Libros de Matemáticas, página 12 de 470

This textbook is a self-contained introduction to partial differential equations. It has been designed for undergraduates and first year graduate students majoring in mathematics, physics, engineering, or science. The text provides an introduction to the basic equations of mathematical physics and the properties of their solutions, based on classical calculus and ordinary differential equations. Advanced concepts such as week solutions and discontinuous solutions of nonlinear conservation laws are also considered.

This book gives a clear, practical and self-contained presentation of the methods of asymptotics and perturbation theory and explains how to use these methods to obtain approximate analytical solutions to differential and difference equations. These methods allow one to analyze physics and engineering problems that may not be solvable in closed form and for which brute-force numerical methods may not converge to useful solutions. The objective of this book is to teaching the insights and problem-solving skills that are most useful in solving mathematical problems arising in the course of modern research. Intended for graduate students and advanced undergraduates, the book assumes only a limited familiarity with differential equations and complex variables. The presentation begins with a review of differential and difference equations; develops local asymptotic methods for differential and difference equations; explains perturbation and summation theory; and concludes with a an exposition of global asymptotic methods, including boundary-layer theory, WKB theory, and multiple-scale analysis.Emphasizing applications, the discussion stresses care rather than rigor and relies on many well-chosen examples to teach the reader how an applied mathematician tackles problems. There are 190 computer-generated plots and tables comparing approximate and exact solutions; over 600 problems, of varying levels of difficulty; and an appendix summarizing the properties of special functions.

It has been over a decade since the release of the now classic original edition of Murray''s Mathematical Biology. Since then mathematical biology has grown at an astonishing rate and is well established as a distinct discipline. Mathematical modeling is now being applied in every major discipline in the biomedical sciences. Though the field has become increasingly large and specialized, this book remains important as a text that introduces some of the exciting problems that arise in biology and gives some indication of the wide spectrum of questions that modeling can address. Due to the tremendous development in the field this book is being published in two volumes. This first volume is an introduction to the field, the mathematics mainly involves ordinary differential equations that are suitable for undergraduate and graduate courses at different levels. For this new edition Murray is covering certain items in-depth, giving new applications such as modeling marital interactions and temperature dependence sex determination. SIAM, 2004 -Murray''s Mathematical Biology is a classic that belongs on the shelf of any serious student or researcher in the field.Together the two volumes contain well over 1000 references, a rich source of material, together with an excellent index to help readers quickly find key words...I recommend the new and expanded third edition to any serious young student interested in mathematical biology who already has a solid basis in applied mathematics.

For practicing engineers who are faced with problems of modeling.Written by a recognized authority in the field of identification and control, this book draws together into a single volume the important aspects of system identification AND physical modelling. Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc. (e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work).Explains system identification techniques used to infer knowledge about the behavior of dynamic systems based on observations of the various input and output signals that are available for measurement. Shows how both types of techniques need to be applied in any given practical modeling situation. Considers applications, primarily simulation.Features- brings together all basic techniques for modeling of dynamic systems - with even stress on physical modeling and system identification. - describes how to create physical models and - in cases where these physical models contain unknown parameters - how to estimate these from data from actual experiments of a system. - explains how to form models from basic physical equations. Shows in a specific example techniques that can be applied to a multitude of situations. - shows how to use data to develop nonlinear models in a simple fashion. - considers how to identify systems in a closed loop. Describes the problems that arise when data are collected under output feedback and describes how to deal with such a situation successfully.

Designed for courses on Evolutionary Multi-objective Optimization and Evolutionary Algorithms.This textbook is the second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly augmented with contemporary knowledge and adapted for the classroom. All the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and student-friendly fashion, incorporating state-of-the-art research results. The diversity of serial and parallel MOEA structures are given, evaluated and compared. The book provides detailed insight into the application of MOEA techniques to an array of practical problems. The assortment of test suites are discussed along with the variety of appropriate metrics and relevant statistical performance techniques.Distinctive features of the new edition include:- Designed for graduate courses on Evolutionary Multi-Objective Optimization, with exercises and links to a complete set of teaching material including tutorials.- Updated and expanded MOEA exercises, discussion questions and research ideas at the end of each chapter.- New chapter devoted to coevolutionary and memetic MOEAs with added material on solving constrained multi-objective problems.- Additional material on the most recent MOEA test functions and performance measures, as well as on the latest developments on the theoretical foundations of MOEAs.- An exhaustive index and bibliography.This self-contained reference is invaluable to students, researchers and in particular to computer scientists, operational research scientists and engineers working in evolutionary computation, genetic algorithms and artificial intelligence.

Convex optimization problems arise frequently in many different fields.This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods.The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.- Gives comprehensive details on how to recognize convex optimization problems in a wide variety of settings.- Provides a broad range of practical algorithms for solving real problems.- Contains hundreds of worked examples and homework exercises.

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.