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基本信息
- 原书名：Mathematical Modeling, Fourth Edition
- 原出版社： Academic Press
- 作者： (美)Mark M. Meerschaert
- 丛书名： 华章数学原版精品系列
- 出版社：机械工业出版社
- ISBN：9787111448099
- 上架时间：2013-12-27
- 出版日期：2014 年1月
- 开本：16开
- 页码：365
- 版次：1-1
- 所属分类：数学 > 数学实验与数学建模 > 数学建模
教材
【插图】
内容简介
作译者
Mark M．Meerschaert，美国密歇根州立大学概率统计系教授。他曾在密歇根大学、英格兰学院、内华达大学、新西兰达尼丁Otago大学执教，讲授过数学建模、概率、统计学、运筹学、偏微分方程、地下水及地表水水文学与统计物理学课程。他当前的研究方向包括无限方差概率模型的极限定理和参数估计、金融数学中的厚尾模型、用厚尾模型及周期协方差结构建模河水流、医学成像、异常扩散、连续时间随机游动、分数次导数和分数次偏微分方程、地下水流及运输。
目录
《数学建模方法与分析(英文版)(第4版)》
Preface
I OPTIMIZATION MODELS
1 ONE VARIABLE OPTIMIZATION
1.1 The five-step Method
1.2 Sensitivity Analysis
1.3 Sensitivity and Robustness
1.4 Exercises
2 MULTIVARIABLE OPTIMIZATION
2.1 Unconstrained Optimization
2.2 Lagrange Multipliers
2.3 Sensitivity Analysis and Shadow Prices
2.4 Exercises
3 COMPUTATIONAL METHODS FOR OPTIMIZATION
3.1 One Variable Optimization
3.2 Multivariable Optimization
3.3 Linear Programming
3.4 Discrete Optimization
3.5 Exercises
II DYNAMIC MODELS
Preface
I OPTIMIZATION MODELS
1 ONE VARIABLE OPTIMIZATION
1.1 The five-step Method
1.2 Sensitivity Analysis
1.3 Sensitivity and Robustness
1.4 Exercises
2 MULTIVARIABLE OPTIMIZATION
2.1 Unconstrained Optimization
2.2 Lagrange Multipliers
2.3 Sensitivity Analysis and Shadow Prices
2.4 Exercises
3 COMPUTATIONAL METHODS FOR OPTIMIZATION
3.1 One Variable Optimization
3.2 Multivariable Optimization
3.3 Linear Programming
3.4 Discrete Optimization
3.5 Exercises
II DYNAMIC MODELS
前言
Mathematical modeling is the link between mathematics and the rest of the world. You ask a question. You think a bit, and then you refine the question,phrasing it in precise mathematical terms. Once the question becomes a math-ematics question, you use mathematics to find an answer. Then finally (and this is the part that too many people forget), you have to reverse the process, translating the mathematical solution back into a comprehensible, no-nonsense answer to the original question. Some people are fluent in English, and some people are fluent in calculus. We have plenty of each. We need more people who are fluent in both languages and are willing and able to translate. These are the people who will be influential in solving the problems of the future.
This text, which is intended to serve as a general introduction to the area of mathematical modeling, is aimed at advanced undergraduate or beginning graduate students in mathematics and closely related fields. Formal prerequi-sites consist of the usual freshman-sophomore sequence in mathematics, includ-ing one-variable calculus, multivariable calculus, linear algebra, and differential equations. Prior exposure to computing and probability and statistics is useful,but is not required.
Unlike some textbooks that, focus on one kind of mathematical model, this book covers the broad spectrum of modeling PrOblems, from optimization to dynamical systems to stochastic processes, Unlike some other textbooks that assume knowledge of only a semester of calculus, this book challenges students to use all of the mathematics they know (because that is what it takes to solve real problems).
The overwhelming majority of mathematical models fall into one of three categories: optimization models; dynamic models; and probability models. The type of model used in a real application might be dictated by the problem at hand, but more often, it is a matter of choice. In many instances, more than one type of model will be used. For example, a large Monte Carlo simulation model may be used in conjunction with a smaller, more tractable deterministic dynamic model based on expected values.
This book is organized into three parts, corresponding to the three main categories of mathematical models. We begin with optimization models. A five-step method for mathematical modeling is introduced in Section 1 of Chapter 1, in the context of one-variable optimization problems. The remainder of the first chapter is an introduction to sensitivity analysis and robustness. These fundamentals of mathematical modeling are used in a consistent way throughout the rest of the book. Exercises at the end of each chapter require students to master them as well. Chapter 2, on multivariable optimization, introduces decision variables, feasible and optimal solutions, and constraints. A review of the method of Lagrange multipliers is provided for the benefit of those students who were not exposed to this important technique in multivariable calculus.In the section on sensitivity analysis for problems with constraints, we learn that Lagrange multipliers represent shadow prices (some authors call them dual variables). This sets the stage for our discussion of linear programming later in Chapter 3. At the end of Chapter 3 is a section on discrete optimization that was added in the second edition. Herewe give a'practical introduction to integer programming using the branch-and-bound method. We also explore the connection between linear and integer programming problems, which allows an earlier introduction to the important issue of discrete versus continuous models.Chapter 3 covers some important computational' techniques, including Newton's method in one and several variables, and linear and integer programming.
In the next part of the book, on dynamic models, students are introduced to the concepts of state and equilibrium. Later discussions of state space, state variables, and equilibrium for stochastic processes are intimately connected to what is done here. Nonlinear dynamical systems in both discrete and continuous time are covered. There is very little emphasis on exact analytical solutions in this part of the book, since most of these models admit no analytic solution.At the end of Chapter 6 is a section on chaos and fractals that was added in the second edition. We use both analytic and simulation methods to explore the behavior of discrete and continuous dynamic models, to understand how they can become chaotic under certain conditions. This section provides a practical and accessible introduction to the subject. Students gain experience with Sensitive dependence to initial conditions, period doubling, and strange attractors that are fractal sets. Most important, these mathematical curiosities emerge from the study of real-world problems.
Finally, in the last part of the book, we introduce probability models. No prior knowledge of probability is assumed. Instead we build upon the material in the first two parts of the book, to introduce probability in a natural and intuitive way as it relates to real-world problems. Chapter 7 introduces the basic notions of random variables, probability distributions, the strong law of large numbers, and the central limit theorem. At the end of Chapter 7, Introduction to Prob- ability Models, is a section on diffusion, which was added in the third edition. Here we give a gentle introduction to partial differential equations by focusing on the diffusion equation. We provide a simple derivation of the point source solution to this partial differential equation, using Fourier transforms, to arrive at the normal density. Then we connect the diffusion modelto the central limit theorem introduced in the previous Section 7.3, Introduction to Statistics. This new section on diffusion grew out of a class taught at the University of Nevada for beginning graduate students in the earth sciences. The applications are to contaminant migration in the atmosphere and ground water. Chapter 8 covers the basic models of stochastic processes, including Markov chains, Markov pro-cesses, and linear regression. At the end of Chapter 8, Stochastic Models, a new section on time series was added in the ,third edition. This section also serves as an .introduction to multivariate regression models with more than one predie-tot. As a natural follow-up to the discussion in Section 8.3, Linear Regression,the new section on time series introduces the important idea of correlation. It also shows how to recognize correlated variables and include the dependence structure in a time series model. The discussion is focused on autoregressive models, since theSe are the most generally useful time series models. They are also the most convenient, in that they can be handled using widely available linear regression software. For the benefit of students with access to a statistical package, this section illustrates the proper application and interpretation of ad-vanced methods including autocorrelation plots and sequential sums of squares.However, the entire section can also be covered using only a basic implemen-tation of regression that allows multiple predictors and outputs the two basic measures: R2 and the residual standard deviation s. This can all be done with a good spreadsheet or hand calculator. Chapter 9 treats simulation methods for stochastic models. The Monte Carlo method is introduced, and Markov property is applied to create efficient simulation algorithms. Analytic simula-tion methods are also explored, and compared to the Monte Carlo method. In the fourth edition, two new sections were added to the end of Chapter 9. The first new section covers particle tracking methods, for solving partial differential equations via Monte carlo simulation of the underlying stochastic process. The final section of the book introduces fractional calculus in the context of anoma-lous diffusion. The fractional diffusion equation is solved by particle tracking, and applied to a problem in ground water pollution. This section ties together the concepts of fractals, fractional derivatives, and probability distributions with heavy tails.
Each chapter in this book is followed by a set of challenging exercises. These exercises require significant effort, as well as a certain amount, of creativity, on the part of the student. I did not invent the problems in this book. They are real problems. They were not designed to illustrate the use of any particular mathematical technique. Quite, the opposite. We will occasionally go over some new mathematical techniques in this book because the problem demands it. I was determined that there would be no place in this book where a student could look up and ask, "What-is all of this for?" Although typically oversim- plified or grossly unrealistic, story problems embody the fundamental challenge in applying mathematics to solve real problems. For most students, story prob- lems present plenty of challenge. This book teaches students how to solve story problems. There is a general method that can be applied successfully by any reasonably capable student to solve any story problem. It appears in Chapter 1, Section 1. This same general method is applied to problems of all kinds throughout the text.
Following the exercises in each chapter is a list of suggestions for further reading. This list includes references to a number of UMAP modules in applied mathematics that are relevant to the material in the chapter. UMAP modules can provide interesting supplements to the material in the text, or extra credit projects. All of the UMAP modules are available at a nominal cost from the Consortium for Mathematics and Its Applications (www.comap. com).
One of the major themes of this book is the use of appropriate technology for solving mathematical problems. Computer algebra systems, graphics, and numerical methods all have their place in mathematics. Many students have not had an adequate introduction to these tools. In this course we introduce modern technology in context. Students are motivated to learn because the new technology provides a more convenient way to solve real-world problems.Computer algebra systems and 2-D graphics are useful throughout the course.Some 3-D graphics are used in Chapters 2 and 3 in the sections on multivari-able optimization. Students who have already been introduced to 3-D graphics should be encouraged to use what they know. Numerical methods covered in the text include, among others, Newton's method, linear programming, the Euler method, linear regression, and Monte Carlo simulation.
The text contains numerous computer-generated graphs, along with instruc-tion on the appropriate use of graphing utilities in mathematics. Computer al-gebra systems are used extensively in those chapters where significant algebraic calculation is required. The text includes computer output from the computer algebra systems Maple and Mathematica in Chapters 2, 4, 5, and 8. The chap-ters on computational techniques (Chapters 3, 6, and 9) discuss the appropriate use of numerical algorithms to solve problems that admit no analytic solution.Sections 3.3 and 3.4 on linear-integer programming include computer output from the popular linear programming package LINDO. Sections 8.3 and 8.4 on linear regression and time series include output from the commonly used statistical package Minitab.
Students need to be provided with access to appropriate technology in or-der to take full advantage of this textbook. We have tried to make it easy for instructors to use this textbook at their own institution, whatever their situa- tion. Some will have the means to provide students with access to sophisticated computing facilities; while Others will have to make do with less. The bare ne-ceesities include: (1) a Software utility to draw 2-D graphs; and (2) a machine on which students can exeeute a few simple numerical algorithms, All of this can be done, for example, , with a computer spreadsheet program or a programmable graphics calculator. The ideal situation would be to provide all students access to a good computer algebra system, a linear programming package, and a statis-tical computing package. The following is a partial list of appropriate software packages that can be used in conjunction with this textbook.
This text, which is intended to serve as a general introduction to the area of mathematical modeling, is aimed at advanced undergraduate or beginning graduate students in mathematics and closely related fields. Formal prerequi-sites consist of the usual freshman-sophomore sequence in mathematics, includ-ing one-variable calculus, multivariable calculus, linear algebra, and differential equations. Prior exposure to computing and probability and statistics is useful,but is not required.
Unlike some textbooks that, focus on one kind of mathematical model, this book covers the broad spectrum of modeling PrOblems, from optimization to dynamical systems to stochastic processes, Unlike some other textbooks that assume knowledge of only a semester of calculus, this book challenges students to use all of the mathematics they know (because that is what it takes to solve real problems).
The overwhelming majority of mathematical models fall into one of three categories: optimization models; dynamic models; and probability models. The type of model used in a real application might be dictated by the problem at hand, but more often, it is a matter of choice. In many instances, more than one type of model will be used. For example, a large Monte Carlo simulation model may be used in conjunction with a smaller, more tractable deterministic dynamic model based on expected values.
This book is organized into three parts, corresponding to the three main categories of mathematical models. We begin with optimization models. A five-step method for mathematical modeling is introduced in Section 1 of Chapter 1, in the context of one-variable optimization problems. The remainder of the first chapter is an introduction to sensitivity analysis and robustness. These fundamentals of mathematical modeling are used in a consistent way throughout the rest of the book. Exercises at the end of each chapter require students to master them as well. Chapter 2, on multivariable optimization, introduces decision variables, feasible and optimal solutions, and constraints. A review of the method of Lagrange multipliers is provided for the benefit of those students who were not exposed to this important technique in multivariable calculus.In the section on sensitivity analysis for problems with constraints, we learn that Lagrange multipliers represent shadow prices (some authors call them dual variables). This sets the stage for our discussion of linear programming later in Chapter 3. At the end of Chapter 3 is a section on discrete optimization that was added in the second edition. Herewe give a'practical introduction to integer programming using the branch-and-bound method. We also explore the connection between linear and integer programming problems, which allows an earlier introduction to the important issue of discrete versus continuous models.Chapter 3 covers some important computational' techniques, including Newton's method in one and several variables, and linear and integer programming.
In the next part of the book, on dynamic models, students are introduced to the concepts of state and equilibrium. Later discussions of state space, state variables, and equilibrium for stochastic processes are intimately connected to what is done here. Nonlinear dynamical systems in both discrete and continuous time are covered. There is very little emphasis on exact analytical solutions in this part of the book, since most of these models admit no analytic solution.At the end of Chapter 6 is a section on chaos and fractals that was added in the second edition. We use both analytic and simulation methods to explore the behavior of discrete and continuous dynamic models, to understand how they can become chaotic under certain conditions. This section provides a practical and accessible introduction to the subject. Students gain experience with Sensitive dependence to initial conditions, period doubling, and strange attractors that are fractal sets. Most important, these mathematical curiosities emerge from the study of real-world problems.
Finally, in the last part of the book, we introduce probability models. No prior knowledge of probability is assumed. Instead we build upon the material in the first two parts of the book, to introduce probability in a natural and intuitive way as it relates to real-world problems. Chapter 7 introduces the basic notions of random variables, probability distributions, the strong law of large numbers, and the central limit theorem. At the end of Chapter 7, Introduction to Prob- ability Models, is a section on diffusion, which was added in the third edition. Here we give a gentle introduction to partial differential equations by focusing on the diffusion equation. We provide a simple derivation of the point source solution to this partial differential equation, using Fourier transforms, to arrive at the normal density. Then we connect the diffusion modelto the central limit theorem introduced in the previous Section 7.3, Introduction to Statistics. This new section on diffusion grew out of a class taught at the University of Nevada for beginning graduate students in the earth sciences. The applications are to contaminant migration in the atmosphere and ground water. Chapter 8 covers the basic models of stochastic processes, including Markov chains, Markov pro-cesses, and linear regression. At the end of Chapter 8, Stochastic Models, a new section on time series was added in the ,third edition. This section also serves as an .introduction to multivariate regression models with more than one predie-tot. As a natural follow-up to the discussion in Section 8.3, Linear Regression,the new section on time series introduces the important idea of correlation. It also shows how to recognize correlated variables and include the dependence structure in a time series model. The discussion is focused on autoregressive models, since theSe are the most generally useful time series models. They are also the most convenient, in that they can be handled using widely available linear regression software. For the benefit of students with access to a statistical package, this section illustrates the proper application and interpretation of ad-vanced methods including autocorrelation plots and sequential sums of squares.However, the entire section can also be covered using only a basic implemen-tation of regression that allows multiple predictors and outputs the two basic measures: R2 and the residual standard deviation s. This can all be done with a good spreadsheet or hand calculator. Chapter 9 treats simulation methods for stochastic models. The Monte Carlo method is introduced, and Markov property is applied to create efficient simulation algorithms. Analytic simula-tion methods are also explored, and compared to the Monte Carlo method. In the fourth edition, two new sections were added to the end of Chapter 9. The first new section covers particle tracking methods, for solving partial differential equations via Monte carlo simulation of the underlying stochastic process. The final section of the book introduces fractional calculus in the context of anoma-lous diffusion. The fractional diffusion equation is solved by particle tracking, and applied to a problem in ground water pollution. This section ties together the concepts of fractals, fractional derivatives, and probability distributions with heavy tails.
Each chapter in this book is followed by a set of challenging exercises. These exercises require significant effort, as well as a certain amount, of creativity, on the part of the student. I did not invent the problems in this book. They are real problems. They were not designed to illustrate the use of any particular mathematical technique. Quite, the opposite. We will occasionally go over some new mathematical techniques in this book because the problem demands it. I was determined that there would be no place in this book where a student could look up and ask, "What-is all of this for?" Although typically oversim- plified or grossly unrealistic, story problems embody the fundamental challenge in applying mathematics to solve real problems. For most students, story prob- lems present plenty of challenge. This book teaches students how to solve story problems. There is a general method that can be applied successfully by any reasonably capable student to solve any story problem. It appears in Chapter 1, Section 1. This same general method is applied to problems of all kinds throughout the text.
Following the exercises in each chapter is a list of suggestions for further reading. This list includes references to a number of UMAP modules in applied mathematics that are relevant to the material in the chapter. UMAP modules can provide interesting supplements to the material in the text, or extra credit projects. All of the UMAP modules are available at a nominal cost from the Consortium for Mathematics and Its Applications (www.comap. com).
One of the major themes of this book is the use of appropriate technology for solving mathematical problems. Computer algebra systems, graphics, and numerical methods all have their place in mathematics. Many students have not had an adequate introduction to these tools. In this course we introduce modern technology in context. Students are motivated to learn because the new technology provides a more convenient way to solve real-world problems.Computer algebra systems and 2-D graphics are useful throughout the course.Some 3-D graphics are used in Chapters 2 and 3 in the sections on multivari-able optimization. Students who have already been introduced to 3-D graphics should be encouraged to use what they know. Numerical methods covered in the text include, among others, Newton's method, linear programming, the Euler method, linear regression, and Monte Carlo simulation.
The text contains numerous computer-generated graphs, along with instruc-tion on the appropriate use of graphing utilities in mathematics. Computer al-gebra systems are used extensively in those chapters where significant algebraic calculation is required. The text includes computer output from the computer algebra systems Maple and Mathematica in Chapters 2, 4, 5, and 8. The chap-ters on computational techniques (Chapters 3, 6, and 9) discuss the appropriate use of numerical algorithms to solve problems that admit no analytic solution.Sections 3.3 and 3.4 on linear-integer programming include computer output from the popular linear programming package LINDO. Sections 8.3 and 8.4 on linear regression and time series include output from the commonly used statistical package Minitab.
Students need to be provided with access to appropriate technology in or-der to take full advantage of this textbook. We have tried to make it easy for instructors to use this textbook at their own institution, whatever their situa- tion. Some will have the means to provide students with access to sophisticated computing facilities; while Others will have to make do with less. The bare ne-ceesities include: (1) a Software utility to draw 2-D graphs; and (2) a machine on which students can exeeute a few simple numerical algorithms, All of this can be done, for example, , with a computer spreadsheet program or a programmable graphics calculator. The ideal situation would be to provide all students access to a good computer algebra system, a linear programming package, and a statis-tical computing package. The following is a partial list of appropriate software packages that can be used in conjunction with this textbook.
媒体评论
“这是一本很好的数学建模教科书，其中的数学知识非常有用，符合对本科主教学建模课程的教学要求。”
——John E．Doner，加州大学圣巴巴拉分校数学系
——John E．Doner，加州大学圣巴巴拉分校数学系
