### 28.11 Error Recovery ¶

When two numbers are combined by one of the four basic operations, the result often requires rounding to storage precision. For accurate computation, one would like to be able to recover that rounding error. With historical floating-point designs, it was difficult to do so portably, but now that IEEE 754 arithmetic is almost universal, the job is much easier.

For addition with the default round-to-nearest rounding mode, we can determine the error in a sum like this:

    volatile double err, sum, tmp, x, y;

    if (fabs (x) >= fabs (y))
      {
        sum = x + y;
        tmp = sum - x;
        err = y - tmp;
      }
    else /* fabs (x) < fabs (y) */
      {
        sum = x + y;
        tmp = sum - y;
        err = x - tmp;
      }

Now, `x + y` is exactly represented by `sum + err`. This basic operation, which has come to be called twosum in the numerical-analysis literature, is the first key to tracking, and accounting for, rounding error.

To determine the error in subtraction, just swap the `+` and `-` operators.

We used the `volatile` qualifier (see `volatile` Variables and Fields) in the declaration of the variables, which forces the compiler to store and retrieve them from memory, and prevents the compiler from optimizing `err = y - ((x + y) - x)` into `err = 0`.

For multiplication, we can compute the rounding error without magnitude tests with the FMA operation (see Fused Multiply-Add), like this:

    volatile double err, prod, x, y;
    prod = x * y;                /* rounded product */
    err  = fma (x, y, -prod);    /* exact product = prod + err */

For addition, subtraction, and multiplication, we can represent the exact result with the notional sum of two values. However, the exact result of division, remainder, or square root potentially requires an infinite number of digits, so we can at best approximate it. Nevertheless, we can compute an error term that is close to the true error: it is just that error value, rounded to machine precision.

For division, you can approximate `x / y` with `quo + err` like this:

    volatile double err, quo, x, y;
    quo = x / y;
    err = fma (-quo, y, x) / y;

For square root, we can approximate `sqrt (x)` with `root + err` like this:

    volatile double err, root, x;
    root = sqrt (x);
    err = fma (-root, root, x) / (root + root);

With the reliable and predictable floating-point design provided by IEEE 754 arithmetic, we now have the tools we need to track errors in the five basic floating-point operations, and we can effectively simulate computing in twice working precision, which is sometimes sufficient to remove almost all traces of arithmetic errors.