Numerical analysis

An introduction to hyperbolic PDEs and a class of numerical methods for approximating their solution, including both linear problems and nonlinear conservation laws.

A comprehensive look at the Schwarz-Christoffel transformation, including its many applications.

This book arose out of the authors' desire to present Lebesgue integration and Fourier series on an undergraduate level, since most undergraduate texts do not cover this material or do so in a cursory way.

A systematic introduction to partial differential equations and modern finite element methods for their efficient numerical solution Partial Differential Equations and the Finite Element Method provides a much-needed, clear, and systematic introduction to modern theory of partial differential equations (PDEs) and finite element methods (FEM).

A new, revised edition of a yet unrivaled work on frequency domain analysis Long recognized for his unique focus on frequency domain methods for the analysis of time series data as well as for his applied, easy-to-understand approach, Peter Bloomfield brings his well-known 1976 work thoroughly up to date.

This important textbook provides an introduction to the concepts of the newly developed extended finite element method (XFEM) for fracture analysis of structures, as well as for other related engineering applications.

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing.

This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems.

A hands-on approach to the basic principles of empirical model building.

This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory.

Discover the benefits of applying algorithms to solve scientific, engineering, and practical problems Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in "e;hot"e; application areas, such as computational biology, computational chemistry, wireless networks, and computer vision.

Praise for The Mathematics of Derivatives "e;The Mathematics of Derivatives provides a concise pedagogical discussion of both fundamental and very recent developments in mathematical finance, and is particularly well suited for readers with a science or engineering background.

Throughout banking, mathematical techniques are used.

Besides their intrinsic mathematical interest, geometric partial differential equations (PDEs) are ubiquitous in many scientific, engineering and industrial applications.

Processing, Analyzing and Learning of Images, Shapes, and Forms: Volume 19, Part One provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms.

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques.

Besides their intrinsic mathematical interest, geometric partial differential equations (PDEs) are ubiquitous in many scientific, engineering and industrial applications.

Handbook on Numerical Methods for Hyperbolic Problems: Applied and Modern Issues details the large amount of literature in the design, analysis, and application of various numerical algorithms for solving hyperbolic equations that has been produced in the last several decades.

Handbook of Numerical Methods for Hyperbolic Problems explores the changes that have taken place in the past few decades regarding literature in the design, analysis and application of various numerical algorithms for solving hyperbolic equations.

Essential Computational Modeling in Chemistry presents key contributions selected from the volume in the Handbook of Numerical Analysis: Computational Modeling in Chemistry Vol.

In this popular text for an Numerical Analysis course, the authors introduce several major methods of solving various partial differential equations (PDEs) including elliptic, parabolic, and hyperbolic equations.

In this popular text for an Numerical Analysis course, the authors introduce several major methods of solving various partial differential equations (PDEs) including elliptic, parabolic, and hyperbolic equations.

Many mathematical problems in science and engineering are defined by ordinary or partial differential equations with appropriate initial-boundary conditions.

Many mathematical problems in science and engineering are defined by ordinary or partial differential equations with appropriate initial-boundary conditions.

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods.

There has been dramatic growth in the development and application of Bayesian inference in statistics.

Solving optimization problems in complex systems often requires the implementation of advanced mathematical techniques.

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science.

Nonlinear Optimization is an intriguing area of study where mathematical theory, algorithms and applications converge to calculate the optimal values of continuous functions.

This book represents a collection of recent advances in computational studies in neuroscience research that practically applies to a collaborative and integrative environment in engineering and medical domains.

"e;Optimization on Metric and Normed Spaces"e; is devoted to the recent progress in optimization on Banach spaces and complete metric spaces.

In a world with highly competitive markets and economic instability due to capitalization, industrial competition has increasingly intensified.

While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting.

While we were busy putting together the present collection of articles celebrating the twentieth birthday of our journal, Discrete & Computational Geometry, and, in a way, of the ?

Sobolev spaces become the established and universal language of partial differential equations and mathematical analysis.

In this undergraduate/graduate textbook, the authors introduce ODEs and PDEs through 50 class-tested lectures.

Optimization is a field important in its own right but is also integral to numerous applied sciences, including operations research, management science, economics, finance and all branches of mathematics-oriented engineering.

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs).

Problems in Real Analysis: Advanced Calculus on the Real Axis features a comprehensive collection of challenging problems in mathematical analysis that aim to promote creative, non-standard techniques for solving problems.

Many aspects of phenomena critical to our lives can not be measured directly.

Recent Advances in Numerical Methods features contributions from distinguished researchers focused on significant aspects of current numerical methods and computational mathematics.

A large number of scientists and engineers use Monte Carlo simulation as an essential tool in their work.

Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scienti?

Algorithms in algebraic geometry go hand in hand with software packages that implement them.

Stability is a very important property of mathematical models simulating physical processes which provides an adequate description of the process.

Agriculture has experienced a dramatic change during the past decades.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics.