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Numerical Linear Algebra

Lloyd N. Trefethen, David Bau III

Chapter 15

More on Stability - all with Video Answers

Educators


Chapter Questions

13:00

Problem 1

Each of the following problems describes an algorithm implemented on a computer satisfying the axioms (13.5) and $(13.7) .$ For each one, state whether the algorithm is backward stable, stable but not backward stable, or unstable, and prove it or at least give a reasonably convincing argument. Be
sure to follow the definitions as given in the text.
(a) Data: $x \in \mathbb{C} .$ Solution: $2 x,$ computed as $x \oplus x$
(b) Data: $x \in \mathbb{C}$. Solution: $x^{2},$ computed as $x \otimes x$
(c) Data: $x \in \mathbb{C} \backslash\{0\} .$ Solution: $1,$ computed as $x \oplus x .$ (A machine satisfying (13.6) will give exactly the right answer, but our definitions are based on the weaker condition $(13.7) .)$
(d) Data: $x \in \mathbb{C}$. Solution: $0,$ computed as $x \ominus x .$ (Again, a real machine may do better than our definitions based on $(13.7) .)$
(e) Data: none. Solution: $e,$ computed by summing $\sum_{k=0}^{\infty} 1 / k !$ from left to right using $\otimes$ and $\oplus,$ stopping when a summand is reached of magnitude
$<\epsilon_{\text {machine }}$
(f) Data: none. Solution: $e,$ computed by the same algorithm as above except with the series summed from right to left. (g) Data: none. Solution: $\pi,$ computed by doing an exhaustive search to find the smallest floating point number $x$ in the interval [3,4] such that $s(x) \otimes$ $s\left(x^{\prime}\right) \leq 0 .$ Here $s(x)$ is an algorithm that calculates $\sin (x)$ stably in the given interval, and $x^{\prime}$ denotes the next floating point number after $x$ in the floating point system.

Gabriel Eduok
Gabriel Eduok
Numerade Educator
05:05

Problem 2

Consider an algorithm for the problem of computing the (full) SVD of a matrix. The data for this problem is a matrix $A$, and the solution is three matrices $U(\text { unitary }), \Sigma(\text { diagonal }),$ and $V(\text { unitary })$ such that $A=$ $U \Sigma V^{*} .$ (We are speaking here of explicit matrices $U$ and $V$, not implicit representations as products of reflectors.)
(a) Explain what it would mean for this algorithm to be backward stable.
(b) In fact, for a simple reason, this algorithm cannot be backward stable. Explain.
(c) Fortunately, the standard algorithms for computing the SVD (Lecture 31 ) are stable. Explain what stability means for such an algorithm.

Jack Chen
Jack Chen
Numerade Educator