Height-Optimal Tangles Faster.-\u003cb\u003e \u003c\/b\u003eOn Arrangements of Orthogonal Circles.- Extending Simple Drawings.- Coloring Hasse diagrams and disjointness graphs of curves.- \u003cb\u003eA Low Number of Crossings.-\u003c\/b\u003e Efficient Generation of Different Topological Representations of Graphs Beyond-Planarity.- The QuaSEFE Problem.- ChordLink: A New Hybrid Visualization Model.- Stress-Plus-X (SPX) Graph Layout.- \u003cb\u003eBest Paper in Track 1.-\u003c\/b\u003e Exact Crossing Number Parameterized by Vertex Cover.- \u003cb\u003eMorphing and Planarity.-\u003c\/b\u003e Maximizing Ink in Partial Edge Drawings of k-Plane Graphs.- Graph Drawing with Morphing Partial Edges.- A Note on Universal Point Sets for Planar Graphs.- \u003cb\u003eParameterized Complexity.-\u003c\/b\u003e Parameterized Algorithms for Book Embedding Problems.- Sketched Representations and Orthogonal Planarity of Bounded Treewidth Graphs.- \u003cb\u003eCollinearities.- \u003c\/b\u003e4-Connected Triangulations on Few Lines.-\u003cb\u003e \u003c\/b\u003eLine and Plane Cover Numbers Revisited.-\u003cb\u003e \u003c\/b\u003eDrawing planar graphs with few segments on a polynomial grid.- Variants of the Segment Number of a Graph.-\u003cb\u003e Topological Graph Theory.- \u003c\/b\u003eLocal and Union Page Numbers.- Mixed Linear Layouts: Complexity, Heuristics, and Experiments.-\u003cb\u003e \u003c\/b\u003eHomotopy height, grid-major height and graph-drawing height.-\u003cb\u003e \u003c\/b\u003eOn the Edge-Vertex Ratio of Maximal Thrackles.- \u003cb\u003eBest Paper in Track 2.-\u003c\/b\u003e Symmetry Detection and Classification in Drawings of Graphs.- \u003cb\u003eLevel Planarity.-\u003c\/b\u003e An SPQR-Tree-Like Embedding Representation for Upward Planarity.- A Natural Quadratic Approach to the Generalized Graph Layering Problem.- Graph Stories in Small Area.- Level-Planar Drawings with Few Slopes.- \u003cb\u003eGraph Drawing Contest Report.- \u003c\/b\u003eGraph Drawing Contest Report.-\u003cb\u003e Poster Abstracts.- \u003c\/b\u003eA 1-planarity Testing and Embedding Algorithm.-\u003cb\u003e \u003c\/b\u003eStretching Two Pseudolines in Planar Straight-Line Drawings.-\u003cb\u003e \u003c\/b\u003eAdventures in Abstraction: Reachability in Hierarchical Drawings.-\u003cb\u003e \u003c\/b\u003eOn Topological Book Embedding for k-Plane Graphs.- On Compact RAC Drawings.- FPQ-choosable Planarity Testing.- Packing Trees into 1-Planar Graphs.- Geographic Network Visualization Techniques: A Work-In-Progress Taxonomy.- On the Simple Quasi Crossing Number of K 11.- Minimising Crossings in a Tree-Based Network.- Crossing Families and Their Generalizations.- Which Sets of Strings are Pseudospherical?.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743033930071,"sku":"9783030358013","price":44.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"primer-for-data-analytics-and-graduate-study-in-statistics-9783030474783","title":"Primer for Data Analytics and Graduate Study in Statistics","description":"This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. 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In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743037174103,"sku":"9783030474782","price":71.24,"currency_code":"GBP","in_stock":true}]},{"product_id":"powers-of-two-the-information-universe-information-as-the-building-block-of-everything-9783030583477","title":"Powers of Two: The Information Universe —","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIs everything Information? This is a tantalizing question which emerges in modern physics, life sciences, astronomy and in today’s information and technology-driven society. In \u003cem\u003ePowers of Two\u003c\/em\u003e expert authors undertake a unique expedition - in words and images - throughout the world (and scales) of information. The story resembles, in a way, the classic\u003cem\u003e Powers of Ten\u003c\/em\u003e\u003cem\u003e \u003c\/em\u003ejourneys through space: from us to the macro and the micro worlds . However, by following \u003cem\u003ePowers of Two\u003c\/em\u003e through the world of information, a completely different and timely paradigm unfolds. Every power of two, 1, 2, 4, 8…. tells us a different  story: starting from the creation of the very first bit at the Big Bang and the evolution of life, through 50 years of computational science, and finally into deep space, describing the information in black holes and even in the entire universe and beyond….  All this to address one question: \u003cstrong\u003eIs our universe made of information? \u003c\/strong\u003e\u003cstrong\u003e \u003c\/strong\u003eIn this book, we experience the Information Universe in nature and in our society and how information lies at the very foundation of our understanding of the Universe.\u003c\/p\u003e\u003cp\u003eFrom the Foreword by Robbert Dijkgraaf:\u003c\/p\u003e\u003cp\u003e \u003cem\u003eThis book is in many ways a vastly extended version of Shannon’s one-page blueprint. It carries us all the way to the total information content of the Universe. And it bears testimony of how widespread the use of data has become in all aspects of life. Information is the connective tissue of the modern sciences. […] Undoubtedly, future generations will look back at this time, so much enthralled by Big Data and quantum computers, as beholden to the information metaphor. But that is exactly the value of this book. \u003c\/em\u003e\u003ci\u003eWith its crisp descriptions and evocative illustrations, it brings the reader into the here and now, at the very frontier of scientific research, including the excitement and promise of all the outstanding questions and future discoveries.\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMessage for the e-reader of the book Powers of Two\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003eThe book has been designed to be read in two-page spreads in full screen mode.\u003c\/p\u003e  \u003cp\u003e  For optimal reader experience in a downloaded .pdf file we strongly recommend you use the following settings \u003cb\u003ein Adobe Acrobat Reader\u003c\/b\u003e:\u003c\/p\u003e  \u003cp\u003e      - Taskbar:  View \u0026gt; Page Display \u0026gt; two page view\u003cbr\u003e     - Taskbar:  View \u0026gt; Page Display \u0026gt; Show Cover Page in Two Page View\u003cbr\u003e     - Taskbar: ^ Preferences   \u0026gt; Full Screen \u0026gt;  deselect \" Fill screen with one page at a time\"\u003cbr\u003e     -  Taskbar: View  \u0026gt; Full screen mode or  ctrl L (cmd L on a Mac)\u003c\/p\u003e  \u003cp\u003e*****\u003c\/p\u003e  \u003cp\u003eNote: for reading the previews on \u003cb\u003eSpinger link\u003c\/b\u003e (and on-line reading in a browser), the full screen two-page view only works with these browsers:\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eFirefox\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e - Taskbar:  on top of the text, at the uppermost right you will see then \u0026gt;\u0026gt; (which is a drop-down menu)    \u0026gt;\u0026gt;\u003ci\u003e even double page\u003c\/i\u003es\u003c\/p\u003e  \u003cp\u003e - Fullscreen: F11 or Control+Cmd+F with Mac\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eEdge \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e - Taskbar middle: \u003ci\u003eTwo-page view\u003c\/i\u003e and select show cover page separately\u003c\/p\u003e\u003ci\u003e\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The book … a very unusual collection of some facts about the relationship between the immaterial world represented by bits and the real physical world described by fundamental physical equations. This book continues the very categorical point of view of J. A. Wheeler … . The book presents short articles on various areas of modern science … in which it is shown that in these areas in some mysterious way there is a connection with the theory of information.” (Vladimir Dzhunushaliev, zbMATH 1479.83004, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword by Robbert DijkgraafChapter 0: IntroductionJoy-riding the Universe – by the authorWorking as an astronomer, data scientist and professor of astro-informatics for nearly fifty years, Edwin Valentijn has witnessed and first-hand engineered the dawn of the era of Big Data in science and society. Throughout his career, he became increasingly aware of the role of information in our world: in computers, in our society, and even in nature and in the Universe itself.\u003cbr\u003eThe Information UniverseFollowing the increasing powers of two, the story paints a journey through the whole world of information, both in society and in nature. Each step opens a door into a new world: from the first bits with the Big Bang and the dawn of life, going through fifty years of human technology, all the way up to the information content of the whole Universe.\u003cbr\u003eWhat is Information? - Item pageThe basics of information are introduced.\u003cbr\u003eChapter 1: The beginningSpace-time foam – Ti (0 bit: 20 =1)The very first power of two: 20, corresponds to the value one. This identifies the single, eternal, indistinguishable state: the primordial sea from which our Universe emerged – sometimes called the Space-time foam. I call this Ti, the reverse of It. This is one of the miraculous new notions in the story of the Powers of Two.\u003cbr\u003eMultiverse: Anthropic principle (Item page)From Ti, the primordial space-time foam, countless universes arise with widely different characteristics: the Multiverse. The Anthropic Principle is a philosophical consideration which states that we, people, will find ourselves in a universe that is suitable for intelligent life to emerge. Therefore, this Principle demonstrates that conditions in our Universe are not “fine-tuned” to the existence of human life and a “creator” doesn’t exist.\u003cbr\u003eBig bang (1 bit: 21 =2 states)At the Big Bang the first bit is created. From the indistinguishable unity of the primordial foam Ti, “the zeros were separated from the 1’s”: the first bit corresponds to two possible states. This bit is the first step on our journey to capture the ever-increasing complexity of our expanding Universe in terms of information, through the increasing powers of two.\u003cbr\u003eWhat is a bit? (Item page)The bit is at the core of the concept of information. A bit is any system that can have two states. Humans assign meanings to these states, which are illustrated with the concept of the traffic light: red or green, stop or go. The combination of multiple bits creates an exponentially increasing number of possible states, and hence meanings.\u003cbr\u003eMulticellular life (2 bit: 22 =4 states) \/ (4 bit: 24 =16 states)?Life started with exchanging information between cells. This is fundamental for the evolution of any kind of life. It took at least two billion years for uni-cellular to evolve into multi-cellular organisms around 600 million years ago, and to start the exchange of information between their different cells. By exchanging information, cells collaborate and act as a unified whole: life.\u003cbr\u003eThe game of life (Item page)The characteristic features of life (or any complex system in the Universe) can be created from information. A simple computer game is all you need to demonstrate this concept. A famous example is Conway's Game of Life, which is full of visuals of living, growing, moving and dying objects. This game was already made on the computers of the early 70's with just a few lines of code.\u003cbr\u003eChapter 2: People's Information UniverseASCII (7 bit: 27 =128 states)There is currently no physical theory how the digital world connects to the human consciousness. In the world of Information Technology (IT) all information exchange is based on agreements between people. For instance, ASCII, a simple list relating each letter of the alphabet to a 7-bit string, connects the digital world to the human consciousness.\u003cbr\u003e Machu Picchu (8 bit: 28 =256, 1 byte)The Intiwatana stone, a giant rock carved by the Inca's of ancient Machu Picchu in Peru, can be considered as a first 8-bit hard disk. Why so? As the sunrays lit the different surfaces of this huge rock throughout the year, it triggered the Inca's activities: sowing, harvesting, celebrating and praying.This ancient stone dissolves both the boundaries between heaven and earth, and those between the digital and natural Information Universe. In fact, the stone represents an ultimate picture of the cross-over between the in vivo and the in vitro Information Universe - a main theme of the book. In vitro being the man made technology to handle information and in vivo being the information built in nature, in this case the orbit and the light rays of the sun.\u003cbr\u003eFirst computers (16 bit: 216 =65.536, 2 byte)When computers emerged in the 1970's, astronomers first adopted them to steer their telescopes. Back then, a maximal effort to understand the mathematics of the problem was needed to squeeze the solution into the small computer memory. Nowadays, with large amounts of computing power and machine learning at their disposal, scientists and computer programmers often do the reverse.\u003cbr\u003eStar Peace vs. Star Wars (Item page)King Juan Carlos adored the harmony of galaxies as a source of inspiration for people on earth, in those days when Ronald Reagan was promoting his Star-wars programme. With this adoration in mind, in 1985, he gave an inspiring speech at the Royal inauguration of the international astronomical observatory on La Palma, Canary Islands. The inauguration was attended by, for those days, an unprecedented large crowd of European royals and government officials despite the great threat of terrorist attacks by the ETA. (the next and later spreads on facts vs fakes elucidate the relevance of this spread in the story line).\u003cbr\u003ePre-internet Facts and Fakes (Item page)“Edwin Valentijn saved the life of the Dutch Queen Beatrix by catching her just before falling off a cliff at the inauguration on La Palma”, according to the headlines in Dutch newspapers. Fake news-stories are at all times alike and can only be dispelled by tracing links of information to their source, links or associations being a fundamental property of the Information Universe. Later, I discuss the less innocent case of overdrawing attention to terrorist attacks in the past decade.\u003cbr\u003eHard disk (24 bit: 224 =1.6*107, 2 Mb)Only sixty years ago, a 5 MB hard disk weighed over five tons, and had to be loaded onto an aeroplane by using a truck. Now, we carry a thousand times more information in our trouser pocket. This demonstrates the amazing advance of information technology over the past decades. (Picture: first IBM hard disk loaded onto a plane).\u003cbr\u003eThe telephone (Item page) As a precursor of the Internet, the telephone offered many of the same advantages and dangers, and was heavily discussed at its introduction. Whether telephone or the Internet, it all revolves around communication or copying of information. The telephone, as example of it, is one of the major discoveries of the 20th century. \u003cbr\u003eDNA (32 bit: 232 = 4*109, 500 Mb) – Guest author: Charley Lineweaver The information in the DNA creates life. All base pairs of the human DNA can be stored on a 500 Mb drive. How is this information communicated? How does a cell know it has to build part of a liver and not an eye, while they all have the same DNA? Apoptosis and the role of information exchange.Where does biological Information come from? (Item page) – Guest author: Charley Lineweaver Charley Lineweaver, expert on evolutionary biology, exoplanetology and astrobiology, will expand on the role of information in the evolution of life.\u003cbr\u003eLifelines (Item page) – Guest author: Morris SwertzWhat is the role of nature versus that of nurture? A key question in modern health research. In Lifesciences, this question is addressed now using Big Data, like the astronomers who acquire huge data volumes to address the same question on the nature of galaxies. In Lifelines, a cohort of 165.000 people is studied over a period of 30 years using hospital data, blood samples and DNA scans.\u003cbr\u003eDVD (33 bit: 233 =9*109, 1 Gb)It’ s amazing how fast the digital image revolution went since 1989.30 years ago, Philips lab approached me since they had made a big discovery: it was possible to store many digital images on a CD. They were chasing me for digital images. While NASA had less than a thousand, I had 32.000 galaxy images obtained by scanning photographic plates from the European Southern Observatory – the first large digital image collection.\u003cbr\u003eHuman Brain (36 bit: 236 =7*1010, 9 Gb) – Guest author: Katrin Amunts- JulichIn the large EU human brain project, the activities of the human brain are simulated in computers. This is a very difficult mission since the transistors in computers consume 100.000 billion times more energy than the synapsis of neurons. Our brains consist of 1011 neurons, corresponding to 9 Gb of data.Thinking of Karlheinz Meier, coordinator of the Human Brain Project in Heidelberg, Katrin Amunts will author two spreads on the role of information in the human brain.\u003cbr\u003eNeuromorphic computing – Guest author: Katrin AmuntsCurrently, it takes a hundred years of a supercomputer’s time to compete with the learning power of only a single day of the human brain. “Neuromorphic computing” researchers design electronic systems inspired by the human brain, in order to make computers many times faster and more energy efficient.\u003cbr\u003eCT scan (38 bit: 238 =3*1011, 34 Gb) – Guest author: Anders YnnermanNow it is possible to look inside animal and human bodies on touchscreens. Forensic investigations on, for instance, corpses of victims can be done with touch-screen tables. You can look inside, rotate, scroll and zoom animal and human bodies using tens of gigabytes of CT scan data. Prof. Anders Ynnerman explains how he does it.\u003cbr\u003eTerabytes (45 bit: 245 =4.4*1012, 1 Tb) - The largest (astronomical) datasetsDark energy and dark matter: two mysterious constituents of our Universe. How do astronomers get and handle the data from the VLT Survey Telescope on a high mountain top in Chile to shed lights on these ‘still too dark’ topics. This Telescope surveys the sky every hour at night generating Terabytes of astronomical data.\u003cbr\u003eGravity as a lens (Item page) – Guest author: Margot BrouwerWhen light rays are bent by the gravity of a heavy object, this object acts as a lens. This effect can be used to map dark matter, which is invisible but constitutes 80% of the matter in our visible Universe. In 1915, Albert Einstein posed that gravity is equivalent to the curvature of the fabric of space and time itself, leading to the lensing effect.\u003cbr\u003eWeak gravitational lensing surveys – Guest author: Margot BrouwerTerabytes of astronomical data are reduced to a few numbers, describing how dark matter behaves and what is its true nature. https:\/\/www.youtube.com\/watch?v=ZCyYGWqCmFw\u0026amp;t=23s\u003cbr\u003eEntering the Petabyte regime (53 bit: 253 =1*1015, 1 Pb)How do we technically acquire and deal with Petabytes of data?\u003cbr\u003eDark Matter maps (Item page)A first dark matter map projected on the night sky. An ultimate encounter between the digital world of modern astronomical observations, and nature: the mysterious dark matter mapped on top of the everyday “night” stellar sky. A visualization that condenses Terabytes of astronomical data to a simple map.\u003cbr\u003eMetadata for Peta-data (62 bit: 262 =6*1017, 600 Pb)With pointers, one can connect everything in the Information Universe. Pointers are often inserted in Metadata (data about data) - an ultimate tool for dealing with Big Data. It is possible to create unique pointers to hundreds of Petabytes of data, using a string of less than 64 bits. This is what makes pointers so powerful and indispensable in current and future stages of the big data era; not only for astronomical research, but also for companies like Google, Amazon and Facebook.\u003cbr\u003eDownloading the Universe (Item page)The universe can be seen as a spreadsheet, certainly in the way we map it on our computers (in vitro), but also in nature (in vivo). Perceiving the Universe as a spreadsheet links bit to It.\u003cbr\u003eMeta data (Item page)A visualisation of the enormous complexity of data models which trace all pointers between data items. (picture: thrilling still from a full dome animation of a data model)\u003cbr\u003eFuture (astronomical) datasets (item page)While current telescopes collect astronomical datasets of Terabytes, future telescopes such as the LSST and the Euclid satellite, instead, will collect Petabytes. These enormous amounts of data need a whole new approach to data management. For the Euclid satellite my “Universe as a spreadsheet” approach has been adopted.\u003cbr\u003eThe Euclid satellite (Item page) – Guest author: Margot BrouwerEuclid is ESA’s new space mission to map the Dark Universe. At a distance of 1.5 million kilometres from Earth, this telescope will observe billions of galaxies. Its goal: to shed light on the nature of Dark Matter and Dark Energy, which make up 95% of our Universe. Dr. Margot Brouwer, Dutch scientific communication officer for Euclid, will explain more.\u003cbr\u003eThe Information Universe (Item page)The resemblance of the overall structure of the real observed Universe (in vivo) with the simulated universe (in vitro), based on the concurrent cosmological model, gave a lot of credit to the latter. When we zoom out the Universe, we see billions of galaxies forming a web-like structure. Amazingly, astronomers can now compute and simulate these structures with very large supercomputers.\u003cbr\u003eThe lost boy (Item page)Information is timeless, and knows no boundaries. It crosses over the in vivo and the in vitro Information Universe. This concept is well illustrated through daily life stories involving time. At the age of five, a boy loses sight of his older brother on a train in India, and eventually gets lost on the streets of Mumbai. Twenty years later, after being adopted by a family in Australia, he is able to find his natural mother (in vivo) through only searching on Google maps (in vitro).\u003cbr\u003eQbits (50 qbit: 250 =1.1*1015 qbit, 1 Pbit) – Guest author: Lieven VandersypenUsing fundamental particles (quanta, such as electrons) to perform calculations and build computers, is one of the most exciting cross-overs between the in vivo and the in vitro Information Universe. Prof. Lieven Vandersypen, who leads a Quantum Computing group at TU Delft in the Netherlands, will explain how this technology will change the way we compute.\u003cbr\u003eQuantum entanglement (Item page) – Guest author: Lieven VandersypenThe states of two particles can be intimately linked (entangled), no matter how far they are separated. What Einstein famously dismissed as “spooky action at a distance”, can now be established on demand at TU Delft in the Netherlands. Prof. Vandersypen will explain how his research group, for the first time ever, both create and apply this entanglement in laboratory.\u003cbr\u003eEntanglement (item page) - EVThe Square Kilometre Array (64 bit: 264 =1.3*1018, 1 Eb) – Guest author: TBAThe Square Kilometre Telescope will collect data at the rate of the global internet traffic of 2013, in its endeavour to answer fundamental questions about the origin and evolution of the Universe, and its search for extra-terrestrial life.\u003cbr\u003eCryptography (128 bit: 2128 =3.4*1038) – Guest author Tanja LangeEncrypted messages should not be decoded by adversaries, be they criminals or hostile countries. Cryptography enables secure communications and is one of the few applications which require 128-bit numbers. A guest author will explain more.\u003cbr\u003eChapter 3: Deep spaceThe Desert (128-256 bit) Theoretical physics is not progressing much in the last decennia – some call it a crisis. Likely, an observational breakthrough is out of reach: the highest man-made information density on earth is produced by the high energy accelerators at CERN. But these accelerators have to be 1013 -1015 more powerful to reach the fundamental unit of information, which is probably at the same level of the Planck length. Unfortunately, there is no way to reach this unit of information with these instruments. This enormous gap in reaching all the domains in the Information Universe is illustrated in a figure and in a very sobering, but instructive table in the Appendix.\u003cbr\u003eBlack holes (128-256 bit?) – Guest author: Manus VisserCan information disappear into a black hole? The Information paradox. Stephen Hawking wondered it and started a field in which space and time are described in terms of information. Dr. Manus Visser, expert on gravity and space-time, will explain more.\u003cbr\u003eObserving a Black Hole: Event Horizon Telescope – Guest author: Heino FalckeThe first image of a black hole. Prof. Heino Falcke, chair of the Event Horizon Telescope Science Council, will explain how information from a world-wide network of telescopes was combined using atomic clocks, to create the first ever image of a black hole. (Picture: first image of a black hole)\u003cbr\u003eCogwheels: a deeper level – Guest author: Gerard 't HooftNobel laureate ‘t Hooft explains his views on cogwheels, carrying the fundamental information in the Universe.\u003cbr\u003eGravitational waves – Guest author: Chris van den Broeck\u003cbr\u003eLinks: The Universe as a spreadsheetLinks, joins, references, URLs, blockchain, associations and even entanglement in physics are all different words for the same building block, forming the connections in the Information Universe.\u003cbr\u003eCosmic Microwave Background – Guest author: Margot BrouwerParticles of light created in the hot and dense state of the Universe after the Big Bang are still flying through the Universe today. Together, these 1077 photons contain the largest amount of information known in the Universe. This information can still be accessed through telescopes, and brings us invaluable information about the dawn of our Universe.\u003cbr\u003eEmergent Gravity – Guest author: Erik VerlindeProf. Erik Verlinde, professor of theoretical physics at the University of Amsterdam, won the Spinoza prize for his new theory explaining gravity. In his theory, all matter, space and time consist of information and are all connected by entanglement. If this theory is correct, the information content of the entire Universe is 2399. This is the highest power described in this book, and actually, in physics.\u003cbr\u003eChapter 4: It from BitOne big information processing machine – Guest author: Gerard 't Hooft (TBC)t Hooftt Hooft: : ““there is something happening at a different level of nature”there is something happening at a different level of nature”..\u003cbr\u003eOn the origin of physical information. – Guest author: Stefano Gottardi\u003cbr\u003eThe ear In the ear information is copied a dozen times!\u003cbr\u003eThe eye – on the visual perception of data- climate change. Links to - facts and fakes- the system of ScienceThe System of Science\u003cbr\u003eHow does this system work? Discussing Hegel’s system of science, logic, technology, Nature, life, physics, consciousness.\u003cbr\u003eArtificial IntelligenceThe machine learning and the data-base oriented communities are still living on different planets. I discuss and revisit Tegmark’s recent book Life 3.0 by comparing 3 crosscuts through the Information Universe: i) the classical computer centric view ii) the data centric view iii) the artificial intelligence view.\u003cbr\u003eInformation densityThe average information density of the universe can be compared to that of written text.\u003cbr\u003eBlack Body radiation On the information aspects of the third big physical breakthrough of the 20th century (next to General relativity and quantum mechanics).\u003cbr\u003eEntropyDiscussing Shannon’s work and identifying that “Information only exists in relation to its environment”. Examples will be given.\u003cbr\u003eCosmic information, cosmogenesis and dark energy by PadmanabhanCosmic information connects the cosmological constant to cosmogenesis\u003cbr\u003eIt from BitIs the Universe one big information processing machine?\u003cbr\u003eConsciousnessVery little is known about the consciousness and I refrain from addressing the consciousness per se. A relevant list of about 5 facts we do know are listed. Any view on the relation between the consciousness and the Information Universe should at least deal with this list.\u003cbr\u003eSomnium – Musician Jacco Gardner performing at DOTLiveplanetarium at Eurosonic 2019 show case music festival- Inspired by Kepler’s Somnium – directed by EV \u003cbr\u003eThe Information UniverseAn overview.\u003cbr\u003eFacts and fakesHow is all this related to the current facts and fakes issues on the Internet? How do you make sure that what you are reading is accurate and comes from a reliable source?The link between Open Science, FAIR and reliability of data.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743041139031,"sku":"9783030583477","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030583477.jpg?v=1720063853"},{"product_id":"fundamentals-of-quantum-computing-theory-and-practice-9783030636883","title":"Fundamentals of Quantum Computing: Theory and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis introductory book on quantum computing includes an emphasis on the development of algorithms.  Appropriate for both university students as well as software developers interested in programming a quantum computer, this practical approach to modern quantum computing takes the reader through the required background and up to the latest developments.\u003c\/p\u003e  \u003cp\u003eBeginning with introductory chapters on the required math and quantum mechanics, \u003ci\u003eFundamentals of Quantum Computing\u003c\/i\u003e proceeds to describe four leading qubit modalities and explains the core principles of quantum computing in detail. Providing a step-by-step derivation of math and source code, some of the well-known quantum algorithms are explained in simple ways so the reader can try them either on IBM Q or Microsoft QDK. The book also includes a chapter on adiabatic quantum computing and modern concepts such as topological quantum computing and surface codes.\u003c\/p\u003e\u003cp\u003eFeatures:\u003c\/p\u003e\u003cp\u003eo   Foundational chapters that build the necessary background on math and quantum mechanics.\u003c\/p\u003e\u003cp\u003eo   Examples and illustrations throughout provide a practical approach to quantum programming with end-of-chapter exercises.\u003c\/p\u003e\u003cp\u003eo   Detailed treatment on four leading qubit modalities -- trapped-ion, superconducting transmons, topological qubits, and quantum dots -- teaches how qubits work so that readers can understand how quantum computers work under the hood and devise efficient algorithms and error correction codes. Also introduces protected qubits - 0-π qubits, fluxon parity protected qubits, and charge-parity protected qubits.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eo   Principles of quantum computing, such as quantum superposition principle, quantum entanglement, quantum teleportation, no-cloning theorem, quantum parallelism, and quantum interference are explained in detail.  \u003cbr\u003e\u003c\/p\u003eA dedicated chapter on quantum algorithm explores both oracle-based, and Quantum Fourier Transform-based algorithms in detail with step-by-step math and working code that runs on IBM QisKit and Microsoft QDK. Topics on EPR Paradox, Quantum Key Distribution protocols, Density Matrix formalism, and Stabilizer formalism are intriguing. While focusing on the universal gate model of quantum computing, this book also introduces adiabatic quantum computing and quantum annealing.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book includes a section on fault-tolerant quantum computing to make the discussions complete. The topics on Quantum Error Correction, Surface codes such as Toric code and Planar code, and protected qubits help explain how fault tolerance can be built at the system level.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The book represents a new and fresh approach to quantum computing, starting with theoretical physical knowledge that is highlighted by beautiful figures. Then, quantum computing is explained by quantum programing languages and extensive languages. It is recommended to everyone interested in quantum computing. It is easy to follow through a beautiful and clear presentation, programming examples and additional exercises.” (Andreas Wichert, zbMATH 1477.68005, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART ONE\t1 Foundations of Quantum Mechanics\t1.1 Matter\t1.2 Atoms, Elementary Particles, and Molecules\t1.3 Light and Quantization of Energy\t1.4 Electron Configuration\t1.5 Wave-Particle Duality and Probabilistic Nature\t1.6 Wavefunctions and Probability Amplitudes\t1.7 Some exotic states of matter\t1.8 Summary\t1.9 Practice Problems\t1.10 References and further reading\t2 Dirac’s bra-ket notation and Hermitian Operators2.1 Scalars\t2.2 Complex Numbers\t2.3 Vectors\t2.4 Matrices\t2.5 Linear Vector Spaces\t2.6 Using Dirac’s bra-ket notation\t2.7 Expectation Values and Variances2.8 Eigenstates, Eigenvalues and Eigenfunctions2.9 Characteristic Polynomial\t2.10 Definite Symmetric Matrices\t2.11 Tensors2.12 Statistics and Probability2.13 Summary\t2.14 Practice problems2.15 References and further reading3 The Quantum Superposition Principle and Bloch Sphere Representation3.1 Euclidian Space3.2 Metric Space3.3 Hilbert space.3.4 Schrodinger Equation3.5 Postulates of Quantum Mechanics3.6 Quantum Tunneling3.7 Stern and Gerlach Experiment3.8 Bloch sphere representation3.9 Projective Measurements3.10 Qudits3.11 Summary3.12 Practice Problems3.13 References and further readingPART TWO4 Qubit Modalities4.1 The vocabulary of quantum computing4.2 Classical Computers – a recap\t4.3 Qubits and usability4.4 Noisy Intermediate Scale Quantum Technology4.5 Qubit Metrics4.6 Leading Qubit Modalities4.7 A note on the dilution refrigerator4.8 Summary4.9 Practice Problems4.10 References and further reading5 Quantum Circuits and DiVincenzo Criteria5.1 Setting up the development environment5.2 Learning Quantum Programming Languages\t5.3 Introducing Quantum Circuits\t5.4 Quantum Gates\t5.5 The Compute Stage5.6 Quantum Entanglement5.7 No-Cloning theorem5.8 Quantum Teleportation5.9 Superdense coding5.10 Greenberger–Horne–Zeilinger state (GHZ state)5.11 Walsh-Hadamard Transform5.12 Quantum Interference5.13 Phase kickback5.14 DiVincenzo’s criteria for quantum computation5.15 Summary\t5.16 Practice Problems5.17 References and further reading6 Quantum Communications6.1 EPR Paradox6.2 Density Matrix Formalism6.3 Von Neumann Entropy6.4 Photons6.5 Quantum Communication6.6 The Quantum Channel6.7 Quantum Communication Protocols6.8 RSA Security6.9 Summary6.10 Practice Problems6.11 References and further reading7 Quantum Algorithms7.1 Quantum Ripple Adder Circuit7.2 Quantum Fourier Transformation7.3 Deutsch-Jozsa oracle7.4 The Bernstein-Vazirani Oracle7.5 Simon’s algorithm7.6 Quantum arithmetic using QFT7.7 Modular exponentiation7.8 Grover’s search algorithm\t7.9 Shor’s algorithm7.10 A quantum algorithm for k-means7.11 Quantum Phase Estimation (QPE)7.12 HHL algorithm for solving linear equations7.13 Quantum Complexity Theory7.14 Summary\t7.15 Practice Problems7.16 References and further reading8 Adiabatic Optimization and Quantum Annealing8.1 Adiabatic evolution8.2 Proof of the Adiabatic Theorem8.3 Adiabatic optimization8.4 Quantum Annealing8.5 Summary8.6 Practice Problems8.7 References and further reading9 Quantum Error Correction9.1 Classical Error Correction9.2 Quantum Error Codes9.3 Stabilizer formalism9.4 The path forward – fault-tolerant quantum computing9.5 Surface codes9.6 Protected qubits9.7 Practice Problems9.8 References and further reading10 Conclusion10.1 How many qubits do we need?10.2 Classical simulation10.3 Backends today10.4 Future state10.5 References\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743043498327,"sku":"9783030636883","price":75.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"statistical-foundations-reasoning-and-inference-for-science-and-data-science-9783030698263","title":"Statistical Foundations, Reasoning and Inference:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Background in Probability.- Parametric Statistical Models.- Maximum Likelihood Inference.- Bayesian Statistics.- Statistical Decisions.- Regression.- Bootstrapping.- Model Selection and Model Averaging.- Multivariate and Extreme Value Distributions.- Missing and Deficient Data.- Experiments and Causality.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743045857623,"sku":"9783030698263","price":94.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"a-quantum-computation-workbook-9783030912161","title":"A Quantum Computation Workbook","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eTeaching quantum computation and information is notoriously difficult, because it requires covering subjects from various fields of science, organizing these subjects consistently in a unified way despite their tendency to favor their specific languages, and overcoming the subjects’ abstract and theoretical natures, which offer few examples of actual realizations. \u003cbr\u003eIn this book, we have organized all the subjects required to understand the principles of quantum computation and information processing in a manner suited to physics, mathematics, and engineering courses as early as undergraduate studies.In addition, we provide a supporting package of quantum simulation software from Wolfram Mathematica, specialists in symbolic calculation software. \u003cbr\u003eThroughout the book’s main text, demonstrations are provided that use the software package, allowing the students to deepen their understanding of each subject through self-practice. Readers can change the code so as to experiment with their own ideas and contemplate possible applications. The information in this book reflects many years of experience teaching quantum computation and information. The quantum simulation-based demonstrations and the unified organization of the subjects are both time-tested and have received very positive responses from the students who have experienced them.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The book provides an extensive bibliography and index. … this volume is well suited for a advanced graduate or first-year PhD course in quantum mechanics, with ample time available for self-study.” (L.-F. Pau, Computing Reviews, January 30, 2023)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 The Postulates of Quantum Mechanics.- 2 Virtual Realization of Quantum Computers.- 3 Quantum Computation: Overview.- 4 Quantum Algorithms: Introduction.- 5 Quantum Information: Introduction.- 6 Quantum Error Correction Codes: Introduction.- Appendix A Linear Algebra.- Appendix B Mathematica Application Q3.- References.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743057523031,"sku":"9783030912161","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030912161.jpg?v=1723812626"},{"product_id":"computer-vision-statistical-models-for-marrs-paradigm-9783030965297","title":"Computer Vision: Statistical Models for Marr's","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAs the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics. \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.- About the Authors.- 1 Introduction.- 2 Statistics of Natural Images.- 3 Textures.- 4 Textons.- 5 Gestalt Laws and Perceptual Organizations.- 6 Primal Sketch: Integrating Textures and Textons.- 7 2.1D Sketch and Layered Representation.- 8 2.5D Sketch and Depth Maps.- 9 Learning about information Projection.-  10 Informing Scaling and Regimes of Models.- 11 Deep Images and Models.- 12 A Tale of Three Families: Discriminative, Generative and Descriptive Models.- Bibliography\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743061946711,"sku":"9783030965297","price":64.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"machine-learning-algorithms-adversarial-robustness-in-signal-processing-9783031163746","title":"Machine Learning Algorithms: Adversarial","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks.\u003cbr\u003e \u003cbr\u003e The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm.\u003cbr\u003e \u003cbr\u003e This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R\u0026amp;D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter. 1. IntroductionChapter. 2. Optimal Feature Manipulation Attacks Against Linear RegressionChapter. 3. On the Adversarial Robustness of LASSO Based Feature SelectionChapter. 4. On the Adversarial Robustness of Subspace LearningChapter. 5. Summary and ExtensionsChapter. 6. Appendix","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743074038103,"sku":"9783031163746","price":87.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031163746.jpg?v=1720063997"},{"product_id":"simulation-algorithms-for-computational-systems-biology-9783319631110","title":"Simulation Algorithms for Computational Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.\u003c\/p\u003e\u003cp\u003eThis is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“I will not hesitate to recommend … this book, both as an introductory explanation as well as later on when they are deep in a modeling exercise and need to understand the many subtle yet important variations of stochastic simulation techniques applicable to biological systems.” (Sara Kalvala, Computing Reviews, March, 2018)​\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Deterministic Simulation Algorithms.- Stochastic Simulation Algorithms.- Hybrid Simulation Algorithms.- Reaction-Diffusion Systems.- Conclusions and Perspectives.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743100285271,"sku":"9783319631110","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"introduction-to-reliable-and-secure-distributed-programming-9783642152597","title":"Introduction to Reliable and Secure Distributed","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn modern computing a program is usually distributed among several processes. The fundamental challenge when developing reliable and secure distributed programs is to support the cooperation of processes required to execute a common task, even when some of these processes fail. Failures may range from crashes to adversarial attacks by malicious processes.\u003c\/p\u003e\u003cp\u003eCachin, Guerraoui, and Rodrigues present an introductory description of fundamental distributed programming abstractions together with algorithms to implement them in distributed systems, where processes are subject to crashes and malicious attacks. The authors follow an incremental approach by first introducing basic abstractions in simple distributed environments, before moving to more sophisticated abstractions and more challenging environments. Each core chapter is devoted to one topic, covering reliable broadcast, shared memory, consensus, and extensions of consensus. For every topic, many exercises and their solutions enhance the understanding \u003c\/p\u003e\u003cp\u003eThis book represents the second edition of \"Introduction to Reliable Distributed Programming\". Its scope has been extended to include security against malicious actions by non-cooperating processes. This important domain has become widely known under the name \"Byzantine fault-tolerance\". \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction. - 1.1 Motivation. -1.2 Distributed Programming Abstractions. 1.3 The End-to-End Argument. 1.4 Software Components. - 1.5 Classes of Algorithms. -1.6 Chapter Notes. 2. Basic Abstractions. - 2.1 Distributed Computation. - 2.2 Abstracting Processes. - 2.3 Cryptographic Abstractions. - 2.4 Abstracting Communication. - 2.5 Timing Assumptions. - 2.6 Abstracting Time. - 2.7 Distributed-System Models. - 2.8 Exercises. - 2.9 Solutions. - 2.10 Chapter Notes . - . - 3. Reliable Broadcast. - 3.1 Motivation. - 3.2 Best-Effort Broadcast. - 3.3 Regular Reliable Broadcast. - 3.4 Uniform Reliable Broadcast. - 3.5 Stubborn Broadcast. - 3.6 Logged Best-Effort Broadcast. - 3.7 Logged Uniform Reliable Broadcast. - 3.8 Probabilistic Broadcast. - 3.9 FIFO and Causal Broadcast. - 3.10 Byzantine Consistent Broadcast. - 3.11 Byzantine Reliable Broadcast. - 3.12 Byzantine Broadcast Channels. - 3.13 Exercises. - 3.14 Solutions. - 3.15 Chapter Notes . - . - 4. Shared Memory. - 4.1 Introduction. - 4.2 (1, N) Regular Register. - 4.3 (1, N) Atomic Register. - 4.4 (N, N) Atomic Register. - 4.5 (1, N) Logged Regular Register. - 4.6 (1,N) Byzantine Safe Register. - 4.7 (1, N) Byzantine Regular Register. - 4.8 (1,N) Byzantine Atomic Register. - 4.9 Exercises. - 4.10 Solutions. - 4.11 Chapter Notes . - . - 5. Consensus. - 5.1 Regular Consensus. - 5.2 Uniform Consensus. - 5.3 Uniform Consensus in the Fail-Noisy Model. - 5.4 Logged Consensus. - 5.5 Randomized Consensus. - 5.6 Byzantine Consensus. - 5.7 Byzantine Randomized Consensus. - 5.8 Exercises. - 5.9 Solutions. - 5.10 Chapter Notes . - . - 6. Consensus Variants. - 6.1 Total-Order Broadcast. - 6.2 Byzantine Total-Order Broadcast. - 6.3 Terminating Reliable Broadcast. - 6.4 Fast Consensus. - 6.5 Fast Byzantine Consensus. - 6.6 Non-blocking Atomic Commit. - 6.7 Group Membership. - 6.8 View-Synchronous Communication. - 6.9 Exercises. - 6.10 Solutions. - 6.11 Chapter Notes . - . - 7. Concluding Remarks. - 7.1 Implementation in Appia. - 7.2 Further Implementations. - 7.3 Further Reading","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743134167383,"sku":"9783642152597","price":71.24,"currency_code":"GBP","in_stock":true}]},{"product_id":"basic-concepts-in-algorithms-9789811238529","title":"Basic Concepts In Algorithms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is the result of several decades of teaching experience in data structures and algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. Its coverage includes the algorithms' design process and an analysis of their performance. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming.Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Each chapter comes with its own set of exercises, and solutions to most of them are appended.Related Link(s)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eDivide and Conquer; Dynamic Programming; Minimum Spanning Tree; Shortest Paths; Primality; Compression; Pattern Matching; Fat Fourier Transform; Cryptography; NP Completeness; Approximations; Solutions to Selected Exercises;","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743280443735,"sku":"9789811238529","price":52.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811238529.jpg?v=1720064901"},{"product_id":"mcmc-from-scratch-a-practical-introduction-to-markov-chain-monte-carlo-9789811927140","title":"MCMC from Scratch: A Practical Introduction to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC)  without assuming advanced knowledge of mathematics and programming. MCMC is  a powerful technique that can be used to integrate complicated functions or to handle  complicated probability distributions. MCMC is frequently used in diverse fields where  statistical methods are important – e.g. Bayesian statistics, quantum physics, machine  learning, computer science, computational biology, and mathematical economics. This  book aims to equip readers with a sound understanding of MCMC and enable them  to write simulation codes by themselves.  \u003c\/p\u003e  \u003cp\u003eThe content consists of six chapters. Following Chap. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents  the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through  the simple example of the Metropolis algorithm. In turn, Chap. 5 explains the HMC  algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing  their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC.  Including a wealth of examples and exercises with solutions, as well as sample codes  and further math topics in the Appendix, this book offers a valuable asset for students  and beginners in various fields. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Introduction.- Chapter 2: What is the Monte Carlo method?.- Chapter 3: General Aspects of Markov Chain Monte Carlo.- Chapter 4: Metropolis Algorithm.- Chapter 5: Other Useful Algorithms.- Chapter 6: Applications of Markov Chain Monte Carlo.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743293616471,"sku":"9789811927140","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811927140.jpg?v=1720064960"},{"product_id":"algorithmics-of-matching-under-preferences-9789814425247","title":"Algorithmics Of Matching Under Preferences","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMatching problems with preferences are all around us: they arise when agents seek to be allocated to one another on the basis of ranked preferences over potential outcomes. Efficient algorithms are needed for producing matchings that optimise the satisfaction of the agents according to their preference lists.In recent years there has been a sharp increase in the study of algorithmic aspects of matching problems with preferences, partly reflecting the growing number of applications of these problems worldwide. The importance of the research area was recognised in 2012 through the award of the Nobel Prize in Economic Sciences to Alvin Roth and Lloyd Shapley.This book describes the most important results in this area, providing a timely update to The Stable Marriage Problem: Structure and Algorithms (D Gusfield and R W Irving, MIT Press, 1989) in connection with stable matching problems, whilst also broadening the scope to include matching problems with preferences under a range of alternative optimality criteria.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreliminary Definitions, Results and Motivation; Stable Matching Problems: The Stable Marriage Problem: An Update; SM and HR with Indifference; The Stable Roommates Problem; Further Stable Matching Problems; Other Optimal Matching Problems: Pareto Optimal Matchings; Popular Matchings; Profile-Based Optimal Matchings.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743297352023,"sku":"9789814425247","price":148.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789814425247.jpg?v=1723812658"},{"product_id":"algorithms-to-live-by-the-computer-science-of-human-decisions-9780007547999","title":"Algorithms to Live By The Computer Science of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA fascinating exploration of how computer algorithms can be applied to our everyday lives.In this dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show us how the simple, precise algorithms used by computers can also untangle very human questions. Modern life is constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? The authors explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others.From finding a spouse to finding a parking spot, from organizing one''s inbox to understanding the workings of human memory, Algorithms To Live By is full of practical takeaways to help you solve common decision-making problems and illuminate the workings of the human mind.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e‘I’ve been waiting for a book to come along that merges computational models with human psychology – and Christian and Griffiths have succeeded beyond all expectations. This is a wonderful book, written so that anyone can understand the computer science that runs our world – and more importantly, what it means to our lives’ David Eagleman, author of ‘Sum: Tales from the Afterlives’\u003c\/p\u003e           \u003cp\u003e‘Compelling and entertaining, Algorithms to Live By is packed with practical advice about how to use time, space, and effort more efficiently. And it’s a fascinating exploration of the workings of computer science and the human mind. Whether you want to optimize your to-do list, organize your closet, or understand human memory, this is a great read’ ‘Charles Duhigg, author of The Power of Habit’\u003c\/p\u003e           \u003cp\u003e‘A truly beautiful exploration through math, computer science and philosophy of some of the most ordinary, yet most important dilemmas any of us is likely to face. Filled with humour and wisdom, this is a bible with a brain’ Aarathi Prasad\u003c\/p\u003e","brand":"HarperCollins Publishers","offers":[{"title":"Default Title","offer_id":48863867863383,"sku":"9780007547999","price":10.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780007547999.jpg?v=1722269381"},{"product_id":"data-structures-algorithms-in-python-9780134855684","title":"Data Structures  Algorithms in Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e1 Overview  . . . 1 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eWhat Are Data Structures and Algorithms?. . . . . . . . . . . . . . . . . . . . . . . 1\u003c\/p\u003e \u003cp\u003eOverview of Data Structures.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\u003c\/p\u003e \u003cp\u003eOverview of Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6\u003c\/p\u003e \u003cp\u003eSome Definitions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6\u003c\/p\u003e \u003cp\u003eProgramming in Python.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8\u003c\/p\u003e \u003cp\u003eObject-Oriented Programming.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 2 Arrays  . . . 29\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThe Array Visualization Tool.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30\u003c\/p\u003e \u003cp\u003eUsing Python Lists to Implement the Array Class.. . . . . . . . . . . . . . . . . 37\u003c\/p\u003e \u003cp\u003eThe OrderedArray Visualization Tool.. . . . . . . . . . . . . . . . . . . . . . . . . . 47\u003c\/p\u003e \u003cp\u003eBinary Search.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48\u003c\/p\u003e \u003cp\u003ePython Code for an OrderedArray Class.. . . . . . . . . . . . . . . . . . . . . . . . 52\u003c\/p\u003e \u003cp\u003eLogarithms.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\u003c\/p\u003e \u003cp\u003eStoring Objects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60\u003c\/p\u003e \u003cp\u003eBig O Notation.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65\u003c\/p\u003e \u003cp\u003eWhy Not Use Arrays for Everything?.. . . . . . . . . . . . . . . . . . . . . . . . . . 69\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 3 Simple Sorting  . . . 75\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eHow Would You Do It?.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76\u003c\/p\u003e \u003cp\u003eBubble Sort. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77\u003c\/p\u003e \u003cp\u003eSelection Sort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83\u003c\/p\u003e \u003cp\u003eInsertion Sort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87\u003c\/p\u003e \u003cp\u003eComparing the Simple Sorts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 4 Stacks and Queues  . . . 103\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eDifferent Structures for Different Use Cases.. . . . . . . . . . . . . . . . . . . . . 103\u003c\/p\u003e \u003cp\u003eStacks.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104\u003c\/p\u003e \u003cp\u003eQueues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116\u003c\/p\u003e \u003cp\u003ePriority Queues.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126\u003c\/p\u003e \u003cp\u003eParsing Arithmetic Expressions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 5 Linked Lists . . .  157\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eLinks.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158\u003c\/p\u003e \u003cp\u003eThe LinkedList Visualization Tool.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 164\u003c\/p\u003e \u003cp\u003eA Simple Linked List.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167\u003c\/p\u003e \u003cp\u003eDouble-Ended Lists.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177\u003c\/p\u003e \u003cp\u003eLinked List Efficiency.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183\u003c\/p\u003e \u003cp\u003eAbstract Data Types and Objects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184\u003c\/p\u003e \u003cp\u003eOrdered Lists.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192\u003c\/p\u003e \u003cp\u003eDoubly Linked Lists.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198\u003c\/p\u003e \u003cp\u003eInsertion and Deletion at the Ends.. . . . . . . . . . . . . . . . . . . . . . . 201\u003c\/p\u003e \u003cp\u003eInsertion and Deletion in the Middle.. . . . . . . . . . . . . . . . . . . . . 204\u003c\/p\u003e \u003cp\u003eDoubly Linked List as Basis for Deques.. . . . . . . . . . . . . . . . . . . . 208\u003c\/p\u003e \u003cp\u003eCircular Lists.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209\u003c\/p\u003e \u003cp\u003eIterators.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e6 Recursion  . . . 229\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eTriangular Numbers.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230\u003c\/p\u003e \u003cp\u003eFactorials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237\u003c\/p\u003e \u003cp\u003eAnagrams.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239\u003c\/p\u003e \u003cp\u003eA Recursive Binary Search.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242\u003c\/p\u003e \u003cp\u003eThe Tower of Hanoi.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245\u003c\/p\u003e \u003cp\u003eSorting with mergesort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255\u003c\/p\u003e \u003cp\u003eEliminating Recursion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267\u003c\/p\u003e \u003cp\u003eSome Interesting Recursive Applications.. . . . . . . . . . . . . . . . . . . . . . . 275\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 7 Advanced Sorting  . . . 285 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eShellsort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285\u003c\/p\u003e \u003cp\u003ePartitioning.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294\u003c\/p\u003e \u003cp\u003eQuicksort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302\u003c\/p\u003e \u003cp\u003eRadix Sort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320\u003c\/p\u003e \u003cp\u003eTimsort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e8 Binary Trees . . .  335 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eWhy Use Binary Trees?.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335\u003c\/p\u003e \u003cp\u003eTree Terminology.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337\u003c\/p\u003e \u003cp\u003eAn Analogy.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340\u003c\/p\u003e \u003cp\u003eHow Do Binary Search Trees Work?.. . . . . . . . . . . . . . . . . . . . . . . . . . 341\u003c\/p\u003e \u003cp\u003eFinding a Node.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346\u003c\/p\u003e \u003cp\u003eInserting a Node.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350\u003c\/p\u003e \u003cp\u003eTraversing the Tree.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353\u003c\/p\u003e \u003cp\u003eFinding Minimum and Maximum Key Values. . . . . . . . . . . . . . . . . . . 365\u003c\/p\u003e \u003cp\u003eDeleting a Node.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366\u003c\/p\u003e \u003cp\u003eThe Efficiency of Binary Search Trees.. . . . . . . . . . . . . . . . . . . . . . . . . 375\u003c\/p\u003e \u003cp\u003eTrees Represented as Arrays.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377\u003c\/p\u003e \u003cp\u003ePrinting Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379\u003c\/p\u003e \u003cp\u003eDuplicate Keys.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381\u003c\/p\u003e \u003cp\u003eThe BinarySearchTreeTester.py Program. . . . . . . . . . . . . . . . . . . . . . . . 382\u003c\/p\u003e \u003cp\u003eThe Huffman Code.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 9 2-3-4 Trees and External Storage  . . . 401\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to 2-3-4 Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401\u003c\/p\u003e \u003cp\u003eThe Tree234 Visualization Tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408\u003c\/p\u003e \u003cp\u003ePython Code for a 2-3-4 Tree.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412\u003c\/p\u003e \u003cp\u003eEfficiency of 2-3-4 Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430\u003c\/p\u003e \u003cp\u003e2-3 Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432\u003c\/p\u003e \u003cp\u003eExternal Storage.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e10 AVL and Red-Black Trees 463 Our Approach to the Discussion.. . . 463\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eBalanced and Unbalanced Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464\u003c\/p\u003e \u003cp\u003eAVL Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470\u003c\/p\u003e \u003cp\u003eThe Efficiency of AVL Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486\u003c\/p\u003e \u003cp\u003eRed-Black Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487\u003c\/p\u003e \u003cp\u003eUsing the Red-Black Tree Visualization Tool.. . . . . . . . . . . . . . . . . . . . 489\u003c\/p\u003e \u003cp\u003eExperimenting with the Visualization Tool.. . . . . . . . . . . . . . . . . . . . . 492\u003c\/p\u003e \u003cp\u003eRotations in Red-Black Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497\u003c\/p\u003e \u003cp\u003eInserting a New Node.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498\u003c\/p\u003e \u003cp\u003eDeletion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508\u003c\/p\u003e \u003cp\u003eThe Efficiency of Red-Black Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 509\u003c\/p\u003e \u003cp\u003e2-3-4 Trees and Red-Black Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510\u003c\/p\u003e \u003cp\u003eRed-Black Tree Implementation.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 11 Hash Tables 525 Introduction to Hashing.. . . . . . . . . . . . . . . . . 526\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eOpen Addressing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536\u003c\/p\u003e \u003cp\u003eSeparate Chaining.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565\u003c\/p\u003e \u003cp\u003eHash Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575\u003c\/p\u003e \u003cp\u003eHashing Efficiency.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581\u003c\/p\u003e \u003cp\u003eHashing and External Storage.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e12 Spatial Data Structures 597 Spatial Data.. . . . . .. . . . . .  . . . . . . 597 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eComputing Distances Between Points.. . . . . . . . . . . . . . . . . . . . . . . . . 599\u003c\/p\u003e \u003cp\u003eCircles and Bounding Boxes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601\u003c\/p\u003e \u003cp\u003eSearching Spatial Data.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611\u003c\/p\u003e \u003cp\u003eLists of Points.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612\u003c\/p\u003e \u003cp\u003eGrids.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617\u003c\/p\u003e \u003cp\u003eQuadtrees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633\u003c\/p\u003e \u003cp\u003eTheoretical Performance and Optimizations.. . . . . . . . . . . . . . . . . . . . 656\u003c\/p\u003e \u003cp\u003ePractical Considerations.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656\u003c\/p\u003e \u003cp\u003eFurther Extensions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e13 Heaps 665 Introduction to Heaps.. . . . . . . . . . . . . . . . . . . .. . . . . 666 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThe Heap Visualization Tool.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674\u003c\/p\u003e \u003cp\u003ePython Code for Heaps.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677\u003c\/p\u003e \u003cp\u003eA Tree-Based Heap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684\u003c\/p\u003e \u003cp\u003eHeapsort.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686\u003c\/p\u003e \u003cp\u003eOrder Statistics.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e14 Graphs 705 Introduction to Graphs.. . . . . . .  . . . . . . . . . . . . . . . . 705 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eTraversal and Search.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718\u003c\/p\u003e \u003cp\u003eMinimum Spanning Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734\u003c\/p\u003e \u003cp\u003eTopological Sorting.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740\u003c\/p\u003e \u003cp\u003eConnectivity in Directed Graphs.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 753\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e15 Weighted Graphs  . . . 767 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eMinimum Spanning Tree with Weighted Graphs.. . . . . . . . . . . . . . . . . 767\u003c\/p\u003e \u003cp\u003eThe All-Pairs Shortest-Path Problem.. . . . . . . . . . . . . . . . . . . . . . . . . . 797\u003c\/p\u003e \u003cp\u003eEfficiency.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 800\u003c\/p\u003e \u003cp\u003eIntractable Problems.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801\u003c\/p\u003e \u003cp\u003eSummary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805\u003c\/p\u003e \u003cp\u003eQuestions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806\u003c\/p\u003e \u003cp\u003eExperiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 808\u003c\/p\u003e \u003cp\u003eProgramming Projects.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 16 What to Use and Why 813 Analyzing the Problem.. . . . . .  . . . . . 814\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eFoundational Data Structures.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818\u003c\/p\u003e \u003cp\u003eSpecial-Ordering Data Structures.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 824\u003c\/p\u003e \u003cp\u003eSorting.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826\u003c\/p\u003e \u003cp\u003eSpecialty Data Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 828\u003c\/p\u003e \u003cp\u003eExternal Storage.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829\u003c\/p\u003e \u003cp\u003eOnward. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eA Running the Visualizations  . . . 833 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eB Further Reading . . .  841 \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eC Answers to Questions  . . . 845\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e 9780134855684, TOC, 8\/3\/2022\u003c\/strong\u003e\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864172081495,"sku":"9780134855684","price":49.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780134855684.jpg?v=1722270729"},{"product_id":"once-upon-an-algorithm-9780262545297","title":"Once Upon an Algorithm","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"MIT Press","offers":[{"title":"Default Title","offer_id":48864306725207,"sku":"9780262545297","price":19.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262545297.jpg?v=1722271327"},{"product_id":"algorithms-9780321573513","title":"Algorithms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp style=\"MARGIN:\"\u003e\u003cb\u003eRobert Sedgewick\u003c\/b\u003e has been a Professor of Computer Science at Princeton University since 1985, where he was the founding Chairman of the Department of Computer Science. He has held visiting research positions at Xerox PARC, Institute for Defense Analyses, and INRIA, and is member of the board of directors of Adobe Systems. Professor Sedgewick's research interests include analytic combinatorics, design and analysis of data structures and algorithms, and program visualization. His landmark book, \u003ci\u003eAlgorithms,\u003c\/i\u003e now in its fourth edition, has appeared in numerous versions and languages over the past thirty years. In addition, with Kevin Wayne, he is the coauthor of the highly acclaimed textbook, \u003ci\u003eIntroduction to Programming in Java: An Interdisciplinary Approach \u003c\/i\u003e(Addison-Wesley, 2008).\u003c\/p\u003e \u003cp style=\"MARGIN:\"\u003e \u003c\/p\u003e \u003cp style=\"MARGIN:\"\u003e\u003cb\u003eKevin Wayne \u003c\/b\u003eis the Phillip Y. Goldman Senior Lecturer in Computer Science at Princeton University, where h\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e    \u003cb\u003eChapter 1: Fundamentals \u003c\/b\u003e  \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e  \u003c\/b\u003e\u003cb\u003e  1.1 Programming Model  \u003c\/b\u003e  \u003cb\u003e     \u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e  \u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e  1.2 Data Abstraction  \u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e  1.3 Queues, Stacks, and Bags  \u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e  1.4 Analysis of Algorithms  \u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e  1.5 Case Study: Union-Find  \u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e     \u003c\/b\u003e \u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e  \u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e  Chapter 2: Sorting  \u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e  2.1 Elementary Sorts  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e 2.1 Elementary Sorts \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e  2.2 Mergesort  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e  2.3 Quicksort  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e  2.4 Priority Queues  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e  2.5 Applications  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e     \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Chapter 3: Searching  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  3.1 Symbol Tables  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e 3.1 Symbol Tables \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  3.2 Binary Search Trees  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  3.3 Balanced Search Trees  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  3.4 Hash Tables  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  3.5 Applications  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e     \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Chapter 4: Graphs  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  4.1 Undirected graphs  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e 4.1 Undirected graphs \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  4.2 Directed graphs  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  4.3 Minimum Spanning Trees  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  4.4 Shortest Paths  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e     \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Chapter 5: Strings  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  5.1 String Sorts  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e 5.1 String Sorts \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  5.2 Tries  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  5.3 Substring Search  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  5.4 Regular Expressions  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  5.5 Data Compression  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e     \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Context  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Systems Programming  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e Systems Programming \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Scientific Computing  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Commercial Applications  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Operations Research  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e  Intractability  \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e  \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e     \u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003ci\u003e \u003c\/i\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003ci\u003e  \u003c\/i\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003ci\u003e  Index  \u003c\/i\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e \u003c\/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003cb\u003e\u003ci\u003e\u003ci\u003e \u003c\/i\u003e \u003c\/i\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/b\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864419381591,"sku":"9780321573513","price":59.84,"currency_code":"GBP","in_stock":true}]},{"product_id":"trading-at-the-speed-of-light-9780691211381","title":"Trading at the Speed of Light","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Winner of the Bronze Medal in Business Technology, Axiom Business Book Awards\"\u003cbr\u003e\"I loved this book. . . . \u003ci\u003eTrading at the Speed of Light\u003c\/i\u003e is an amazing, detailed account of why material reality matters for virtual outcomes, and conversely, in the financial markets. Everybody with the slightest interest in modern finance should read it.\"\u003cb\u003e---Diane Coyle, \u003ci\u003eEnlightened Economist\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865548206423,"sku":"9780691211381","price":29.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691211381.jpg?v=1722274503"},{"product_id":"metaheuristic-and-evolutionary-algorithms-for-engineering-optimization-9781119386995","title":"Metaheuristic and Evolutionary Algorithms for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.\u003c\/p\u003e \u003cp\u003eChapter 1 of\u003ci\u003eMeta-heuristic and Evolutionary Algorithms for Engineering Optimization\u003c\/i\u003eprovides an overview of optimization and defines it by presenting examples of optimization problems i\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAbout the Authors xvii\u003c\/p\u003e \u003cp\u003eList of Figures xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview of Optimization 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 1\u003c\/p\u003e \u003cp\u003e1.1 Optimization 1\u003c\/p\u003e \u003cp\u003e1.1.1 Objective Function 2\u003c\/p\u003e \u003cp\u003e1.1.2 Decision Variables 2\u003c\/p\u003e \u003cp\u003e1.1.3 Solutions of an Optimization Problem 3\u003c\/p\u003e \u003cp\u003e1.1.4 Decision Space 3\u003c\/p\u003e \u003cp\u003e1.1.5 Constraints or Restrictions 3\u003c\/p\u003e \u003cp\u003e1.1.6 State Variables 3\u003c\/p\u003e \u003cp\u003e1.1.7 Local and Global Optima 4\u003c\/p\u003e \u003cp\u003e1.1.8 Near-Optimal Solutions 5\u003c\/p\u003e \u003cp\u003e1.1.9 Simulation 6\u003c\/p\u003e \u003cp\u003e1.2 Examples of the Formulation of Various Engineering Optimization Problems 7\u003c\/p\u003e \u003cp\u003e1.2.1 Mechanical Design 7\u003c\/p\u003e \u003cp\u003e1.2.2 Structural Design 9\u003c\/p\u003e \u003cp\u003e1.2.3 Electrical Engineering Optimization 10\u003c\/p\u003e \u003cp\u003e1.2.4 Water Resources Optimization 11\u003c\/p\u003e \u003cp\u003e1.2.5 Calibration of Hydrologic Models 13\u003c\/p\u003e \u003cp\u003e1.3 Conclusion 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction to Meta\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eHeuristic and Evolutionary Algorithms 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 17\u003c\/p\u003e \u003cp\u003e2.1 Searching the Decision Space for Optimal Solutions 17\u003c\/p\u003e \u003cp\u003e2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms 21\u003c\/p\u003e \u003cp\u003e2.2.1 Initial State 21\u003c\/p\u003e \u003cp\u003e2.2.2 Iterations 21\u003c\/p\u003e \u003cp\u003e2.2.3 Final State 21\u003c\/p\u003e \u003cp\u003e2.2.4 Initial Data (Information) 21\u003c\/p\u003e \u003cp\u003e2.2.5 Decision Variables 22\u003c\/p\u003e \u003cp\u003e2.2.6 State Variables 23\u003c\/p\u003e \u003cp\u003e2.2.7 Objective Function 23\u003c\/p\u003e \u003cp\u003e2.2.8 Simulation Model 24\u003c\/p\u003e \u003cp\u003e2.2.9 Constraints 24\u003c\/p\u003e \u003cp\u003e2.2.10 Fitness Function 24\u003c\/p\u003e \u003cp\u003e2.3 Principles of Meta-Heuristic and Evolutionary Algorithms 25\u003c\/p\u003e \u003cp\u003e2.4 Classification of Meta-Heuristic and Evolutionary Algorithms 27\u003c\/p\u003e \u003cp\u003e2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms 27\u003c\/p\u003e \u003cp\u003e2.4.2 Population-Based and Single-Point Search Algorithms 28\u003c\/p\u003e \u003cp\u003e2.4.3 Memory-Based and Memory-Less Algorithms 28\u003c\/p\u003e \u003cp\u003e2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains 28\u003c\/p\u003e \u003cp\u003e2.6 Generating Random Values of the Decision Variables 29\u003c\/p\u003e \u003cp\u003e2.7 Dealing with Constraints 29\u003c\/p\u003e \u003cp\u003e2.7.1 Removal Method 30\u003c\/p\u003e \u003cp\u003e2.7.2 Refinement Method 30\u003c\/p\u003e \u003cp\u003e2.7.3 Penalty Functions 31\u003c\/p\u003e \u003cp\u003e2.8 Fitness Function 33\u003c\/p\u003e \u003cp\u003e2.9 Selection of Solutions in Each Iteration 33\u003c\/p\u003e \u003cp\u003e2.10 Generating New Solutions 34\u003c\/p\u003e \u003cp\u003e2.11 The Best Solution in Each Algorithmic Iteration 35\u003c\/p\u003e \u003cp\u003e2.12 Termination Criteria 35\u003c\/p\u003e \u003cp\u003e2.13 General Algorithm 36\u003c\/p\u003e \u003cp\u003e2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms 36\u003c\/p\u003e \u003cp\u003e2.15 Search Strategies 39\u003c\/p\u003e \u003cp\u003e2.16 Conclusion 41\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Pattern Search 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 43\u003c\/p\u003e \u003cp\u003e3.1 Introduction 43\u003c\/p\u003e \u003cp\u003e3.2 Pattern Search (PS) Fundamentals 44\u003c\/p\u003e \u003cp\u003e3.3 Generating an Initial Solution 47\u003c\/p\u003e \u003cp\u003e3.4 Generating Trial Solutions 47\u003c\/p\u003e \u003cp\u003e3.4.1 Exploratory Move 47\u003c\/p\u003e \u003cp\u003e3.4.2 Pattern Move 49\u003c\/p\u003e \u003cp\u003e3.5 Updating the Mesh Size 50\u003c\/p\u003e \u003cp\u003e3.6 Termination Criteria 50\u003c\/p\u003e \u003cp\u003e3.7 User-Defined Parameters of the PS 51\u003c\/p\u003e \u003cp\u003e3.8 Pseudocode of the PS 51\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 52\u003c\/p\u003e \u003cp\u003eReferences 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Genetic Algorithm 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 53\u003c\/p\u003e \u003cp\u003e4.1 Introduction 53\u003c\/p\u003e \u003cp\u003e4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution 54\u003c\/p\u003e \u003cp\u003e4.3 Creating an Initial Population 56\u003c\/p\u003e \u003cp\u003e4.4 Selection of Parents to Create a New Generation 56\u003c\/p\u003e \u003cp\u003e4.4.1 Proportionate Selection 57\u003c\/p\u003e \u003cp\u003e4.4.2 Ranking Selection 58\u003c\/p\u003e \u003cp\u003e4.4.3 Tournament Selection 59\u003c\/p\u003e \u003cp\u003e4.5 Population Diversity and Selective Pressure 59\u003c\/p\u003e \u003cp\u003e4.6 Reproduction 59\u003c\/p\u003e \u003cp\u003e4.6.1 Crossover 60\u003c\/p\u003e \u003cp\u003e4.6.2 Mutation 62\u003c\/p\u003e \u003cp\u003e4.7 Termination Criteria 63\u003c\/p\u003e \u003cp\u003e4.8 User- Defined Parameters of the GA 63\u003c\/p\u003e \u003cp\u003e4.9 Pseudocode of the GA 64\u003c\/p\u003e \u003cp\u003e4.10 Conclusion 65\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Simulated Annealing 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 69\u003c\/p\u003e \u003cp\u003e5.1 Introduction 69\u003c\/p\u003e \u003cp\u003e5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process 70\u003c\/p\u003e \u003cp\u003e5.3 Generating an Initial State 72\u003c\/p\u003e \u003cp\u003e5.4 Generating a New State 72\u003c\/p\u003e \u003cp\u003e5.5 Acceptance Function 74\u003c\/p\u003e \u003cp\u003e5.6 Thermal Equilibrium 75\u003c\/p\u003e \u003cp\u003e5.7 Temperature Reduction 75\u003c\/p\u003e \u003cp\u003e5.8 Termination Criteria 76\u003c\/p\u003e \u003cp\u003e5.9 User- Defined Parameters of the SA 76\u003c\/p\u003e \u003cp\u003e5.10 Pseudocode of the SA 77\u003c\/p\u003e \u003cp\u003e5.11 Conclusion 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Tabu Search 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 79\u003c\/p\u003e \u003cp\u003e6.1 Introduction 79\u003c\/p\u003e \u003cp\u003e6.2 Tabu Search (TS) Foundation 80\u003c\/p\u003e \u003cp\u003e6.3 Generating an Initial Searching Point 82\u003c\/p\u003e \u003cp\u003e6.4 Neighboring Points 82\u003c\/p\u003e \u003cp\u003e6.5 Tabu Lists 84\u003c\/p\u003e \u003cp\u003e6.6 Updating the Tabu List 84\u003c\/p\u003e \u003cp\u003e6.7 Attributive Memory 85\u003c\/p\u003e \u003cp\u003e6.7.1 Frequency-Based Memory 85\u003c\/p\u003e \u003cp\u003e6.7.2 Recency-Based Memory 85\u003c\/p\u003e \u003cp\u003e6.8 Aspiration Criteria 87\u003c\/p\u003e \u003cp\u003e6.9 Intensification and Diversification Strategies 87\u003c\/p\u003e \u003cp\u003e6.10 Termination Criteria 87\u003c\/p\u003e \u003cp\u003e6.11 User- Defined Parameters of the TS 87\u003c\/p\u003e \u003cp\u003e6.12 Pseudocode of the TS 88\u003c\/p\u003e \u003cp\u003e6.13 Conclusion 89\u003c\/p\u003e \u003cp\u003eReferences 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Ant Colony Optimization 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 91\u003c\/p\u003e \u003cp\u003e7.1 Introduction 91\u003c\/p\u003e \u003cp\u003e7.2 Mapping Ant Colony Optimization (ACO) to Ants’ Foraging Behavior 92\u003c\/p\u003e \u003cp\u003e7.3 Creating an Initial Population 94\u003c\/p\u003e \u003cp\u003e7.4 Allocating Pheromone to the Decision Space 96\u003c\/p\u003e \u003cp\u003e7.5 Generation of New Solutions 98\u003c\/p\u003e \u003cp\u003e7.6 Termination Criteria 99\u003c\/p\u003e \u003cp\u003e7.7 User- Defined Parameters of the ACO 99\u003c\/p\u003e \u003cp\u003e7.8 Pseudocode of the ACO 100\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 100\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Particle Swarm Optimization 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 103\u003c\/p\u003e \u003cp\u003e8.1 Introduction 103\u003c\/p\u003e \u003cp\u003e8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals 104\u003c\/p\u003e \u003cp\u003e8.3 Creating an Initial Population of Particles 107\u003c\/p\u003e \u003cp\u003e8.4 The Individual and Global Best Positions 107\u003c\/p\u003e \u003cp\u003e8.5 Velocities of Particles 109\u003c\/p\u003e \u003cp\u003e8.6 Updating the Positions of Particles 110\u003c\/p\u003e \u003cp\u003e8.7 Termination Criteria 110\u003c\/p\u003e \u003cp\u003e8.8 User- Defined Parameters of the PSO 110\u003c\/p\u003e \u003cp\u003e8.9 Pseudocode of the PSO 111\u003c\/p\u003e \u003cp\u003e8.10 Conclusion 112\u003c\/p\u003e \u003cp\u003eReferences 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Differential Evolution 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 115\u003c\/p\u003e \u003cp\u003e9.1 Introduction 115\u003c\/p\u003e \u003cp\u003e9.2 Differential Evolution (DE) Fundamentals 116\u003c\/p\u003e \u003cp\u003e9.3 Creating an Initial Population 118\u003c\/p\u003e \u003cp\u003e9.4 Generating Trial Solutions 119\u003c\/p\u003e \u003cp\u003e9.4.1 Mutation 119\u003c\/p\u003e \u003cp\u003e9.4.2 Crossover 119\u003c\/p\u003e \u003cp\u003e9.5 Greedy Criteria 120\u003c\/p\u003e \u003cp\u003e9.6 Termination Criteria 120\u003c\/p\u003e \u003cp\u003e9.7 User-Defined Parameters of the DE 120\u003c\/p\u003e \u003cp\u003e9.8 Pseudocode of the DE 121\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 121\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Harmony Search 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 123\u003c\/p\u003e \u003cp\u003e10.1 Introduction 123\u003c\/p\u003e \u003cp\u003e10.2 Inspiration of the Harmony Search (HS) 124\u003c\/p\u003e \u003cp\u003e10.3 Initializing the Harmony Memory 125\u003c\/p\u003e \u003cp\u003e10.4 Generating New Harmonies (Solutions) 127\u003c\/p\u003e \u003cp\u003e10.4.1 Memory Strategy 127\u003c\/p\u003e \u003cp\u003e10.4.2 Random Selection 128\u003c\/p\u003e \u003cp\u003e10.4.3 Pitch Adjustment 129\u003c\/p\u003e \u003cp\u003e10.5 Updating the Harmony Memory 129\u003c\/p\u003e \u003cp\u003e10.6 Termination Criteria 130\u003c\/p\u003e \u003cp\u003e10.7 User- Defined Parameters of the HS 130\u003c\/p\u003e \u003cp\u003e10.8 Pseudocode of the HS 130\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 131\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Shuffled Frog\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eLeaping Algorithm 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 133\u003c\/p\u003e \u003cp\u003e11.1 Introduction 133\u003c\/p\u003e \u003cp\u003e11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA) 134\u003c\/p\u003e \u003cp\u003e11.3 Creating an Initial Population 137\u003c\/p\u003e \u003cp\u003e11.4 Classifying Frogs into Memeplexes 137\u003c\/p\u003e \u003cp\u003e11.5 Frog Leaping 138\u003c\/p\u003e \u003cp\u003e11.6 Shuffling Process 140\u003c\/p\u003e \u003cp\u003e11.7 Termination Criteria 141\u003c\/p\u003e \u003cp\u003e11.8 User-Defined Parameters of the SFLA 141\u003c\/p\u003e \u003cp\u003e11.9 Pseudocode of the SFLA 141\u003c\/p\u003e \u003cp\u003e11.10 Conclusion 142\u003c\/p\u003e \u003cp\u003eReferences 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Honey\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eBee Mating Optimization 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 145\u003c\/p\u003e \u003cp\u003e12.1 Introduction 145\u003c\/p\u003e \u003cp\u003e12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey- Bee Colony Structure 146\u003c\/p\u003e \u003cp\u003e12.3 Creating an Initial Population 148\u003c\/p\u003e \u003cp\u003e12.4 The Queen 150\u003c\/p\u003e \u003cp\u003e12.5 Drone Selection 150\u003c\/p\u003e \u003cp\u003e12.5.1 Mating Flights 151\u003c\/p\u003e \u003cp\u003e12.5.2 Trial Solutions 152\u003c\/p\u003e \u003cp\u003e12.6 Brood (New Solution) Production 152\u003c\/p\u003e \u003cp\u003e12.7 Improving Broods (New Solutions) by Workers 155\u003c\/p\u003e \u003cp\u003e12.8 Termination Criteria 156\u003c\/p\u003e \u003cp\u003e12.9 User-Defined Parameters of the HBMO 156\u003c\/p\u003e \u003cp\u003e12.10 Pseudocode of the HBMO 156\u003c\/p\u003e \u003cp\u003e12.11 Conclusion 158\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Invasive Weed Optimization 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 163\u003c\/p\u003e \u003cp\u003e13.1 Introduction 163\u003c\/p\u003e \u003cp\u003e13.2 Mapping Invasive Weed Optimization (IWO) to Weeds’ Biology 164\u003c\/p\u003e \u003cp\u003e13.3 Creating an Initial Population 167\u003c\/p\u003e \u003cp\u003e13.4 Reproduction 167\u003c\/p\u003e \u003cp\u003e13.5 The Spread of Seeds 168\u003c\/p\u003e \u003cp\u003e13.6 Eliminating Weeds with Low Fitness 169\u003c\/p\u003e \u003cp\u003e13.7 Termination Criteria 170\u003c\/p\u003e \u003cp\u003e13.8 User- Defined Parameters of the IWO 170\u003c\/p\u003e \u003cp\u003e13.9 Pseudocode of the IWO 170\u003c\/p\u003e \u003cp\u003e13.10 Conclusion 171\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Central Force Optimization 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 175\u003c\/p\u003e \u003cp\u003e14.1 Introduction 175\u003c\/p\u003e \u003cp\u003e14.2 Mapping Central Force Optimization (CFO) to Newtons Gravitational Law 176\u003c\/p\u003e \u003cp\u003e14.3 Initializing the Position of Probes 177\u003c\/p\u003e \u003cp\u003e14.4 Calculation of Accelerations 180\u003c\/p\u003e \u003cp\u003e14.5 Movement of Probes 181\u003c\/p\u003e \u003cp\u003e14.6 Modification of Deviated Probes 181\u003c\/p\u003e \u003cp\u003e14.7 Termination Criteria 182\u003c\/p\u003e \u003cp\u003e14.8 User-Defined Parameters of the CFO 182\u003c\/p\u003e \u003cp\u003e14.9 Pseudocode of the CFO 183\u003c\/p\u003e \u003cp\u003e14.10 Conclusion 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Biogeography\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eBased Optimization 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 185\u003c\/p\u003e \u003cp\u003e15.1 Introduction 185\u003c\/p\u003e \u003cp\u003e15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts 186\u003c\/p\u003e \u003cp\u003e15.3 Creating an Initial Population 188\u003c\/p\u003e \u003cp\u003e15.4 Migration Process 189\u003c\/p\u003e \u003cp\u003e15.5 Mutation 191\u003c\/p\u003e \u003cp\u003e15.6 Termination Criteria 192\u003c\/p\u003e \u003cp\u003e15.7 User- Defined Parameters of the BBO 192\u003c\/p\u003e \u003cp\u003e15.8 Pseudocode of the BBO 193\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 193\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Firefly Algorithm 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 195\u003c\/p\u003e \u003cp\u003e16.1 Introduction 195\u003c\/p\u003e \u003cp\u003e16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies 196\u003c\/p\u003e \u003cp\u003e16.3 Creating an Initial Population 198\u003c\/p\u003e \u003cp\u003e16.4 Attractiveness 199\u003c\/p\u003e \u003cp\u003e16.5 Distance and Movement 199\u003c\/p\u003e \u003cp\u003e16.6 Termination Criteria 200\u003c\/p\u003e \u003cp\u003e16.7 User-Defined Parameters of the FA 200\u003c\/p\u003e \u003cp\u003e16.8 Pseudocode of the FA 201\u003c\/p\u003e \u003cp\u003e16.9 Conclusion 201\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Gravity Search Algorithm 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 203\u003c\/p\u003e \u003cp\u003e17.1 Introduction 203\u003c\/p\u003e \u003cp\u003e17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity 204\u003c\/p\u003e \u003cp\u003e17.3 Creating an Initial Population 205\u003c\/p\u003e \u003cp\u003e17.4 Evaluation of Particle Masses 207\u003c\/p\u003e \u003cp\u003e17.5 UpdatingVelocities and Positions 207\u003c\/p\u003e \u003cp\u003e17.6 Updating Newton’s Gravitational Factor 208\u003c\/p\u003e \u003cp\u003e17.7 Termination Criteria 209\u003c\/p\u003e \u003cp\u003e17.8 User- Defined Parameters of the GSA 209\u003c\/p\u003e \u003cp\u003e17.9 Pseudocode of the GSA 209\u003c\/p\u003e \u003cp\u003e17.10 Conclusion 210\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Bat Algorithm 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 213\u003c\/p\u003e \u003cp\u003e18.1 Introduction 213\u003c\/p\u003e \u003cp\u003e18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats 214\u003c\/p\u003e \u003cp\u003e18.3 Creating an Initial Population 215\u003c\/p\u003e \u003cp\u003e18.4 Movement of Virtual Bats 217\u003c\/p\u003e \u003cp\u003e18.5 Local Search and Random Flying 218\u003c\/p\u003e \u003cp\u003e18.6 Loudness and Pulse Emission 218\u003c\/p\u003e \u003cp\u003e18.7 Termination Criteria 219\u003c\/p\u003e \u003cp\u003e18.8 User-Defined Parameters of the BA 219\u003c\/p\u003e \u003cp\u003e18.9 Pseudocode of the BA 219\u003c\/p\u003e \u003cp\u003e18.10 Conclusion 220\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Plant Propagation Algorithm 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 223\u003c\/p\u003e \u003cp\u003e19.1 Introduction 223\u003c\/p\u003e \u003cp\u003e19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA) 223\u003c\/p\u003e \u003cp\u003e19.3 Creating an Initial Population of Plants 226\u003c\/p\u003e \u003cp\u003e19.4 Normalizing the Fitness Function 226\u003c\/p\u003e \u003cp\u003e19.5 Propagation 227\u003c\/p\u003e \u003cp\u003e19.6 Elimination of Extra Solutions 228\u003c\/p\u003e \u003cp\u003e19.7 Termination Criteria 228\u003c\/p\u003e \u003cp\u003e19.8 User-Defined Parameters of the PPA 228\u003c\/p\u003e \u003cp\u003e19.9 Pseudocode of the PPA 229\u003c\/p\u003e \u003cp\u003e19.10 Conclusion 230\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Water Cycle Algorithm 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 231\u003c\/p\u003e \u003cp\u003e20.1 Introduction 231\u003c\/p\u003e \u003cp\u003e20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle 232\u003c\/p\u003e \u003cp\u003e20.3 Creating an Initial Population 233\u003c\/p\u003e \u003cp\u003e20.4 Classification of Raindrops 235\u003c\/p\u003e \u003cp\u003e20.5 Streams Flowing to the Rivers or Sea 236\u003c\/p\u003e \u003cp\u003e20.6 Evaporation 237\u003c\/p\u003e \u003cp\u003e20.7 Raining Process 238\u003c\/p\u003e \u003cp\u003e20.8 Termination Criteria 239\u003c\/p\u003e \u003cp\u003e20.9 User-Defined Parameters of the WCA 239\u003c\/p\u003e \u003cp\u003e20.10 Pseudocode of the WCA 239\u003c\/p\u003e \u003cp\u003e20.11 Conclusion 240\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Symbiotic Organisms Search 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 241\u003c\/p\u003e \u003cp\u003e21.1 Introduction 241\u003c\/p\u003e \u003cp\u003e21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS) 241\u003c\/p\u003e \u003cp\u003e21.3 Creating an Initial Ecosystem 242\u003c\/p\u003e \u003cp\u003e21.4 Mutualism 244\u003c\/p\u003e \u003cp\u003e21.5 Commensalism 245\u003c\/p\u003e \u003cp\u003e21.6 Parasitism 245\u003c\/p\u003e \u003cp\u003e21.7 Termination Criteria 246\u003c\/p\u003e \u003cp\u003e21.8 Pseudocode of the SOS 246\u003c\/p\u003e \u003cp\u003e21.9 Conclusion 247\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Comprehensive Evolutionary Algorithm 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 249\u003c\/p\u003e \u003cp\u003e22.1 Introduction 249\u003c\/p\u003e \u003cp\u003e22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA) 250\u003c\/p\u003e \u003cp\u003e22.3 Generating an Initial Population of Solutions 253\u003c\/p\u003e \u003cp\u003e22.4 Selection 253\u003c\/p\u003e \u003cp\u003e22.5 Reproduction 255\u003c\/p\u003e \u003cp\u003e22.5.1 Crossover Operators 255\u003c\/p\u003e \u003cp\u003e22.5.2 Mutation Operators 261\u003c\/p\u003e \u003cp\u003e22.6 Roles of Operators 262\u003c\/p\u003e \u003cp\u003e22.7 Input Data to the CEA 263\u003c\/p\u003e \u003cp\u003e22.8 Termination Criteria 264\u003c\/p\u003e \u003cp\u003e22.9 Pseudocode of the CEA 265\u003c\/p\u003e \u003cp\u003e22.10 Conclusion 265\u003c\/p\u003e \u003cp\u003eReferences 266\u003c\/p\u003e \u003cp\u003eWiley Series in Operations Research and Management Science 267\u003c\/p\u003e \u003cp\u003eIndex 269\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866394014039,"sku":"9781119386995","price":106.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119386995.jpg?v=1722278442"},{"product_id":"ocp-oracle-certified-professional-java-se-17-developer-practice-tests-9781119864615","title":"OCP Oracle Certified Professional Java SE 17","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduction xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1 Handling Date, Time, Text, Numeric and Boolean Values 1\u003c\/p\u003e \u003cp\u003eChapter 2 Controlling Program Flow 25\u003c\/p\u003e \u003cp\u003eChapter 3 Utilizing Java Object- Oriented Approach 45\u003c\/p\u003e \u003cp\u003eChapter 4 Handling Exceptions 149\u003c\/p\u003e \u003cp\u003eChapter 5 Working with Arrays and Collections 181\u003c\/p\u003e 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It does not explain the C++ language or syntax, but is accessible to anyone with basic C++ knowledge or programming experience. Even the most experienced C++ programmer will learn a thing or two from it and find it a useful memory-aid. \u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eIt is hard to remember all the possibilities, details, and intricacies of the vast and growing Standard Library. This handy reference guide is therefore indispensable to any C++ programmer. It offers a condensed, well-structured summary of all essential aspects of the C++ Standard Library. No page-long, repetitive examples or obscure, rarely used features. 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Madhumita Murgia, AI Editor at the \u003ci\u003eFT\u003c\/i\u003e, exposes how AI can strip away our collective and individual sense of agency – and shatter our illusion of free will.\u003cbr\u003e\u003cbr\u003eAI is already changing what it means to be human, in ways large and small. In this compelling work, Murgia reveals what could happen if we fail to reclaim our humanity.\u003cbr\u003e\u003cbr\u003e\u003cb\u003e'The intimate\u003c\/b\u003e\u003c\/p\u003e","brand":"Pan Macmillan","offers":[{"title":"Default Title","offer_id":48867502784855,"sku":"9781529097313","price":15.29,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781529097313.jpg?v=1722283587"}],"url":"https:\/\/bookcurl.com\/collections\/algorithms-and-data-structures.oembed?page=11","provider":"Book Curl","version":"1.0","type":"link"}