For undergraduate Introduction to Numerical Analysis courses in mathematics, science, and engineering departments. This book provides a fundamental introduction to numerical analysis for undergraduate students in the areas of mathematics, computer science, physical sciences, and engineering. Knowledge of calculus is assumed.

 1. Preliminaries.  2. The Solution of Nonlinear Equations f (x) = 0.  3. The Solution of Linear Systems AX = B.  4. Interpolation and Polynomial Approximation.  5. Curve Fitting.  6. Numerical Differentiation.  7. Numerical Integration.  8. Numerical Optimization.  9. Solution of Differential Equations. 10. Solution of Partial Differential Equations. 11. Eigenvalues and Eigenvectors. Appendix: An Introduction to MATLAB. Answers to Selected Exercises. Index.

NEW - Expanded emphasis on analysis of competing methods and issues of error. Helps students understand that one can't rely blindly on a given numerical package. NEW - Rewritten chapter on numerical optimization. Provides a presentation that flows more smoothly. NEW - New topics for minimization of z = f(x,y) are included. Gives students a more thorough treatment that is useful here. NEW - Projects for undergraduate library research experience have been added. Provides students with opportunities for further study. Explicit use of the software MATLAB is offered. Builds on students' knowledge of structured programming and provides the opportunity to practice scientific programming. Each numerical method is presented in a self-contained format. Clearly explains numerical methods to students. Balance of theory and application. Builds on students' knowledge of calculus and basic linear algebra in a clear and readable presentation. A variety of problems. Sharpens students skills with extensive problem sets with a wide variety of activities. A wealth of tables and graphs. Illustrates computer calculations in examples making the resulting numerical approximations easier to interpret.

Expanded emphasis on analysis of competing methods and issues of error. Helps students understand that one can't rely blindly on a given numerical package. Rewritten chapter on numerical optimization. Provides a presentation that flows more smoothly. New topics for minimization of z = f(x,y) are included. Gives students a more thorough treatment that is useful here. Projects for undergraduate library research experience have been added. Provides students with opportunities for further study.