The manipulation of symbolic algebraic expressions is a complex process that illustrates many of the hardest problems that occur in the design of large-scale systems. An algebraic expression, in general, can be viewed as a hierarchical structure, a tree of operators applied to operands. We can construct algebraic expressions by starting with a set of primitive objects, such as constants and variables, and combining these by means of algebraic operators, such as addition and multiplication. As in other languages, we form abstractions that enable us to refer to compound objects in simple terms. Typical abstractions in symbolic algebra are ideas such as linear combination, polynomial, rational function, or trigonometric function. We can regard these as compound types, which are often useful for directing the processing of expressions. For example, we could describe the expression \[ x^{2}\, \sin (y^2+1)+x\, \cos 2y+\cos (y^3 -2y^2) \] as a polynomial in $x$ with coefficients that are trigonometric functions of polynomials in $y$ whose coefficients are integers.

We will not attempt to develop a complete algebraic-manipulation system here. Such systems are exceedingly complex programs, embodying deep algebraic knowledge and elegant algorithms. What we will do is look at a simple but important part of algebraic manipulation: the arithmetic of polynomials. We will illustrate the kinds of decisions the designer of such a system faces, and how to apply the ideas of abstract data and generic operations to help organize this effort.

Arithmetic on polynomials

Our first task in designing a system for performing arithmetic on polynomials is to decide just what a polynomial is. Polynomials are normally defined relative to certain variables (the indeterminates of the polynomial). For simplicity, we will restrict ourselves to polynomials having just one indeterminate (univariate polynomials).[1] We will define a polynomial to be a sum of terms, each of which is either a coefficient, a power of the indeterminate, or a product of a coefficient and a power of the indeterminate. A coefficient is defined as an algebraic expression that is not dependent upon the indeterminate of the polynomial. For example, \[ 5x^2 +3x +7 \] is a simple polynomial in $x$, and \[ (y^2 +1)x^3 +(2y)x+1 \] is a polynomial in $x$ whose coefficients are polynomials in $y$.

Already we are skirting some thorny issues. Is the first of these polynomials the same as the polynomial $5y^2 +3y +7$, or not? A reasonable answer might be yes, if we are considering a polynomial purely as a mathematical function, but no, if we are considering a polynomial to be a syntactic form. The second polynomial is algebraically equivalent to a polynomial in $y$ whose coefficients are polynomials in $x$. Should our system recognize this, or not? Furthermore, there are other ways to represent a polynomial—for example, as a product of factors, or (for a univariate polynomial) as the set of roots, or as a listing of the values of the polynomial at a specified set of points.[2] We can finesse these questions by deciding that in our algebraic-manipulation system a polynomial will be a particular syntactic form, not its underlying mathematical meaning.

Now we must consider how to go about doing arithmetic on polynomials. In this simple system, we will consider only addition and multiplication. Moreover, we will insist that two polynomials to be combined must have the same indeterminate.

We will approach the design of our system by following the familiar discipline of data abstraction. We will represent polynomials using a data structure called a poly, which consists of a variable and a collection of terms. We assume that we have selectors variable and term-list term_list that extract those parts from a poly and a constructor make-poly make_poly that assembles a poly from a given variable and a term list. A variable will be just a symbol, string, so we can use the same-variable? is_same_variable procedure function of section 2.3.2 to compare variables. The following procedures functions define addition and multiplication of polys:

To incorporate polynomials into our generic arithmetic system, we need to supply them with type tags. We'll use the tag polynomial, "polynomial", and install appropriate operations on tagged polynomials in the operation table.

Polynomial addition is performed termwise. Terms of the same order (i.e., with the same power of the indeterminate) must be combined. This is done by forming a new term of the same order whose coefficient is the sum of the coefficients of the addends. Terms in one addend for which there are no terms of the same order in the other addend are simply accumulated into the sum polynomial being constructed.

In order to manipulate term lists, we will assume that we have a constructor the-empty-termlist the_empty_termlist that returns an empty term list and a constructor adjoin-term adjoin_term that adjoins a new term to a term list. We will also assume that we have a predicate empty-termlist? is_empty_termlist that tells if a given term list is empty, a selector first-term first_term that extracts the highest-order term from a term list, and a selector rest-terms rest_terms that returns all but the highest-order term. To manipulate terms, we will suppose that we have a constructor make-term make_term that constructs a term with given order and coefficient, and selectors order and coeff that return, respectively, the order and the coefficient of the term. These operations allow us to consider both terms and term lists as data abstractions, whose concrete representations we can worry about separately.

Here is the procedure function that constructs the term list for the sum of two polynomials:polynomials;[3]

In order to multiply two term lists, we multiply each term of the first list by all the terms of the other list, repeatedly using mul-term-by-all-terms, mul_term_by_all_terms, which multiplies a given term by all terms in a given term list. The resulting term lists (one for each term of the first list) are accumulated into a sum. Multiplying two terms forms a term whose order is the sum of the orders of the factors and whose coefficient is the product of the coefficients of the factors:

This is really all there is to polynomial addition and multiplication. Notice that, since we operate on terms using the generic procedures functions add and mul, our polynomial package is automatically able to handle any type of coefficient that is known about by the generic arithmetic package. If we include a coercion mechanism such as one of those discussed in section 2.5.2, then we also are automatically able to handle operations on polynomials of different coefficient types, such as \[ \begin{array}{l} {\left[3x^2 +(2+3i)x+7\right] \cdot \left[x^4 +\frac{2}{3}x^2 +(5+3i)\right]} \end{array} \]

Because we installed the polynomial addition and multiplication procedures functions add-poly add_poly and mul-poly mul_poly in the generic arithmetic system as the add and mul operations for type polynomial, our system is also automatically able to handle polynomial operations such as \[ \begin{array}{l} {\left[ (y+1)x^2 +(y^2 +1)x+(y-1)\right]\cdot \left[(y-2)x+(y^3 +7)\right]} \end{array} \] The reason is that when the system tries to combine coefficients, it will dispatch through add and mul. Since the coefficients are themselves polynomials (in $y$), these will be combined using add-poly add_poly and mul-poly. mul_poly. The result is a kind of data-directed recursion in which, for example, a call to mul-poly mul_poly will result in recursive calls to mul-poly mul_poly in order to multiply the coefficients. If the coefficients of the coefficients were themselves polynomials (as might be used to represent polynomials in three variables), the data direction would ensure that the system would follow through another level of recursive calls, and so on through as many levels as the structure of the data dictates.[4]

Representing term lists

Finally, we must confront the job of implementing a good representation for term lists. A term list is, in effect, a set of coefficients keyed by the order of the term. Hence, any of the methods for representing sets, as discussed in section 2.3.3, can be applied to this task. On the other hand, our procedures functions add-terms add_terms and mul-terms mul_terms always access term lists sequentially from highest to lowest order. Thus, we will use some kind of ordered list representation.

How should we structure the list that represents a term list? One consideration is the density of the polynomials we intend to manipulate. A polynomial is said to be dense if it has nonzero coefficients in terms of most orders. If it has many zero terms it is said to be sparse. For example, \[ A:\quad x^5 +2x^4 +3x^2 -2x -5 \] is a dense polynomial, whereas \[ B:\quad x^{100} +2x^2 +1 \] is sparse.

The term lists of dense polynomials are most efficiently represented as lists of the coefficients. The term list of a dense polynomial is most efficiently represented as a list of the coefficients. For example, the polynomial $A$ above would be nicely represented as (1 2 0 3 -2 -5). list(1, 2, 0, 3, -2, -5). The order of a term in this representation is the length of the sublist beginning with that term's coefficient, decremented by 1.[5] This would be a terrible representation for a sparse polynomial such as $B$: There would be a giant list of zeros punctuated by a few lonely nonzero terms. A more reasonable representation of the term list of a sparse polynomial is as a list of the nonzero terms, where each term is a list containing the order of the term and the coefficient for that order. In such a scheme, polynomial $B$ is efficiently represented as ((100 1) (2 2) (0 1)). list(list(100, 1), list(2, 2), list(0, 1)). As most polynomial manipulations are performed on sparse polynomials, we will use this method. We will assume that term lists are represented as lists of terms, arranged from highest-order to lowest-order term. Once we have made this decision, implementing the selectors and constructors for terms and term lists is straightforward:[6]

Users of the polynomial package will create (tagged) polynomials by means of the procedure: function:

Hierarchies of types in symbolic algebra

Our polynomial system illustrates how objects of one type (polynomials) may in fact be complex objects that have objects of many different types as parts. This poses no real difficulty in defining generic operations. We need only install appropriate generic operations for performing the necessary manipulations of the parts of the compound types. In fact, we saw that polynomials form a kind of recursive data abstraction, in that parts of a polynomial may themselves be polynomials. Our generic operations and our data-directed programming style can handle this complication without much trouble.

On the other hand, polynomial algebra is a system for which the data types cannot be naturally arranged in a tower. For instance, it is possible to have polynomials in $x$ whose coefficients are polynomials in $y$. It is also possible to have polynomials in $y$ whose coefficients are polynomials in $x$. Neither of these types is above the other in any natural way, yet it is often necessary to add together elements from each set. There are several ways to do this. One possibility is to convert one polynomial to the type of the other by expanding and rearranging terms so that both polynomials have the same principal variable. One can impose a towerlike structure on this by ordering the variables and thus always converting any polynomial to a canonical form with the highest-priority variable dominant and the lower-priority variables buried in the coefficients. This strategy works fairly well, except that the conversion may expand a polynomial unnecessarily, making it hard to read and perhaps less efficient to work with. The tower strategy is certainly not natural for this domain or for any domain where the user can invent new types dynamically using old types in various combining forms, such as trigonometric functions, power series, and integrals.

It should not be surprising that controlling coercion is a serious problem in the design of large-scale algebraic-manipulation systems. Much of the complexity of such systems is concerned with relationships among diverse types. Indeed, it is fair to say that we do not yet completely understand coercion. In fact, we do not yet completely understand the concept of a data type. Nevertheless, what we know provides us with powerful structuring and modularity principles to support the design of large systems.

Extended exercise: Rational functions

We can extend our generic arithmetic system to include rational functions. These are fractions whose numerator and denominator are polynomials, such as \[ \begin{array}{l} \dfrac{x+1}{x^3 -1} \end{array} \] The system should be able to add, subtract, multiply, and divide rational functions, and to perform such computations as \[ \begin{array}{lll} \dfrac{x+1}{x^3 -1}+\dfrac{x}{x^2 -1} & = & \dfrac{x^3 +2x^2 +3x +1}{x^4 + x^3 -x-1} \end{array} \] (Here the sum has been simplified by removing common factors. Ordinary cross multiplication would have produced a fourth-degree polynomial over a fifth-degree polynomial.)

If we modify our rational-arithmetic package so that it uses generic operations, then it will do what we want, except for the problem of reducing fractions to lowest terms.

We can reduce polynomial fractions to lowest terms using the same idea we used with integers: modifying make-rat make_rat to divide both the numerator and the denominator by their greatest common divisor. The notion of greatest common divisor makes sense for polynomials. In fact, we can compute the GCD of two polynomials using essentially the same Euclid's Algorithm that works for integers.[7] The integer version is

We can solve the problem exhibited in exercise 2.99 if we use the following modification of the GCD algorithm (which really works only in the case of polynomials with integer coefficients). Before performing any polynomial division in the GCD computation, we multiply the dividend by an integer constant factor, chosen to guarantee that no fractions will arise during the division process. Our answer will thus differ from the actual GCD by an integer constant factor, but this does not matter in the case of reducing rational functions to lowest terms; the GCD will be used to divide both the numerator and denominator, so the integer constant factor will cancel out.

More precisely, if $P$ and $Q$ are polynomials, let $O_1$ be the order of $P$ (i.e., the order of the largest term of $P$) and let $O_2$ be the order of $Q$. Let $c$ be the leading coefficient of $Q$. Then it can be shown that, if we multiply $P$ by the integerizing factor $c^{1+O_{1} -O_{2}}$, the resulting polynomial can be divided by $Q$ by using the div-terms div_terms algorithm without introducing any fractions. The operation of multiplying the dividend by this constant and then dividing is sometimes called the pseudodivision of $P$ by $Q$. The remainder of the division is called the pseudoremainder.

Thus, here is how to reduce a rational function to lowest terms:

• Compute the GCD of the numerator and denominator, using the version of gcd-terms gcd_terms from exercise 2.100.
• When you obtain the GCD, multiply both numerator and denominator by the same integerizing factor before dividing through by the GCD, so that division by the GCD will not introduce any noninteger coefficients. As the factor you can use the leading coefficient of the GCD raised to the power $1+O_{1} -O_{2}$, where $O_{2}$ is the order of the GCD and $O_{1}$ is the maximum of the orders of the numerator and denominator. This will ensure that dividing the numerator and denominator by the GCD will not introduce any fractions.
• The result of this operation will be a numerator and denominator with integer coefficients. The coefficients will normally be very large because of all of the integerizing factors, so the last step is to remove the redundant factors by computing the (integer) greatest common divisor of all the coefficients of the numerator and the denominator and dividing through by this factor.

The GCD computation is at the heart of any system that does operations on rational functions. The algorithm used above, although mathematically straightforward, is extremely slow. The slowness is due partly to the large number of division operations and partly to the enormous size of the intermediate coefficients generated by the pseudodivisions. One of the active areas in the development of algebraic-manipulation systems is the design of better algorithms for computing polynomial GCDs.[10]