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# Details on the Hypergeometric Function Expansion

This page describes how the function :func:~diofant.simplify.hyperexpand.hyperexpand and related code work. For usage, see the documentation of the symplify module.

## Hypergeometric Function Expansion Algorithm

This section describes the algorithm used to expand hypergeometric functions. Most of it is based on the papers [Roach1996] and [Roach1997].

Recall that the hypergeometric function is (initially) defined as

{}_pF_q\left(\begin{matrix} a_1, \ldots, a_p \\ b_1, \ldots, b_q \end{matrix}
\middle| z \right)
= \sum_{n=0}^\infty \frac{(a_1)_n \ldots (a_p)_n}{(b_1)_n \ldots (b_q)_n}
\frac{z^n}{n!}.


It turns out that there are certain differential operators that can change the a_p and p_q parameters by integers. If a sequence of such operators is known that converts the set of indices a_r^0 and b_s^0 into a_p and b_q, then we shall say the pair a_p, b_q is reachable from a_r^0, b_s^0. Our general strategy is thus as follows: given a set a_p, b_q of parameters, try to look up an origin a_r^0, b_s^0 for which we know an expression, and then apply the sequence of differential operators to the known expression to find an expression for the Hypergeometric function we are interested in.

### Notation

In the following, the symbol a will always denote a numerator parameter and the symbol b will always denote a denominator parameter. The subscripts p, q, r, s denote vectors of that length, so e.g. a_p denotes a vector of p numerator parameters. The subscripts i and j denote "running indices", so they should usually be used in conjuction with a "for all i". E.g. a_i < 4 for all i. Uppercase subscripts I and J denote a chosen, fixed index. So for example a_I > 0 is true if the inequality holds for the one index I we are currently interested in.

### Incrementing and decrementing indices

Suppose a_i ne 0. Set A(a_i) = frac{z}{a_i}frac{mathrm{d}}{dz}+1. It is then easy to show that A(a_i) {}_p F_qleft({a_p atop b_q} middle| z right) = {}_p F_qleft({a_p + e_i atop b_q} middle| z right), where e_i is the i-th unit vector. Similarly for b_j ne 1 we set B(b_j) = frac{z}{b_j-1} frac{mathrm{d}}{dz}+1 and find B(b_j) {}_p F_qleft({a_p atop b_q} middle| z right) = {}_p F_qleft({a_p atop b_q - e_i} middle| z right). Thus we can increment upper and decrement lower indices at will, as long as we don't go through zero. The A(a_i) and B(b_j) are called shift operators.

It is also easy to show that frac{mathrm{d}}{dz} {}_p F_qleft({a_p atop b_q} middle| z right) = frac{a_1 ldots a_p}{b_1 ldots b_q} {}_p F_qleft({a_p + 1 atop b_q + 1} middle| z right), where a_p + 1 is the vector a_1 + 1, a_2 + 1, ldots and similarly for b_q + 1. Combining this with the shift operators, we arrive at one form of the Hypergeometric differential equation: left[ frac{mathrm{d}}{dz} prod_{j=1}^q B(b_j) - frac{a_1 ldots a_p}{(b_1-1) ldots (b_q-1)} prod_{i=1}^p A(a_i) right] {}_p F_qleft({a_p atop b_q} middle| z right) = 0. This holds if all shift operators are defined, i.e. if no a_i = 0 and no b_j = 1. Clearing denominators and multiplying through by z we arrive at the following equation: left[ zfrac{mathrm{d}}{dz} prod_{j=1}^q left(zfrac{mathrm{d}}{dz} + b_j-1 right) - z prod_{i=1}^p left( zfrac{mathrm{d}}{mathrm{d}z} + a_i right) right] {}_p F_qleft({a_p atop b_q} middle| zright) = 0. Even though our derivation does not show it, it can be checked that this equation holds whenever the {}_p F_q is defined.

Notice that, under suitable conditions on a_I, b_J, each of the operators A(a_i), B(b_j) and zfrac{mathrm{d}}{mathrm{d}z} can be expressed in terms of A(a_I) or B(b_J). Our next aim is to write the Hypergeometric differential equation as follows: [X A(a_I) - r] {}_p F_qleft({a_p atop b_q} middle| zright) = 0, for some operator X and some constant r to be determined. If r ne 0, then we can write this as frac{-1}{r} X {}_p F_qleft({a_p + e_I atop b_q} middle| zright) = {}_p F_qleft({a_p atop b_q} middle| zright), and so frac{-1}{r}X undoes the shifting of A(a_I), whence it will be called an inverse-shift operator.

Now A(a_I) exists if a_I ne 0, and then zfrac{mathrm{d}}{mathrm{d}z} = a_I A(a_I) - a_I. Observe also that all the operators A(a_i), B(b_j) and zfrac{mathrm{d}}{mathrm{d}z} commute. We have prod_{i=1}^p left( zfrac{mathrm{d}}{mathrm{d}z} + a_i right) = left(prod_{i=1, i ne I}^p left( zfrac{mathrm{d}}{mathrm{d}z} + a_i right)right) a_I A(a_I), so this gives us the first half of X. The other half does not have such a nice expression. We find zfrac{mathrm{d}}{dz} prod_{j=1}^q left(zfrac{mathrm{d}}{dz} + b_j-1 right) = left(a_I A(a_I) - a_Iright) prod_{j=1}^q left(a_I A(a_I) - a_I + b_j - 1right). Since the first half had no constant term, we infer r = -a_Iprod_{j=1}^q(b_j - 1 -a_I).

This tells us under which conditions we can "un-shift" A(a_I), namely when a_I ne 0 and r ne 0. Substituting a_I - 1 for a_I then tells us under what conditions we can decrement the index a_I. Doing a similar analysis for B(a_J), we arrive at the following rules:

• An index a_I can be decremented if a_I ne 1 and a_I ne b_j for all b_j.
• An index b_J can be incremented if b_J ne -1 and b_J ne a_i for all a_i.

Combined with the conditions (stated above) for the existence of shift operators, we have thus established the rules of the game!

### Reduction of Order

Notice that, quite trivially, if a_I = b_J, we have {}_p F_qleft({a_p atop b_q} middle| z right) = {}_{p-1} F_{q-1}left({a_p^* atop b_q^*} middle| z right), where a_p^* means a_p with a_I omitted, and similarly for b_q^*. We call this reduction of order.

In fact, we can do even better. If a_I - b_J in mathbb{Z}_{>0}, then it is easy to see that frac{(a_I)_n}{(b_J)_n} is actually a polynomial in n. It is also easy to see that (zfrac{mathrm{d}}{mathrm{d}z})^k z^n = n^k z^n. Combining these two remarks we find:

If a_I - b_J in mathbb{Z}_{>0}, then there exists a polynomial p(n) = p_0 + p_1 n + ldots (of degree a_I - b_J) such that frac{(a_I)_n}{(b_J)_n} = p(n) and {}_p F_qleft({a_p atop b_q} middle| z right) = left(p_0 + p_1 zfrac{mathrm{d}}{mathrm{d}z} + p_2 left(zfrac{mathrm{d}}{mathrm{d}z}right)^2 + ldots right) {}_{p-1} F_{q-1}left({a_p^* atop b_q^*} middle| z right).

Thus any set of parameters a_p, b_q is reachable from a set of parameters c_r, d_s where c_i - d_j in mathbb{Z} implies c_i < d_j. Such a set of parameters c_r, d_s is called suitable. Our database of known formulae should only contain suitable origins. The reasons are twofold: firstly, working from suitable origins is easier, and secondly, a formula for a non-suitable origin can be deduced from a lower order formula, and we should put this one into the database instead.

### Moving Around in the Parameter Space

It remains to investigate the following question: suppose a_p, b_q and a_p^0, b_q^0 are both suitable, and also a_i - a_i^0 in mathbb{Z}, b_j - b_j^0 in mathbb{Z}. When is a_p, b_q reachable from a_p^0, b_q^0? It is clear that we can treat all parameters independently that are incongruent mod 1. So assume that a_i and b_j are congruent to r mod 1, for all i and j. The same then follows for a_i^0 and b_j^0.

If r ne 0, then any such a_p, b_q is reachable from any a_p^0, b_q^0. To see this notice that there exist constants c, c^0, congruent mod 1, such that a_i < c < b_j for all i and j, and similarly a_i^0 < c^0 < b_j^0. If n = c - c^0 > 0 then we first inverse-shift all the b_j^0 n times up, and then similarly shift shift up all the a_i^0 n times. If n < 0 then we first inverse-shift down the a_i^0 and then shift down the b_j^0. This reduces to the case c = c^0. But evidently we can now shift or inverse-shift around the a_i^0 arbitrarily so long as we keep them less than c, and similarly for the b_j^0 so long as we keep them bigger than c. Thus a_p, b_q is reachable from a_p^0, b_q^0.

If r = 0 then the problem is slightly more involved. WLOG no parameter is zero. We now have one additional complication: no parameter can ever move through zero. Hence a_p, b_q is reachable from a_p^0, b_q^0 if and only if the number of a_i < 0 equals the number of a_i^0 < 0, and similarly for the b_i and b_i^0. But in a suitable set of parameters, all b_j > 0! This is because the Hypergeometric function is undefined if one of the b_j is a non-positive integer and all a_i are smaller than the b_j. Hence the number of b_j le 0 is always zero.

We can thus associate to every suitable set of parameters a_p, b_q, where no a_i = 0, the following invariants:

• For every r in [0, 1) the number alpha_r of parameters a_i equiv r pmod{1}, and similarly the number beta_r of parameters b_i equiv r pmod{1}.
• The number gamma of integers a_i with a_i < 0.

The above reasoning shows that a_p, b_q is reachable from a_p^0, b_q^0 if and only if the invariants alpha_r, beta_r, gamma all agree. Thus in particular "being reachable from" is a symmetric relation on suitable parameters without zeros.

### Applying the Operators

If all goes well then for a given set of parameters we find an origin in our database for which we have a nice formula. We now have to apply (potentially) many differential operators to it. If we do this blindly then the result will be very messy. This is because with Hypergeometric type functions, the derivative is usually expressed as a sum of two contiguous functions. Hence if we compute N derivatives, then the answer will involve 2N contiguous functions! This is clearly undesirable. In fact we know from the Hypergeometric differential equation that we need at most max(p, q+1) contiguous functions to express all derivatives.

Hence instead of differentiating blindly, we will work with a mathbb{C}(z)-module basis: for an origin a_r^0, b_s^0 we either store (for particularly pretty answers) or compute a set of N functions (typically N = max(r, s+1)) with the property that the derivative of any of them is a mathbb{C}(z)-linear combination of them. In formulae, we store a vector B of N functions, a matrix M and a vector C (the latter two with entries in mathbb{C}(z)), with the following properties:

• {}_r F_sleft({a_r^0 atop b_s^0} middle| z right) = C B
• zfrac{mathrm{d}}{mathrm{d}z} B = M B.

Then we can compute as many derivatives as we want and we will always end up with mathbb{C}(z)-linear combination of at most N special functions.

As hinted above, B, M and C can either all be stored (for particularly pretty answers) or computed from a single {}_p F_q formula.

### Loose Ends

This describes the bulk of the hypergeometric function algorithm. There a few further tricks, described in the hyperexpand.py source file. The extension to Meijer G-functions is also described there.

## Meijer G-Functions of Finite Confluence

Slater's theorem essentially evaluates a G-function as a sum of residues. If all poles are simple, the resulting series can be recognised as hypergeometric series. Thus a G-function can be evaluated as a sum of Hypergeometric functions.

If the poles are not simple, the resulting series are not hypergeometric. This is known as the "confluent" or "logarithmic" case (the latter because the resulting series tend to contain logarithms). The answer depends in a complicated way on the multiplicities of various poles, and there is no accepted notation for representing it (as far as I know). However if there are only finitely many multiple poles, we can evaluate the G function as a sum of hypergeometric functions, plus finitely many extra terms. I could not find any good reference for this, which is why I work it out here.

Recall the general setup. We define

G(z) = \frac{1}{2\pi i} \int_L \frac{\prod_{j=1}^m \Gamma(b_j - s)
\prod_{j=1}^n \Gamma(1 - a_j + s)}{\prod_{j=m+1}^q \Gamma(1 - b_j + s)
\prod_{j=n+1}^p \Gamma(a_j - s)} z^s \mathrm{d}s,


where L is a contour starting and ending at +infty, enclosing all of the poles of Gamma(b_j - s) for j = 1, ldots, n once in the negative direction, and no other poles. Also the integral is assumed absolutely convergent.

In what follows, for any complex numbers a, b, we write a equiv b pmod{1} if and only if there exists an integer k such that a - b = k. Thus there are double poles iff a_i equiv a_j pmod{1} for some i ne j le n.

We now assume that whenever b_j equiv a_i pmod{1} for i le m, j > n then b_j < a_i. This means that no quotient of the relevant gamma functions is a polynomial, and can always be achieved by "reduction of order". Fix a complex number c such that {b_i | b_i equiv c pmod{1}, i le m} is not empty. Enumerate this set as b, b+k_1, ldots, b+k_u, with k_i non-negative integers. Enumerate similarly {a_j | a_j equiv c pmod{1}, j > n} as b + l_1, ldots, b + l_v. Then l_i > k_j for all i, j. For finite confluence, we need to assume v ge u for all such c.

Let c_1, ldots, c_w be distinct pmod{1} and exhaust the congruence classes of the b_i. I claim

G(z) = -\sum_{j=1}^w (F_j(z) + R_j(z)),


where F_j(z) is a hypergeometric function and R_j(z) is a finite sum, both to be specified later. Indeed corresponding to every c_j there is a sequence of poles, at mostly finitely many of them multiple poles. This is where the j-th term comes from.

Hence fix again c, enumerate the relevant b_i as b, b + k_1, ldots, b + k_u. We will look at the a_j corresponding to a + l_1, ldots, a + l_u. The other a_i are not treated specially. The corresponding gamma functions have poles at (potentially) s = b + r for r = 0, 1, ldots. For r ge l_u, pole of the integrand is simple. We thus set

R(z) = \sum_{r=0}^{l_u - 1} res_{s = r + b}.


We finally need to investigate the other poles. Set r = l_u + t, t ge 0. A computation shows

\frac{\Gamma(k_i - l_u - t)}{\Gamma(l_i - l_u - t)}
= \frac{1}{(k_i - l_u - t)_{l_i - k_i}}
= \frac{(-1)^{\delta_i}}{(l_u - l_i + 1)_{\delta_i}}
\frac{(l_u - l_i + 1)_t}{(l_u - k_i + 1)_t},


where delta_i = l_i - k_i.

Also

\Gamma(b_j - l_u - b - t) =
\frac{\Gamma(b_j - l_u - b)}{(-1)^t(l_u + b + 1 - b_j)_t}, \\

\Gamma(1 - a_j + l_u + b + t) =
\Gamma(1 - a_j + l_u + b) (1 - a_j + l_u + b)_t


and

res_{s = b + l_u + t} \Gamma(b - s) = -\frac{(-1)^{l_u + t}}{(l_u + t)!}
= -\frac{(-1)^{l_u}}{l_u!} \frac{(-1)^t}{(l_u+1)_t}.


Hence

res_{s = b + l_u + t} =& -z^{b + l_u}
\frac{(-1)^{l_u}}{l_u!}
\prod_{i=1}^{u} \frac{(-1)^{\delta_i}}{(l_u - k_i + 1)_{\delta_i}}
\frac{\prod_{j=1}^n \Gamma(1 - a_j + l_u + b)
\prod_{j=1}^m \Gamma(b_j - l_u - b)^*}
{\prod_{j=n+1}^p \Gamma(a_j - l_u - b)^* \prod_{j=m+1}^q
\Gamma(1 - b_j + l_u + b)}
\\ &\times
z^t
\frac{(-1)^t}{(l_u+1)_t}
\prod_{i=1}^{u} \frac{(l_u - l_i + 1)_t}{(l_u - k_i + 1)_t}
\frac{\prod_{j=1}^n (1 - a_j + l_u + b)_t
\prod_{j=n+1}^p (-1)^t (l_u + b + 1 - a_j)_t^*}
{\prod_{j=1}^m (-1)^t (l_u + b + 1 - b_j)_t^*
\prod_{j=m+1}^q (1 - b_j + l_u + b)_t},


where the * means to omit the terms we treated specially.

We thus arrive at

F(z) = C \times {}_{p+1}F_{q}\left(
\begin{matrix} 1, (1 + l_u - l_i), (1 + l_u + b - a_i)^* \\
1 + l_u, (1 + l_u - k_i), (1 + l_u + b - b_i)^*
\end{matrix} \middle| (-1)^{p-m-n} z\right),


where C designates the factor in the residue independent of t. (This result can also be written in slightly simpler form by converting all the l_u etc back to a_* - b_*, but doing so is going to require more notation still and is not helpful for computation.)

## Extending The Hypergeometric Tables

Adding new formulae to the tables is straightforward. At the top of the file diofant/simplify/hyperexpand.py, there is a function called :func:~diofant.simplify.hyperexpand.add_formulae. Nested in it are defined two helpers, add(ap, bq, res) and addb(ap, bq, B, C, M), as well as dummys a, b, c, and z.

The first step in adding a new formula is by using add(ap, bq, res). This declares hyper(ap, bq, z) == res. Here ap and bq may use the dummys a, b, and c as free symbols. For example the well-known formula sum_0^infty frac{(-a)_n z^n}{n!} = (1-z)^a is declared by the following line: add((-a, ), (), (1-z)**a).

From the information provided, the matrices B, C and M will be computed, and the formula is now available when expanding hypergeometric functions. Next the test file diofant/simplify/tests/test_hyperexpand.py should be run, in particular the test test_formulae. This will test the newly added formula numerically. If it fails, there is (presumably) a typo in what was entered.

Since all newly-added formulae are probably relatively complicated, chances are that the automatically computed basis is rather suboptimal (there is no good way of testing this, other than observing very messy output). In this case the matrices B, C and M should be computed by hand. Then the helper addb can be used to declare a hypergeometric formula with hand-computed basis.

### An example

Because this explanation so far might be very theoretical and difficult to understand, we walk through an explicit example now. We take the Fresnel function C(z) which obeys the following hypergeometric representation:

C(z) = z \cdot {}_{1}F_{2}\left.\left(
\begin{matrix} \frac{1}{4} \\
\frac{1}{2}, \frac{5}{4}
\end{matrix} \right| -\frac{\pi^2 z^4}{16}\right) \,.


First we try to add this formula to the lookup table by using the (simpler) function add(ap, bq, res). The first two arguments are simply the lists containing the parameter sets of {}_{1}F_{2}. The res argument is a little bit more complicated. We only know C(z) in terms of {}_{1}F_{2}(ldots | f(z)) with f a function of z, in our case

f(z) = -\frac{\pi^2 z^4}{16} \,.


What we need is a formula where the hypergeometric function has only z as argument {}_{1}F_{2}(ldots | z). We introduce the new complex symbol w and search for a function g(w) such that

f(g(w)) = w


holds. Then we can replace every z in C(z) by g(w). In the case of our example the function g could look like

g(w) = \frac{2}{\sqrt{\pi}} \exp\left(\frac{i \pi}{4}\right) w^{\frac{1}{4}} \,.


We get these functions mainly by guessing and testing the result. Hence we proceed by computing f(g(w)) (and simplifying naively)

f(g(w)) &= -\frac{\pi^2 g(w)^4}{16} \\
&= -\frac{\pi^2 g\left(\frac{2}{\sqrt{\pi}} \exp\left(\frac{i \pi}{4}\right) w^{\frac{1}{4}}\right)^4}{16} \\
&= -\frac{\pi^2 \frac{2^4}{\sqrt{\pi}^4} \exp\left(\frac{i \pi}{4}\right)^4 {w^{\frac{1}{4}}}^4}{16} \\
&= -\exp\left(i \pi\right) w \\
&= w


and indeed get back w. (In case of branched functions we have to be aware of branch cuts. In that case we take w to be a positive real number and check the formula. If what we have found works for positive w, then just replace :func:~diofant.functions.elementary.exponential.exp inside any branched function by :func:~diofant.functions.elementary.exponential.exp\_polar and what we get is right for all w.) Hence we can write the formula as

C(g(w)) = g(w) \cdot {}_{1}F_{2}\left.\left(
\begin{matrix} \frac{1}{4} \\
\frac{1}{2}, \frac{5}{4}
\end{matrix} \right| w\right) \,.


and trivially

{}_{1}F_{2}\left.\left(
\begin{matrix} \frac{1}{4} \\
\frac{1}{2}, \frac{5}{4}
\end{matrix} \right| w\right)
= \frac{C(g(w))}{g(w)}
= \frac{C\left(\frac{2}{\sqrt{\pi}} \exp\left(\frac{i \pi}{4}\right) w^{\frac{1}{4}}\right)}
{\frac{2}{\sqrt{\pi}} \exp\left(\frac{i \pi}{4}\right) w^{\frac{1}{4}}}


which is exactly what is needed for the third paramenter, res, in add. Finally, the whole function call to add this rule to the table looks like:

add([Rational(1, 4)],
[Rational(1, 2), Rational(5, 4)],
fresnelc(exp(pi*I/4)*root(z,4)*2/sqrt(pi)) / (exp(pi*I/4)*root(z,4)*2/sqrt(pi))
)


Using this rule we will find that it works but the results are not really nice in terms of simplicity and number of special function instances included. We can obtain much better results by adding the formula to the lookup table in another way. For this we use the (more complicated) function addb(ap, bq, B, C, M). The first two arguments are again the lists containing the parameter sets of {}_{1}F_{2}. The remaining three are the matrices mentioned earlier on this page.

We know that the n = max{left(p, q+1right)}-th derivative can be expressed as a linear combination of lower order derivatives. The matrix B contains the basis {B_0, B_1, ldots} and is of shape n times 1. The best way to get B_i is to take the first n = max(p, q+1) derivatives of the expression for {}_p F_q and take out useful pieces. In our case we find that n = max{left(1, 2+1right)} = 3. For computing the derivatives, we have to use the operator zfrac{mathrm{d}}{mathrm{d}z}. The first basis element B_0 is set to the expression for {}_1 F_2 from above:

B_0 = \frac{ \sqrt{\pi} \exp\left(-\frac{\mathbf{\imath}\pi}{4}\right)
C\left( \frac{2}{\sqrt{\pi}} \exp\left(\frac{\mathbf{\imath}\pi}{4}\right) z^{\frac{1}{4}}\right)}
{2 z^{\frac{1}{4}}}


Next we compute zfrac{mathrm{d}}{mathrm{d}z} B_0. For this we can directly use Diofant!

>>> B0 = sqrt(pi)*exp(-I*pi/4)*fresnelc(2*root(z, 4)*exp(I*pi/4)/sqrt(pi))/(2*root(z, 4))
>>> z * diff(B0, z)
z*(cosh(2*sqrt(z))/(4*z) - E**(-I*pi/4)*sqrt(pi)*fresnelc(2*E**(I*pi/4)*z**(1/4)/sqrt(pi))/(8*z**(5/4)))
>>> expand(_)
cosh(2*sqrt(z))/4 - E**(-I*pi/4)*sqrt(pi)*fresnelc(2*E**(I*pi/4)*z**(1/4)/sqrt(pi))/(8*z**(1/4))

Formatting this result nicely we obtain

B_1^\prime =
- \frac{1}{4} \frac{
\sqrt{\pi}
\exp\left(-\frac{\mathbf{\imath}\pi}{4}\right)
C\left( \frac{2}{\sqrt{\pi}} \exp\left(\frac{\mathbf{\imath}\pi}{4}\right) z^{\frac{1}{4}}\right)
}
{2 z^{\frac{1}{4}}}
+ \frac{1}{4} \cosh{\left( 2 \sqrt{z} \right )}


Computing the second derivative we find

>>> B1prime = cosh(2*sqrt(z))/4 - sqrt(pi)*exp(-I*pi/4)*\
...           fresnelc(2*root(z,4)*exp(I*pi/4)/sqrt(pi))/(8*root(z,4))
>>> z * diff(B1prime, z)
z*(-cosh(2*sqrt(z))/(16*z) + sinh(2*sqrt(z))/(4*sqrt(z)) + E**(-I*pi/4)*sqrt(pi)*fresnelc(2*E**(I*pi/4)*z**(1/4)/sqrt(pi))/(32*z**(5/4)))
>>> expand(_)
sqrt(z)*sinh(2*sqrt(z))/4 - cosh(2*sqrt(z))/16 + E**(-I*pi/4)*sqrt(pi)*fresnelc(2*E**(I*pi/4)*z**(1/4)/sqrt(pi))/(32*z**(1/4))

which can be printed as

B_2^\prime =
\frac{1}{16} \frac{
\sqrt{\pi}
\exp\left(-\frac{\mathbf{\imath}\pi}{4}\right)
C\left( \frac{2}{\sqrt{\pi}} \exp\left(\frac{\mathbf{\imath}\pi}{4}\right) z^{\frac{1}{4}}\right)
}
{2 z^{\frac{1}{4}}}
- \frac{1}{16} \cosh{\left(2\sqrt{z}\right)}
+ \frac{1}{4} \sinh{\left(2\sqrt{z}\right)} \sqrt{z}


We see the common pattern and can collect the pieces. Hence it makes sense to choose B_1 and B_2 as follows

B =
\left( \begin{matrix}
B_0 \\ B_1 \\ B_2
\end{matrix} \right)
=
\left( \begin{matrix}
\frac{
\sqrt{\pi}
\exp\left(-\frac{\mathbf{\imath}\pi}{4}\right)
C\left( \frac{2}{\sqrt{\pi}} \exp\left(\frac{\mathbf{\imath}\pi}{4}\right) z^{\frac{1}{4}}\right)
}{2 z^{\frac{1}{4}}} \\
\cosh\left(2\sqrt{z}\right) \\
\sinh\left(2\sqrt{z}\right) \sqrt{z}
\end{matrix} \right)


(This is in contrast to the basis B = left(B_0, B_1^prime, B_2^primeright) that would have been computed automatically if we used just add(ap, bq, res).)

Because it must hold that {}_p F_qleft(cdots middle| z right) = C B the entries of C are obviously

C =
\left( \begin{matrix}
1 \\ 0 \\ 0
\end{matrix} \right)


Finally we have to compute the entries of the 3 times 3 matrix M such that zfrac{mathrm{d}}{mathrm{d}z} B = M B holds. This is easy. We already computed the first part zfrac{mathrm{d}}{mathrm{d}z} B_0 above. This gives us the first row of M. For the second row we have:

>>> B1 = cosh(2*sqrt(z))
>>> z * diff(B1, z)
sqrt(z)*sinh(2*sqrt(z))

and for the third one

>>> B2 = sinh(2*sqrt(z))*sqrt(z)
>>> expand(z * diff(B2, z))
sqrt(z)*sinh(2*sqrt(z))/2 + z*cosh(2*sqrt(z))

Now we have computed the entries of this matrix to be

M =
\left( \begin{matrix}
-\frac{1}{4} & \frac{1}{4} & 0 \\
0            & 0           & 1 \\
0            & z           & \frac{1}{2} \\
\end{matrix} \right)


Note that the entries of C and M should typically be rational functions in z, with rational coefficients. This is all we need to do in order to add a new formula to the lookup table for hyperexpand.

## Implemented Hypergeometric Formulae

A vital part of the algorithm is a relatively large table of hypergeometric function representations. The following automatically generated list contains all the representations implemented in Diofant (of course many more are derived from them). These formulae are mostly taken from [Luke1969] and [Prudnikov1990]. They are all tested numerically.

.. automodule:: diofant.simplify.hyperexpand_doc


.. autofunction:: diofant.simplify.hyperexpand.add_formulae



## References

 [Roach1996] Kelly B. Roach. Hypergeometric Function Representations. In: Proceedings of the 1996 International Symposium on Symbolic and Algebraic Computation, pages 301-308, New York, 1996. ACM.
 [Roach1997] Kelly B. Roach. Meijer G Function Representations. In: Proceedings of the 1997 International Symposium on Symbolic and Algebraic Computation, pages 205-211, New York, 1997. ACM.
 [Luke1969] Luke, Y. L. (1969), The Special Functions and Their Approximations, Volume 1.
 [Prudnikov1990] A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990). Integrals and Series: More Special Functions, Vol. 3, Gordon and Breach Science Publisher.