Products related to Computation:
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Mathematics and Computation : A Theory Revolutionizing Technology and Science
From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophyMathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation.With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges.He explains the ideas and motivations leading to key models, notions, and results.In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences.Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics.He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society.For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields.Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyondHigh-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific disciplineHistorical accounts of the evolution and motivations of central concepts and modelsA broad view of the theory of computation's influence on science, technology, and societyExtensive bibliography
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Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.
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Turbulent Flow Computation
In various branches of fluid mechanics, our understanding is inhibited by the presence of turbulence.Although many experimental and theoretical studies have significantly helped to increase our physical understanding, a comp- hensive and predictive theory of turbulent flows has not yet been established.Therefore, the prediction of turbulent flow relies heavily on simulation stra- gies.The development of reliable methods for turbulent flow computation will have a significant impact on a variety of technological advancements.These range from aircraft and car design, to turbomachinery, combustors, and process engineering.Moreover, simulation approaches are important in materials - sign, prediction of biologically relevant flows, and also significantly contribute to the understanding of environmental processes including weather and climate forecasting.The material that is compiled in this book presents a coherent account of contemporary computational approaches for turbulent flows.It aims to p- vide the reader with information about the current state of the art as well as to stimulate directions for future research and development.The book puts part- ular emphasis on computational methods for incompressible and compressible turbulent flows as well as on methods for analysing and quantifying nume- cal errors in turbulent flow computations.In addition, it presents turbulence modelling approaches in the context of large eddy simulation, and unfolds the challenges in the field of simulations for multiphase flows and computational fluid dynamics (CFD) of engineering flows in complex geometries.Apart from reviewing main research developments, new material is also included in many of the chapters.
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Linear Programming Computation
This monograph represents a historic breakthrough in the field of linear programming (LP)since George Dantzig first discovered the simplex method in 1947. Being both thoughtful and informative, it focuses on reflecting and promoting the state of the art by highlighting new achievements in LP.This new edition is organized in two volumes. The first volume addresses foundations of LP, including the geometry of feasible region, the simplex method and its implementation, duality and the dual simplex method, the primal-dual simplex method, sensitivity analysis and parametric LP, the generalized simplex method, the decomposition method, the interior-point method and integer LP method.The second volume mainly introduces contributions of the author himself, such as efficient primal/dual pivot rules, primal/dual Phase-I methods, reduced/D-reduced simplex methods, the generalized reduced simplex method, primal/dual deficient-basis methods, primal/dual face methods, a new decomposition principle, etc. Many important improvements were made in this edition.The first volume includes new results, such as the mixed two-phase simplex algorithm, dual elimination, fresh pricing scheme for reduced cost, bilevel LP models and intercepting of optimal solution set.In particular, the chapter Integer LP Method was rewritten with great gains of the objective cutting for new ILP solvers {it controlled-cutting/branch} methods, as well as with an attractive implementation of the controlled-branch method. In the second volume, the `simplex feasible-point algorithm' was rewritten, and removed from the chapter Pivotal Interior-Point Method to form an independent chapter with the new title `Simplex Interior-Point Method', as it represents a class of efficient interior-point algorithms transformed from traditional simplex algorithms.The title of the original chapter was then changed to `Facial Interior-Point Method', as the remaining algorithms represent another class of efficient interior-point algorithms transformed from normal interior-point algorithms.Without exploiting sparsity, the original primal/dual face methods were implemented using Cholesky factorization.In order to deal with sparse computation, two new chapters discussing LU factorization were added to the second volume.The most exciting improvement came from the rediscovery of the reduced simplex method.In the first edition, the derivation of its prototype was presented in a chapter with the same title, and then converted into the so-called `improved' version in another chapter.Fortunately, the author recently found a quite concise new derivation, so he can now introduce the distinctive fresh simplex method in a single chapter.It is exciting that the reduced simplex method can be expected to be the best LP solver ever. With a focus on computation, the current edition contains many novel ideas, theories and methods, supported by solid numerical results.Being clear and succinct, its content reveals in a fresh manner, from simple to profound.In particular, a larger number of examples were worked out to demonstrate algorithms.This book is a rare work in LP and an indispensable tool for undergraduate and graduate students, teachers, practitioners, and researchers in LP and related fields.
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What is neural computation?
Neural computation refers to the process by which the brain and nervous system process and transmit information. It involves the complex interactions between neurons, which are the basic building blocks of the nervous system. Neural computation encompasses a wide range of functions, including sensory perception, motor control, learning, and memory. This field of study seeks to understand how neural networks process information and how these processes can be replicated or simulated in artificial systems.
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How does neural computation work?
Neural computation refers to the process by which the brain processes and analyzes information. It involves the transmission of electrical signals between neurons, which are specialized cells that make up the nervous system. These signals are passed through synapses, or connections between neurons, and can be either excitatory or inhibitory. The brain integrates and processes these signals to generate responses and behaviors. Overall, neural computation is a complex and dynamic process that underlies all cognitive functions and behaviors.
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How does symbolic computation work?
Symbolic computation involves manipulating mathematical expressions and equations using symbols and rules, rather than numerical values. This allows for the manipulation of complex mathematical expressions, including algebraic, trigonometric, and calculus operations, without needing to compute specific numerical values. Symbolic computation systems use algorithms and rules to perform operations such as simplification, differentiation, integration, and solving equations symbolically, providing exact solutions in terms of symbols and variables. This approach is particularly useful in mathematics, engineering, and scientific research, where exact symbolic solutions are often needed for further analysis and interpretation.
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What is neural computation 2?
Neural computation 2 refers to the process of information processing and decision-making within the brain using neural networks. It involves the complex interactions between neurons, synapses, and neurotransmitters to perform tasks such as learning, memory, and problem-solving. Neural computation 2 also encompasses the study of artificial neural networks, which are computational models inspired by the structure and function of the brain, and are used in various applications such as pattern recognition, machine learning, and artificial intelligence.
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Architecture in Digital Culture : Machines, Networks and Computation
This book examines the manifestations of architecture, cities, and design processes within digital culture. Adopting a comparative and critical method, the author looks at past and present encounters of the digital with architectural discourse and practice.Along three central themes – machines, networks, and computation – the book begins by discussing transformations of the analogy between architecture and the machine since the early twentieth century, foregrounding questions about the relations between architecture, humans, machines, and the environment.It moves on to the city, to observe how big data and smart city sustainable management systems have transformed historical visions of global networked cities.Lastly, it explores computational design thinking historically and in the context of complex systems, as well as the latest technical, social, and economic developments.Exposing possible drawbacks while still focusing on what is radically innovative, this book proposes a way toward more liberating, digital, and sustainable futures for architecture. An important read for architecture students, academics, and professionals, this book connects instances of digital architecture practice and discourse throughout the history of the digital culture paradigm and their ties with sociopolitical developments.It shares the possibility that these connecting lines may be the canvas for a novel architectural history of the recent past.
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Biomechanics : Concepts and Computation
This is the first textbook to integrates both general and specific topics, theoretical background and biomedical engineering applications, as well as analytical and numerical approaches.This quantitative approach integrates the classical concepts of mechanics and computational modelling techniques, in a logical progression through a wide range of fundamental biomechanics principles.Online MATLAB-based software along with examples and problems using biomedical applications will motivate undergraduate biomedical engineering students to practise and test their skills.The book covers topics such as kinematics, equilibrium, stresses and strains, and also focuses on large deformations and rotations and non-linear constitutive equations, including visco-elastic behaviour and the behaviour of long slender fibre-like structures.This is the definitive textbook for students.
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Statistical Modeling and Computation
This book, Statistical Modeling and Computation, provides a unique introduction to modern statistics from both classical and Bayesian perspectives.It also offers an integrated treatment of mathematical statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. The 2nd edition changes the programming language used in the text from MATLAB to Julia.For all examples with computing components, the authors provide data sets and their own Julia codes.The new edition features numerous full color graphics to illustrate the concepts discussed in the text, and adds three entirely new chapters on a variety of popular topics, including:Regularization and the Lasso regressionBayesian shrinkage methodsNonparametric statistical testsSplines and the Gaussian process regressionJoshua C.C. Chan is Professor of Economics, and holds the endowed Olson Chair at Purdue University.He is an elected fellow at the International Association for Applied Econometrics and served as Chair for the Economics, Finance and Business Section of the International Society for Bayesian Analysis from 2020-2022.His research focuses on building new high-dimensional time-series models and developing efficient estimation methods for these models.He has published over 50 papers in peer-reviewed journals, including some top-field journals such as Journal of Econometrics, Journal of the American Statistical Association and Journal of Business and Economic Statistics. Dirk Kroese is Professor of Mathematics and Statistics at the University of Queensland.He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods.He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. In addition to his scholarly contributions, Dirk Kroese is recognized for his role as an educator and mentor, having supervised and inspired numerous students and researchers.
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Mathematical Logic and Computation
This new book on mathematical logic by Jeremy Avigad gives a thorough introduction to the fundamental results and methods of the subject from the syntactic point of view, emphasizing logic as the study of formal languages and systems and their proper use.Topics include proof theory, model theory, the theory of computability, and axiomatic foundations, with special emphasis given to aspects of mathematical logic that are fundamental to computer science, including deductive systems, constructive logic, the simply typed lambda calculus, and type-theoretic foundations.Clear and engaging, with plentiful examples and exercises, it is an excellent introduction to the subject for graduate students and advanced undergraduates who are interested in logic in mathematics, computer science, and philosophy, and an invaluable reference for any practicing logician's bookshelf.
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How does neuronal computation work through summation?
Neuronal computation works through summation by integrating the input signals received from other neurons. When a neuron receives multiple inputs, these signals are combined through a process called summation. There are two types of summation: spatial summation, which involves the integration of signals from different neurons at the same time, and temporal summation, which involves the integration of signals from the same neuron over a short period of time. The combined input signals are then processed and, if the resulting signal exceeds a certain threshold, the neuron will generate an action potential, transmitting the signal to other neurons. This process allows for complex information processing and decision-making within the brain.
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What is the assignment sheet for neural computation?
The assignment sheet for neural computation typically includes a set of problems or tasks related to the study of neural networks and their computational properties. It may include exercises on topics such as neural network architecture, learning algorithms, and applications in pattern recognition or data analysis. The sheet may also provide guidelines for implementing and testing neural network models using programming languages or simulation tools. Additionally, it may outline the evaluation criteria and deadlines for the completion of the assignment.
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Can someone explain this task to me? It's about neural computation. Photo attached.
Certainly! The task in the photo appears to be related to neural computation, which is a field that studies how the brain processes information. In this task, it seems like you are required to analyze the neural network architecture depicted in the diagram and possibly identify the flow of information or the activation patterns within the network. By understanding how neural networks process information, researchers can develop more efficient algorithms for tasks such as pattern recognition, machine learning, and artificial intelligence.
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What is digital transformation?
Digital transformation is the process of integrating digital technology into all aspects of a business, fundamentally changing how it operates and delivers value to customers. It involves using technology to streamline processes, improve efficiency, and create new business models. Digital transformation is not just about adopting new tools or systems, but also about changing the mindset and culture of an organization to embrace innovation and adapt to the digital age.
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