Important Information.
Textbook.
Required textbook:
A Friendly Introduction to Numerical Analysis, by Brian Bradie
(Pearson / Prentice Hall, ISBN 0-13-013054-0)
Supplements to Lecture Notes.
Section 2.1:
Matlab code for the
bisection algorithm.
Section 2.3:
Illustration of the text
book example of fixed point iterations.
Section 2.4:
Fractals generated by
using Newton's method on f(z)=z^3-1.
Section 2.6:
Aitken's acceleration
method applied to a slowly converging infinite sum.
Section 3.4:
Hilbert matrix: An
example of extreme ill-conditioning.
Section 3.9: An
example of using the
Conjugate Gradient
algorithm.
Section 6.2: Article:
Calculation
of weights in finite difference formulas. Matlab code:
weights.m ,
Mathematica 7 code:
Pade_algorithm.nb
.
Sections 6.4, 6.5, 6.7:
A comparison between
Gaussian quadrature, Newton-Cotes and Romberg's methods
Section 7.3: Effective implementation of the
Taylor
method.
Section 7.4:
Derivation of
Runge-Kutta methods.
Section 7.9:
Linear
recursion relations.
Course Grades.
With Homework totals (lowest HW discarded) worth 10%, each Midterm 15%
and the Final 45%, the distribution of Course Scores became as shown on
this
histogram. The grade distribution
became as follows: 5 (A), 1 (A-), 5 (B+), 4 (B), 4 (B-), 0 (C+), 4 (C),
2 (C-), 3 (D), 2 (F).
Policies.
Official CU policy information: