General sources
There is no single textbook. Much of the material comes from notes for an earlier version of this class by- Much of the material comes from notes for an earlier version of this class by David Bindel and me.
- A good textbook with much overlap in material is by Anne Greenbaum and Timothy Chartier.
- Here is the course web site. for a version of this class taught by a colleague. You may find the class slides helpful in seeing the big picture.
Weekly materials and assignments
- Week 1, assignment due September 16 (contact me if you can't because of Yom Kippur).
- Lecture notes and the assigmnent
- Python module illustrating single and double precision
- Notes on numerical coding in Python.
- Excellent textbook, though slightly dated in the most technical areas. Great treatment of floating point and conditioning of computational problems, by Jim Demmel.
- Excellent textbook, though slightly dated in the most technical areas. Great treatment of floating point and conditioning of computational problems, by Jim Demmel.
- Excellent shorter and more detailed book on floating point arithmetic, by my colleague Michael Overton.
- Week 3, assignment due September 30 .
- Second assignment
- Notes on numerical coding in Python.
- Numerical Methods, by Germund Dahlquist and Ake Bjork, an excellent and quite old book on "classical numerical analyisis". It has the best treatment of differentiation and integration and error expansions I know.
- Week 5, assignment due October 20 .
- Assignment3.pdf, third assignment
- MatrixExponential.pdf, definition and computing of the matrix exponential
- MatrixExponential.py, code for the matrix exponential
- RandomNumbers.py, generate random numbers in Python
- SourcePoints, data file for Exercise 3
- TargetPoints, data file for Exercise 3
- ReadParticleData.py, code to read ascii data files