This is the 6th edition with a publication date of 4/20/2009.
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Instructors loveNumerical Methods for Engineersbecause it makes teaching easy! Students love it because it is written for them--with clear explanations and examples throughout. The text features a broad array of applications that span all engineering disciplines. ..The sixth edition retains the successful instructional techniques of earlier editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation. This prepares the student for upcoming problems in a motivating and engaging manner. Each part closes with an Epilogue containing Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Helpful separate Appendices. "Getting Started with MATLAB" abd "Getting Started with Mathcad" which make excellent references...Numerous new or revised problems drawn from actual engineering practice, many of which are based on exciting new areas such as bioengineering. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Excellent new examples and case studies span asll areas of engineering disciplines; the students using this text will be able to apply their new skills to their chosen field...Users will find use of software packages, specifically MATLAB�, Excel� with VBA and Mathcad�. This includes material on developing MATLAB� m-files and VBA macros. . . .
Table of Contents
Part 1 Modeling, Computers, and Error Analysis 1 Mathematical Modeling and Engineering Problem Solving 2 Programming and Software 3 Approximations and Round-Off Errors 4 Truncation Errors and the Taylor Series Part 2 Roots of Equations 5 Bracketing Methods 6 Open Methods 7 Roots of Polynomials 8 Case Studies: Roots of Equations Part 3 Linear Algebraic Equations 9 Gauss Elimination 10 LU Decomposition and Matrix Inversion 11 Special Matrices and Gauss-Seidel 12 Case Studies: Linear Algebraic Equations Part 4 Optimization 13 One-Dimensional Unconstrained Optimization 14 Multidimensional Unconstrained Optimization 15 Constrained Optimization 16 Case Studies: Optimization Part 5 Curve Fitting 17 Least-Squares Regression 18 Interpolation 19 Fourier Approximation 20 Case Studies: Curve Fitting Part 6 Numerical Differentiation and Integration 21 Newton-Cotes Integration Formulas 22 Integration of Equations 23 Numerical Differentiation 24 Case Studies: Numerical Integration and Differentiation Part 7 Ordinary Differential Equations 25 Runge-Kutta Methods 26 Stiffness and Multistep Methods 27 Boundary-Value and Eigenvalue Problems 28 Case Studies: Ordinary Differential Equations Part 8 Partial Differential Equations 29 Finite Difference: Elliptic Equations 30 Finite Difference: Parabolic Equations 31 Finite-Element Method 32 Case Studies: Partial Differential Equations Appendix A The Fourier Series Appendix B Getting Started with Matlab Bibliography Index