Part 2: Fit a power series to samples of a cosine curve. Do the fitting two ways and compare. First, fit using the QR code from part 1; second, using the normal equations (this can be implemented using high-level Matlab calls).
Guess the functional form.
Implement a linear least squares solver using QR factorization and Householder transformations, as described in chapter 3 of Heath's book. Implement factorization and solving using low-level Matlab code. By low-level, I mean that you shouldn't use Matlab's matrix multiply operator on a 2-D matrix, or use any of Matlab's operators or functions for solving a system or inverting a matrix, such as the "\" operator. You can: store your data in a matrix, extract vectors from a matrix, write a vector into a submatrix, compute inner products of vectors, and use Matlab's vector and scalar operators. Within these constraints, make your code concise. Comment it for clarity. Your QR factorization routine should work on any m by n matrix A, for m>=n. I suggest you test this code on points on a line to check that it's working.
See the course software web page for links to Matlab info.
Solve for the coefficients of the functional form you chose earlier, on your test data. Compute the RMS error (related to the 2-norm of the residual: RMS error = ||b-Ax||_{2}/sqrt(m)) and plot and eyeball the results to decide if you've got a good fit. When computing the error you can use matrix multiply.
If you don't have a good fit, consider changing your functional form and repeating. Perhaps the function is not cubic but quadratic, or perhaps it's got significant lower-order terms, e.g. c3*x^{3}+c2*x^{2}+c1*x+c0. The number of unknown coefficients in your functional form is n. Beware of adding too many terms, however.
Pick the results you like best, because of good fit and/or simple functional form, and graph them (both data points and fit curve). Make sure the graphs are well labeled. Write up a discussion of at least two paragraphs but not more than one page about your approach and your conclusions. Among other things, this discussion should state the coefficient values and RMS error of your best fit, and whether you got the result expected. Also turn in a listing of your Matlab code.
Run your code from part 1 on m data points from the function y=cos(x). Fit with a power series with n terms, all even powers of x.
Now implement the normal equations method. You're allowed to use Matlab's matrix multiply and solution operators and functions this time. But don't solve the overdetermined system Ax=b directly, or Matlab will be smart and use QR factorization. Instead compute the matrices A^{T}A and A^{T}b and solve the normal equations, A^{T}Ax=A^{T}b.
Compare the results, for various n, to determine when the normal equations method becomes inaccurate. How does adding random noise, as discussed in computer problem 3.6 of Heath (page 111), affect the results? How do the coefficients of your truncated power series compare with the infinite power series?
Turn in plots of at least two cases, the first where both methods fit well, and another where the fit of the normal equations method is poor, on each showing the data points, the curves, and the formulas from the two fitting methods. Turn in code listing (for the new code) and discussion.
Optional: compare your results to those found in the book Handbook of mathematical functions with formulas, graphs, and mathematical tables, edited by M. Abramowitz and I. A. Stegun, 1964, (in CMU's E&S library). Look up "circular functions, polynomial approximations" in the index.