Author: Arieh Iserles
Publisher: Cambridge University Press
ISBN: 9780521770880
Category : Computers
Languages : en
Pages : 310
Book Description
Numerical analysis is the subject of applied mathematics concerned mainly with using computers in evaluating or approximating mathematical models. As such, it is crucial to all applications of mathematics in science and engineering, as well as being an important discipline on its own. Acta Numerica surveys annually the most important developments in numerical analysis and scientific computing. The subjects and authors of the substantive survey articles are chosen by a distinguished international editorial board so as to report the most important developments in the subject in a manner accessible to the wider community of professionals with an interest in scientific computing.
Acta Numerica 1999: Volume 8
Author: Arieh Iserles
Publisher: Cambridge University Press
ISBN: 9780521770880
Category : Computers
Languages : en
Pages : 310
Book Description
Numerical analysis is the subject of applied mathematics concerned mainly with using computers in evaluating or approximating mathematical models. As such, it is crucial to all applications of mathematics in science and engineering, as well as being an important discipline on its own. Acta Numerica surveys annually the most important developments in numerical analysis and scientific computing. The subjects and authors of the substantive survey articles are chosen by a distinguished international editorial board so as to report the most important developments in the subject in a manner accessible to the wider community of professionals with an interest in scientific computing.
Publisher: Cambridge University Press
ISBN: 9780521770880
Category : Computers
Languages : en
Pages : 310
Book Description
Numerical analysis is the subject of applied mathematics concerned mainly with using computers in evaluating or approximating mathematical models. As such, it is crucial to all applications of mathematics in science and engineering, as well as being an important discipline on its own. Acta Numerica surveys annually the most important developments in numerical analysis and scientific computing. The subjects and authors of the substantive survey articles are chosen by a distinguished international editorial board so as to report the most important developments in the subject in a manner accessible to the wider community of professionals with an interest in scientific computing.
Author:
Publisher: Springer Nature
ISBN: 3031709098
Category :
Languages : en
Pages : 439
Book Description
Publisher: Springer Nature
ISBN: 3031709098
Category :
Languages : en
Pages : 439
Book Description
Acta Numerica 2008: Volume 17
Author: A. Iserles
Publisher: Cambridge University Press
ISBN: 9780521516426
Category : Mathematics
Languages : en
Pages : 424
Book Description
A high-impact, prestigious annual publication containing invited surveys by subject leaders: essential reading for all practitioners and researchers.
Publisher: Cambridge University Press
ISBN: 9780521516426
Category : Mathematics
Languages : en
Pages : 424
Book Description
A high-impact, prestigious annual publication containing invited surveys by subject leaders: essential reading for all practitioners and researchers.
Inside Out
Author: Gunther Uhlmann
Publisher: Cambridge University Press
ISBN: 9780521824699
Category : Mathematics
Languages : en
Pages : 424
Book Description
In this book, leading experts in the theoretical and applied aspects of inverse problems offer extended surveys on several important topics.
Publisher: Cambridge University Press
ISBN: 9780521824699
Category : Mathematics
Languages : en
Pages : 424
Book Description
In this book, leading experts in the theoretical and applied aspects of inverse problems offer extended surveys on several important topics.
Monte Carlo Methods for Applied Scientists
Author: Ivan Dimov
Publisher: World Scientific
ISBN: 9812779892
Category : Mathematics
Languages : en
Pages : 308
Book Description
The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer. This book attempts to bridge the gap between theory and practice concentrating on modern algorithmic implementation on parallel architecture machines. Although a suitable text for final year postgraduate mathematicians and computational scientists it is principally aimed at the applied scientists: only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithms development often to applied industrial problems. A selection of algorithms developed both for serial and parallel machines are provided. Sample Chapter(s). Chapter 1: Introduction (231 KB). Contents: Basic Results of Monte Carlo Integration; Optimal Monte Carlo Method for Multidimensional Integrals of Smooth Functions; Iterative Monte Carlo Methods for Linear Equations; Markov Chain Monte Carlo Methods for Eigenvalue Problems; Monte Carlo Methods for Boundary-Value Problems (BVP); Superconvergent Monte Carlo for Density Function Simulation by B-Splines; Solving Non-Linear Equations; Algorithmic Effciency for Different Computer Models; Applications for Transport Modeling in Semiconductors and Nanowires. Readership: Applied scientists and mathematicians.
Publisher: World Scientific
ISBN: 9812779892
Category : Mathematics
Languages : en
Pages : 308
Book Description
The Monte Carlo method is inherently parallel and the extensive and rapid development in parallel computers, computational clusters and grids has resulted in renewed and increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer. This book attempts to bridge the gap between theory and practice concentrating on modern algorithmic implementation on parallel architecture machines. Although a suitable text for final year postgraduate mathematicians and computational scientists it is principally aimed at the applied scientists: only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithms development often to applied industrial problems. A selection of algorithms developed both for serial and parallel machines are provided. Sample Chapter(s). Chapter 1: Introduction (231 KB). Contents: Basic Results of Monte Carlo Integration; Optimal Monte Carlo Method for Multidimensional Integrals of Smooth Functions; Iterative Monte Carlo Methods for Linear Equations; Markov Chain Monte Carlo Methods for Eigenvalue Problems; Monte Carlo Methods for Boundary-Value Problems (BVP); Superconvergent Monte Carlo for Density Function Simulation by B-Splines; Solving Non-Linear Equations; Algorithmic Effciency for Different Computer Models; Applications for Transport Modeling in Semiconductors and Nanowires. Readership: Applied scientists and mathematicians.
Algorithms for Decision Making
Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262047012
Category : Computers
Languages : en
Pages : 701
Book Description
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Publisher: MIT Press
ISBN: 0262047012
Category : Computers
Languages : en
Pages : 701
Book Description
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Global discrete artificial boundary conditions for timedependent wave propagation
Author:
Publisher: DIANE Publishing
ISBN: 142899579X
Category :
Languages : en
Pages : 36
Book Description
Publisher: DIANE Publishing
ISBN: 142899579X
Category :
Languages : en
Pages : 36
Book Description
Acta Numerica 2003: Volume 12
Author: Arieh Iserles
Publisher: Cambridge University Press
ISBN: 9780521825238
Category : Juvenile Nonfiction
Languages : en
Pages : 536
Book Description
An annual volume presenting substantive survey articles in numerical mathematics and scientific computing.
Publisher: Cambridge University Press
ISBN: 9780521825238
Category : Juvenile Nonfiction
Languages : en
Pages : 536
Book Description
An annual volume presenting substantive survey articles in numerical mathematics and scientific computing.
Ridge Functions and Applications in Neural Networks
Author: Vugar E. Ismailov
Publisher: American Mathematical Society
ISBN: 1470467658
Category : Mathematics
Languages : en
Pages : 186
Book Description
Recent years have witnessed a growth of interest in the special functions called ridge functions. These functions appear in various fields and under various guises. They appear in partial differential equations (where they are called plane waves), in computerized tomography, and in statistics. Ridge functions are also the underpinnings of many central models in neural network theory. In this book various approximation theoretic properties of ridge functions are described. This book also describes properties of generalized ridge functions, and their relation to linear superpositions and Kolmogorov's famous superposition theorem. In the final part of the book, a single and two hidden layer neural networks are discussed. The results obtained in this part are based on properties of ordinary and generalized ridge functions. Novel aspects of the universal approximation property of feedforward neural networks are revealed. This book will be of interest to advanced graduate students and researchers working in functional analysis, approximation theory, and the theory of real functions, and will be of particular interest to those wishing to learn more about neural network theory and applications and other areas where ridge functions are used.
Publisher: American Mathematical Society
ISBN: 1470467658
Category : Mathematics
Languages : en
Pages : 186
Book Description
Recent years have witnessed a growth of interest in the special functions called ridge functions. These functions appear in various fields and under various guises. They appear in partial differential equations (where they are called plane waves), in computerized tomography, and in statistics. Ridge functions are also the underpinnings of many central models in neural network theory. In this book various approximation theoretic properties of ridge functions are described. This book also describes properties of generalized ridge functions, and their relation to linear superpositions and Kolmogorov's famous superposition theorem. In the final part of the book, a single and two hidden layer neural networks are discussed. The results obtained in this part are based on properties of ordinary and generalized ridge functions. Novel aspects of the universal approximation property of feedforward neural networks are revealed. This book will be of interest to advanced graduate students and researchers working in functional analysis, approximation theory, and the theory of real functions, and will be of particular interest to those wishing to learn more about neural network theory and applications and other areas where ridge functions are used.
Acta Numerica 2005: Volume 14
Author: Arieh Iserles
Publisher: Cambridge University Press
ISBN: 9780521858076
Category : Mathematics
Languages : en
Pages : 584
Book Description
A high-impact factor, prestigious annual publication containing invited surveys by subject leaders: essential reading for all practitioners and researchers.
Publisher: Cambridge University Press
ISBN: 9780521858076
Category : Mathematics
Languages : en
Pages : 584
Book Description
A high-impact factor, prestigious annual publication containing invited surveys by subject leaders: essential reading for all practitioners and researchers.