Procedures, as introduced above, are much like ordinary mathematical functions. They specify a value that is determined by one or more parameters. But there is an important difference between mathematical functions and computer procedures. Procedures must be effective.

As a case in point, consider the problem of computing square roots. We can define the square-root function as

This describes a perfectly legitimate mathematical function. We could use it to recognize whether one number is the square root of another, or to derive facts about square roots in general. On the other hand, the definition does not describe a procedure. Indeed, it tells us almost nothing about how to actually find the square root of a given number. It will not help matters to rephrase this definition in pseudo-Lisp:

This only begs the question.(define (sqrt x) (the y (and (>= y 0) (= (square y) x))))

The contrast between function and procedure is a reflection of the
general distinction between describing properties of things and
describing how to do things, or, as it is sometimes referred to, the
distinction between declarative knowledge and imperative knowledge.
In mathematics we are usually concerned with declarative (what is)
descriptions, whereas in computer science we are usually concerned
with imperative (how to) descriptions. ^{20}

How does one compute square roots? The most common way is to use
Newton's method of successive approximations, which says that whenever
we have a guess *y* for the value of the square root of a
number *x*, we can perform a simple manipulation to get a
better guess (one closer to the actual square root) by averaging
*y* with *x*/*y*. ^{21} For
example, we can compute the square root of 2 as follows. Suppose our
initial guess is 1:

Guess | Quotient | Average |

Continuing this process, we obtain better and better approximations to the square root.

Now let's formalize the process in terms of procedures. We start with a value for the radicand (the number whose square root we are trying to compute) and a value for the guess. If the guess is good enough for our purposes, we are done; if not, we must repeat the process with an improved guess. We write this basic strategy as a procedure:

A guess is improved by averaging it with the quotient of the radicand and the old guess:(define (sqrt-iter guess x) (if (good-enough? guess x) guess (sqrt-iter (improve guess x) x)))

where(define (improve guess x) (average guess (/ x guess)))

We also have to say what we mean by "good enough." The following will do for illustration, but it is not really a very good test. (See exercise 1.7.) The idea is to improve the answer until it is close enough so that its square differs from the radicand by less than a predetermined tolerance (here 0.001):(define (average x y) (/ (+ x y) 2))

Finally, we need a way to get started. For instance, we can always guess that the square root of any number is 1:(define (good-enough? guess x) (< (abs (- (square guess) x)) 0.001))

If we type these definitions to the interpreter, we can use(define (sqrt x) (sqrt-iter 1.0 x))

The(sqrt 9)3.00009155413138(sqrt (+ 100 37))

11.704699917758145(sqrt (+ (sqrt 2) (sqrt 3)))

1.7739279023207892

(square (sqrt 1000))1000.000369924366

**Exercise 1.6**

Alyssa P. Hacker doesn't see why `if` needs to be provided as a
special form. "Why can't I just define it as an ordinary procedure in
terms of `cond`?" she asks. Alyssa's friend Eva Lu Ator claims
this can indeed be done, and she defines a new version of `if
`:

Eva demonstrates the program for Alyssa:(define (new-if predicate then-clause else-clause) (cond (predicate then-clause) (else else-clause)))

Delighted, Alyssa uses(new-if (= 2 3) 0 5)5

(new-if (= 1 1) 0 5)0

What happens when Alyssa attempts to use this to compute square roots? Explain.(define (sqrt-it guess x) (new-if (good-enough? guess x) guess (sqrt-iter (improve guess x) x)))

**Exercise 1.7**

The `good-enough?` test used in computing square roots will not
be very effective for finding the square roots of very small numbers.
Also, in real computers, arithmetic operations are almost always
performed with limited precision. This makes our test inadeqate for
very large numbers. Explain these statements, with examples showing
how the test fails for small and large numbers. An alternative
strategy for implementing `good-enough?` is to watch how guess
changes from one iteration to the next and to stop when the change is
a very small fraction of the guess. Design a square-root procedure
that uses this kind of end test. Does this work better for small and
large numbers?

**Exercise 1.8**

Newton's method for cube roots is based on the fact that if *y*
is an approximation to the cube root of *x*, then a better
approximation is given by the value

Use this formula to implement a cube-root procedure analogous to the square-root procedure. (In section 1.3.4 we will see how to implement Newton's method in general as an abstraction of these square-root and cube-root procedures.)