If you try to read about this material in advance, it should make it easier to follow the class. I will offer some course notes (handouts page) that could reduce note-taking.

1. Sept. 5, 2018: Introduction to the course and how it will be run. Taylor's theorem; k-digit normalized-floating-point representation of reals, arithmetic and algebra in this system, errors under rounding and chopping. Began Nonlnear Equations topic and the bisection method.

2. Sept. 12, 2018: Reprise. Began Regula-Falsi (false position) and gave (without proof) facts about when it converges, and how fast. Described Newtons method the chord method, and the Secant method and discussed (again, no proofs) their convergence. In preparation for upcoming proofs, began topic of fixed point iteration (FPI), and the contraction mapping principle, a theorem that will be used to show when Newtons method converges.

3. Sept. 26, 2018: (The Sept. 19, class was cancelled by illness). To study the convergence, stated and proved the Contraction Mapping Principle. Began topic of rates of convergence in FPI and showed that the chord method has linear convergence. that Newton's method has quadratic (or better) convergence at non-tangency roots, and began to study the secant method. Gave some facts for the secant iterations from which we derived the relation that if e_j denotes the error if we stop converging secant iterations at step j, then |e_{n+1}|/|e_n|^t|->K where t=(1+sqrt(5))/2. Described the idea of accelleration of convergence, (Aitkin, Steffanson) for linearly converging methods (e.g. the chord method). Mentioned the multivariate context and systems of non-linear equations via a small example and the possibility we return to this topic later. Finally we began the new topic Linear Systems and Numerical Linear Algebra. Suggest downloading and reading Notes on Linear Equations available from the Handouts page.

4. Oct. 3, 2018: Lightning review of matrices, vectors and linear algebra facts, moving on to linear equation systems and the leading algorithm for them - Gaussian elimination with backsolving (GEBS) in gory detail, followed by Gauss-Jordan (G-J) reduction. Observed that G-J will do roughly 50% more numerical computation than GEBS. Next, by concrete example we observed the phenomenon of roundoff errors and their propogation during Gaussian elimination in fixed, finite precision. This motivated the idea of "pivoting strategies" (partial pivoting and scaled partial pivoting) as attempts to reduce roundoff errors.

5. Oct. 10, 2018: Showed how to compute A^{-1}: follow Gauss-Jordan reduction on an invertible matrix A, reducing it to the identity. The same sequence of row operations on I produces A^{-1}. Next, explained the LU factorization of an invertible matrix A: U is the result of Gaussian elimination on A (with no row interchanges!) and L is a unit-lower triangular matrix appropriately recording the pivot values used. Ax=b can then be solved using (LU)x=b: FIRST let y=Ux and solve Ly=b for y (this is forward-substitution); THEN solve Ux=y for x (this is back-solving). The computational cost of this process is exactly the same as Gaussian elimination and backsolving for the system Ax=b. Not all invertible matrices have LU factorizations but there is ALWAYS a permutation p of the n rows of A so that A(p) can be LU factored.

6. Oct. 17, 2018: Described the compact notation for the LUp algorithm, with or without use of a pivot strategy. Described the n by n Hilbert matrix H_n and showed the curious property it seems to exhibit as a very ill-conditioned coefficient matrix in a linear system, solved using fixed precision computation: (i) the solution vector x of the linear system where H_n is the coefficient matrix is very likely to be VERY far from the true solution; nevertheless (ii) H_n maps that erroneous solution very close to the right-hand-side vector of the system. Described the iterative-improvement algorithm for computed solutions (fixed precision) of linear systems, an introduction to the topic of iterative methods for Ax=b.

7. Oct. 24, 2018: A small review of matrix norms and then begin topic of iterative methods for Ax=b - the idea in general (multivariate analogue of fixed-point-iteration), and then the specifics for Jacobi's method. We gave conditions under which these iterations must converge to the solution. Next we described the Gauss-Seidel FPI method and gave an example.

8. Oct. 31, 2018: Showed how to execute the Gauss-Seidel iterations by using the Jacobi method ingredients in a special way, and began discussing the computation of eigen values and eigen vectors, starting with the power method.

9. Nov 7, 2018: Finished (for now) topic - eigen values/eigen vextors - but may return later. Began topic of approximation of functions via polynomials, reviewing Taylor polynomials and Taylors' thm. Then began the idea of interpolating polynomials and proving that given a continuous function f and n+1 distinct collocation points u_i, i=0,...,n, there is a unique polynomial I_n of degree at most n that interpolates f at the u_i: i.e., f(u_i)=I_n(u_i), i=0,...,n: (at most one by the fundamental thm. of algebra; at least one via construction of Lagranges form of I_n). Gave the error thm for interpolation - no proof. Began to study the computational cost to find and to evaluate Lagranges form of I_n compared to the cost of determining I_n as a_0+a_1*x+... +a_nx^n and then using Horners method to evaluate it cheaply at a given x.

10. Nov. 14, 2018: Begin study of Newton's form of the interpolating polynomial - much easier to find than the standard form and as easy to evaluate. Begin the topic of "best" or minimax approximation and Tchebycheff's characterization of some if its properties, with some examples.

** Nov. 21, 2018 was FRIDAY AT RUTGERS ... so NO CLASS

11. Nov. 28, 2018: Finish minimax poly approx and begin LSQ poly approx. first the continuous version, then the discrete version.