BILKENT UNIVERSITY
CS 471 Numerical Methods, Fall 2008
11:40-12:30 M, 13:40-15:30 Th (EB202)

• CS 102 Algorithms and Programming II, MATH 225 Linear Algebra and Differential Equations
  are prerequisites.
• We will use Matlab.
• I do NOT recommend the course for students who received a grade below C in linear algebra
  and differential equations or to those who had problems with calculus.
• Note that this course is designated as 400-level.

• Van Loan, C. F., Introduction to Scientific Computing: A Matrix-Vector Approach using MATLAB,
  2nd edition, Prentice Hall, 2000.

• Cheney, W. and Kincaid, D., Numerical Mathematics and Computing, 4th edition, Brooks/Cole,
  California, 1999.
• Stewart. G. W., Matrix Algorithms, SIAM Press, Pennslyvania, 1998. [QA188 .S714 1998]
• Trefethen, Lloyd N. and Bau, D., Numerical Linear Algebra, SIAM Press, Pennslyvania, 1997.
  [QA184.T74 1997]
• Higham, N. J., Accuracy and Stability of Numerical Algorithms, SIAM Press, Pennslyvania, 1996.
  [QA297 .H53 1996]
• Stewart. G. W., Afternotes on Numerical Analysis, SIAM Press, Pennslyvania, 1996.
  [QA297 .S785 1996]
• Additonal references

• Power Tools of the Trade (Chapter [1] in textbook, 3 weeks)
• Polynomial Interpolation (Chapter [2] in textbook, 1 week)
• Piecewise Polynomial Interpolation (Chapter [3] in textbook, 2 weeks)
• Numerical Integration (Chapter [4] in textbook, 1 week)
• Matrix Computations (Chapter [5] in textbook, 3 weeks)
• Linear Systems (Chapter [6] in textbook, 2 weeks)
• The QR and Cholesky Factorizations (Chapter [7] in textbook, 2 weeks)
• Nonlinear Equations and Optimization (Chapter [8] in textbook, 1 week)

• M-files in the textbook
• Errata for the textbook
• The Mathworks, Inc. Home Page
• Introduction to MATLAB from Rice University (pdf file, 43 KB)
• MATLAB Primer by Kermit Sigmon (pdf file, 226 KB)
• MATLAB on Athena, MIT
• Example m-file
• Scilab is similar to Matlab and available for free
• Netlib, where a wide range of software is available
• Check out HotGAMS at NIST
• MatrixMarket at NIST is another interesting site
• Importance of numerical methods for undergrad CS majors according to Bob Funderlic.
• A Hundred-dollar, Hundred-Digit Challenge Winners' names and winning digits
• On predictions for scientific computing, numerical analyisis, and maxims by L. N. Trefethen.
• Linear algebra review notes.

• Homework (30%).
  Assignments are due at class time; they will not be accepted after class time.
  Turned in assignments must be printer outputs with your names and numbers on them;
  otherwise they will not be graded.
  You can see the TA for any questions you have during his office hours.
  It is also suggested that you work out all the problems in each assignment
  since a randomly chosen subset of them will be graded.
  
  • Assignment 1 (due thursday October 16)
  • Assignment 2 (due thursday October 30)
  • Assignment 3 (due monday November 17)
  • Assignment 4 (due monday December 15)
  • Assignment 5 (due monday December 29)
• Midterm (30%) (thursday November 27 at 9:00 in class, covers Chapters 1-4 in the textbook)
• Final (40%) (comprehensive: 10% from midterm material, 30% from Chapters 5-8 in the textbook)
  All tests will be closed book/notes.