Integration of rational functions

June 11, 2010

So for this week’s blog post I will try to explain how the general algorithm for integrating rational functions works. Recall that a rational function is the quotient of two polynomials. We know that using common denominators, we can convert the sum of any number of rational functions into a single quotient, \frac{a_nx^n + a_{n-1}x^{n-1} + \cdots + a_2x^2 + a_1x + a_0}{b_nx^n + b_{n-1}x^{n-1} + \cdots + b_2x^2 + a_1x + a_0}. Also, using polynomial division we can rewrite any rational function as the sum of a polynomial and the quotient of two polynomials such that the degree of the numerator is less than the degree of the denominator (F(x) = \frac{b(x)}{c(x)} = p(x) + \frac{r(x)}{g(x)}, with deg(r) < deg(g)). Furthermore, we know that the representation of a rational function is not unique. For example, \frac{(x + 1)(x - 1)}{(x + 2)(x - 1)} is the same as \frac{x + 1}{x + 2} except at the point x = 1, and \frac{(x - 1)^2}{x - 1} is the same as x - 1 everywhere. But by using Euclid’s algorithm for finding the GCD of polynomials on the numerator and the denominator, along with polynomial division on each, we can cancel all common factors to get a representation that is unique (assuming we expand all factors into one polynomial). Finally, using polynomial division with remainder, we can rewrite any rational function F(x) as \frac{a(x)}{b(x)} = p(x) + \frac{a(x)}{d(x)}, where a(x), b(x), c(x), d(x), and p(x) are all polynomials, and the degree of a is less than the degree of d.

We know from calculus that the integral of any rational function consists of three parts: the polynomial part, the rational part, and the logarithmic part (consider arctangents as complex logarithms). The polynomial part is just the integral of p(x) above. The rational part is another rational function, and the logarithmic part, which is a sum of logarithms of the form a\log{s(x)}, where a is an algebraic constant and s(x) is a polynomial (note that if s(x) is a rational function, we can split it into two logarithms of polynomials using the log identities).

To find the rational part, we first need to know about square-free factorizations. An important result in algebra is that any polynomial with rational coefficients can be factored uniquely into irreducible polynomials with rational coefficients, up to multiplication of a non-zero constant and reordering of factors, similar to how any integer can be factored uniquely into primes up to multiplication of 1 and -1 and reordering of factors (technically, it is with coefficients from a unique factorization domain, for which the rationals is a special case, and up to multiplication of a unit, which for rationals is every non-zero constant). A polynomial is square-free if this unique factorization does not have any polynomials with powers greater than 1. Another theorem from algebra tells us that irreducible polynomials over the rationals do not have any repeated roots, and so given this, it is not hard to see that a polynomial being square-free is equivalent to it not having repeated roots.

A square-free factorization of a polynomial is a list of polynomials, P_1P_2^2 \cdots P_n^n, where each P_i is square-free (in other words, P_1 is the product of all the factors of degree 1, P_2 is the product of all the factors of degree 2, and so on). There is a relatively simple algorithm to compute the square-free factorization of a polynomial, which is based on the fact that gcd(P, \frac{dp}{dx}) reduces the power of each irreducible factor by 1. For example:

(Sorry for the picture. WordPress code blocks do not work)

It is not too hard to prove this using the product rule on the factorization of P. So you can see that by computing \frac{P}{gcd(P, \frac{dP}{dx})}, you can obtain P_1P_2\cdots P_n. Then, by recursively computing A_0 = P, A_1 = gcd(A_0, \frac{dA_0}{dx}), A2 = gcd(A_1, \frac{dA_1}{dx}), … and taking the quotient each time as above, we can find the square-free factors of P.

OK, so we know from partial fraction decompositions we learned in calculus that if we have a rational function of the form \frac{Q(x)}{V(x)^n} , where V(x) is square-free, the integral will be a rational function if n > 1 and a logarithm if n = 1. We can use the partial fraction decomposition that is easy to find once we have the square-free factorization of the denominator to rewrite the remaining rational function as a sum of terms of the form \frac{Q}{V_k^k}, where V_i is square-free. Because V is square-free, gcd(V, V')=1, so the Extended Euclidean Algorithm gives us B_0 and C_0 such that B_0V + C_0V'=1 (recall that g is the gcd of p and q if and only if there exist a and b relatively prime to g such that ap+bq=g. This holds true for integers as well as polynomials). Thus we can find B and C such that BV + CV'= \frac{Q}{1-k}. Multiplying through by \frac{1-k}{V^k}, \frac{Q}{V^k}=-\frac{(k-1)BV'}{V^k} + \frac{(1-k)C}{V^{k-1}}, which is equal to \frac{Q}{V^k} = (\frac{B'}{V^{k-1}} - \frac{(k-1)BV'}{V^k}) + \frac{(1-k)C-B'}{V^{k-1}}. You may notice that the term in the parenthesis is just the derivative of \frac{B}{V^{k-1}}, so we get \int\frac{Q}{V^k}=\frac{B}{V^{k-1}} + \int\frac{(1-k)C - B'}{V^{k-1}}. This is called Hermite Reduction. We can recursively reduce the integral on the right hand side until the k=1. Note that there are more efficient ways of doing this that do not actually require us to compute the partial fraction decomposition, and there is also a linear version due to Mack (this one is quadratic), and an even more efficient algorithm called the Horowitz-Ostrogradsky Algorithm, that doesn’t even require a square-free decomposition.

So when we have finished the Hermite Reduction, we are left with integrating rational functions with purely square-free denominators. We know from calculus that these will have logarithmic integrals, so this is the logarithmic part.

First, we need to look at resultants and PRSs. The resultant of two polynomials is defined as differences of the roots of the two polynomials, i.e., resultant(A, B) = \prod_{i=1}^n\prod_{j=1}^m (\alpha_i - \beta_j), where A = (x - \alpha_1)\cdots(x - \alpha_n) and B = (x - \beta_1)\cdots(x - \beta_m) are monic polynomials split into linear factors. Clearly, the resultant of two polynomials is 0 if and only if the two polynomials share a root. It is an important result that the resultant of two polynomials can be computed from only their coefficients by taking the determinant of the Sylvester Matrix of the two polynomials. However, it is more efficiently calculated using a polynomial remainder sequence (PRS) (sorry, there doesn’t seem to be a Wikipedia article), which in addition to giving the resultant of A and B, also gives a sequence of polynomials with some useful properties that I will discuss below. A polynomial remainder sequence is a generalization of the Euclidian algorithm where in each step, the remainder R_i is multiplied by a constant \beta_i. The Fundamental PRS Theorem shows how to compute specific \beta_i such that the resultant can be calculated from the polynomials in the sequence.

Then, if we have \frac{A}{D}, left over from the Hermite Reduction (so D square-free), let R=resultant_t(A-t\frac{dD}{dx}, D), where t is a new variable, and \alpha_i be the distinct roots of R. Let p_i=\gcd(A - \alpha_i\frac{dD}{dx}, D). Then it turns out that the logarithmic part of the integral is just \alpha_1\log{p_1} + \alpha_2\log{p_2} + \cdots \alpha_n\log{p_n}. This is called the Rothstein-Trager Algorithm.

However, this requires finding the prime factorization of the resultant, which can be avoided if a more efficient algorithm called the Lazard-Rioboo-Trager Algorithm is used. I will talk a little bit about it. It works by using subresultant polynomial reminder sequences.

It turns out that the above gcd(A-\alpha\frac{dD}{dx}, D) will appear in the PRS of D and A-t\frac{dD}{dx}. Furthermore, we can use the PRS to immediately find the resultant R=resultant_t(A-t\frac{dD}{dx}, D), which as we saw, is all we need to compute the logarithmic part.

So that’s rational integration. I hope I haven’t bored you too much, and that this made at least a little sense. I also hope that it was all correct. Note that this entire algorithm has already been implemented in SymPy, so if you plug a rational function in to integrate(), you should get back a solution. However, I describe it here because the transcendental case of the Risch Algorithm is just a generalization of rational function integration.

As for work updates, I found that the Poly version of the heursitic Risch algorithm was considerably slower than the original version, due to inefficiencies in the way the polynomials are currently represented in SymPy. So I have put that aside, and I have started implementing algorithms from the full algorithm. There’s not much to say on that front. It’s tedious work. I copy the algorithm from Bronstein’s book, then try make sure that it is correct based on the few examples given and from the mathematical background given, and when I’m satisfied, I move on to the next one. Follow my integration branch if you are interested.

In my next post, I’ll try to define some terms, like “elementary function,” and introduce a little differential algebra, so you can understand a little bit of the nature of the general integration algorithm.


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