## 07 Accuracy of Computation

"Accuracy of Computations" is one of the topics at the end of Chapter 1. We will spend just a little time on this topic, because it is an issue for almost any practical application of numerical computation—and not just in linear algebra. You won't really need anything from this topic in the rest of this course, but I feel that you should be aware of the issue.

When computations are done with real numbers on a computer, only a limited number of decimal places are used. This means that real numbers, which can have an infinite number of digits after the decimal point, are represented only approximately.

And the problem is not just one of computation with a finite
number of decimal places instead of an infinite number. In practice,
the problem of *measurement* is often more important.
When the inputs to a computation come from physical or statistical
measurements, the input is generally only accurate to a certain
number of digits, often a small number.
There is a built-in error that is not going
to be fixed by using more decimal places.

Most often, computations are done with some fixed number of digits of accuracy, say 8 or 15. This has some funny consequences. For example, addition and multiplication are no longer strictly associative; the order of computation matters. For example, 1.0/3.0 with 8 digit accuracy becomes 0.33333333. This means that $3.0*(1.0/3.0)=3.0*0.33333333=0.99999999$ while $(3.0*1.0)/3.0 = 3.0/3.0 = 1.0$. This small discrepancy might not seem like a big deal, but suppose $A=1.0$ and $B=3.0$, and we need to compute $\frac{1.0}{1.0-3.0*A/B}$. If this is computed a $\frac{1.0}{1.0-3.0*(A/B)}$, the answer is $\frac{1.0}{0.00000001}$ or 100000000. But if it is computed as $\frac{1.0}{1.0-(3.0*A)/B}$, then the denominator is zero and the answer is undefined (or infinite).

More important for us is the fact that a small inaccuracy in the inputs to a computation can result in a larger inaccuracy in the output of that computation. As a simple example, if quantities $A$ and $B$ can be wrong by some small amount $\eps$, then $A+B$ could be off by as much as $2\eps$. When a lot of computations are done to produce the output, a small error in the inputs can result in a large error in the outputs. This is why, to really understand what the result of a computation means, you sometimes need to do an error analysis as well as the actual computation. There is a whole field of mathematics called numerical analysis that studies this issue, and it is certainly not an easy field.

Here is a linear algebra example. Consider a system of two equations $2.01x+3.00y=5.00$ and $3.99x + 6.00y = 10.00$, and suppose that there are possible errors of up to 0.01 in each of the coefficients. Then that actual system could be, for example, any of the following $$\matrix{2.01x+3.00y=5.00\cr 3.99x + 6.00y = 10.00}\qquad \matrix{2.00x+3.00y=5.00\cr 4.00x + 6.00y = 10.00}\qquad \matrix{2.00x+3.00y=5.00\cr 4.00x + 6.00y = 9.99}$$ The first of these systems has a unique solution, the second has infinitely may solutions, and the third has no solution. So we can't even be certain of the most basic fact about this system!

Computations done with integers and rational numbers are perfectly accurate, which is one reason why they are just about the only kinds of number that you will see in this course. However, restricting yourself to rational numbers does not solve the problem of errors in measurement—and it can quickly lead to some really intimidating fractions!