Although the `gp`

calculator is programmable, it is useful to have
a number of preprogrammed loops, including sums, products, and a certain
number of recursions. Also, a number of functions from numerical analysis
like numerical integration and summation of series will be described here.

One of the parameters in these loops must be the control variable, hence a simple variable name. In the descriptions, the letter X will always denote any simple variable name, and represents the formal parameter used in the function. The expression to be summed, integrated, etc. is any legal PARI expression, including of course expressions using loops.

**Library mode.**
Since it is easier to program directly the loops in library mode, these
functions are mainly useful for GP programming. On the other hand, numerical
routines code a function (to be integrated, summed, etc.) with two parameters
named

GEN (*eval)(void*,GEN) void *E; \\ context: eval(E, x) must evaluate your function at x.

see the Libpari manual for details.

**Numerical integration.**
Starting with version 2.2.9 the "double exponential" univariate
integration method is implemented in `intnum`

and its variants. Romberg
integration is still available under the name `intnumromb`

, but
superseded. It is possible to compute numerically integrals to thousands of
decimal places in reasonable time, as long as the integrand is regular. It is
also reasonable to compute numerically integrals in several variables,
although more than two becomes lengthy. The integration domain may be
non-compact, and the integrand may have reasonable singularities at
endpoints. To use `intnum`

, you must split the integral into a sum
of subintegrals where the function has no singularities except at the
endpoints. Polynomials in logarithms are not considered singular, and
neglecting these logs, singularities are assumed to be algebraic (asymptotic
to C(x-a)^{-α} for some α > -1 when x is
close to a), or to correspond to simple discontinuities of some (higher)
derivative of the function. For instance, the point 0 is a singularity of
abs(x).

See also the discrete summation methods below, sharing the prefix `sum`

.

Asymptotic expansion of *expr*, corresponding to a sequence u(n),
assuming it has the shape
u(n) ~ ∑_{i ≥ 0} a_{i} n^{-iα}
with rational coefficients a_{i} with reasonable height; the algorithm
is heuristic and performs repeated calls to limitnum, with
`alpha`

as in `limitnum`

. As in `limitnum`

, u(n) may be
given either by a closure n` ⟼ `

u(n) or as a closure N` ⟼ `

[u(1),...,u(N)], the latter being often more efficient.

? f(n) = n! / (n^n*exp(-n)*sqrt(n)); ? asympnum(f) %2 = [] \\ failure ! ? localprec(57); l = limitnum(f) %3 = 2.5066282746310005024157652848110452530 ? asympnum(n->f(n)/l) \\ normalize %4 = [1, 1/12, 1/288, -139/51840, -571/2488320, 163879/209018880, 5246819/75246796800]

and we indeed get a few terms of Stirling's expansion. Note that it definitely helps to normalize with a limit computed to higher accuracy (as a rule of thumb, multiply the bit accuracy by 1.612):

? l = limitnum(f) ? asympnum(n->f(n) / l) \\ failure again !!! %6 = []

We treat again the example of the Motzkin numbers M_{n} given
in `limitnum`

:

\\ [M_{k}, M_{k*2}, ..., M_{k*N}] / (3^n / n^(3/2)) ? vM(N, k = 1) = { my(q = k*N, V); if (q == 1, return ([1/3])); V = vector(q); V[1] = V[2] = 1; for(n = 2, q - 1, V[n+1] = ((2*n + 1)*V[n] + 3*(n - 1)*V[n-1]) / (n + 2)); f = (n -> 3^n / n^(3/2)); return (vector(N, n, V[n*k] / f(n*k))); } ? localprec(100); l = limitnum(n->vM(n,10)); \\ 3/sqrt(12*Pi) ? \p38 ? asympnum(n->vM(n,10)/l) %2 = [1, -3/32, 101/10240, -1617/1638400, 505659/5242880000, ...]

If `alpha`

is not a rational number, loss of accuracy is
expected, so it should be precomputed to double accuracy, say:

? \p38 ? asympnum(n->log(1+1/n^Pi),Pi) %1 = [0, 1, -1/2, 1/3, -1/4, 1/5] ? localprec(76); a = Pi; ? asympnum(n->log(1+1/n^Pi), a) \\ more terms %3 = [0, 1, -1/2, 1/3, -1/4, 1/5, -1/6, 1/7, -1/8, 1/9, -1/10, 1/11, -1/12] ? asympnum(n->log(1+1/sqrt(n)),1/2) \\ many more terms %4 = 49

The expression is evaluated for n = 1, 2,..., N for an N = O(B) if the current bit accuracy is B. If it is not defined for one of these values, translate or rescale accordingly:

? asympnum(n->log(1-1/n)) \\ can't evaluate at n = 1 ! *** at top-level: asympnum(n->log(1-1/n)) *** ^ — — — — — — — -- *** in function asympnum: log(1-1/n) *** ^ — — — - *** log: domain error in log: argument = 0 ? asympnum(n->-log(1-1/(2*n))) %5 = [0, 1/2, 1/8, 1/24, ...] ? asympnum(n->-log(1-1/(n+1))) %6 = [0, 1, -1/2, 1/3, -1/4, ...]

The library syntax is

, where **asympnum**(void *E, GEN (*u)(void *,GEN,long), GEN alpha, long prec)`u(E, n, prec)`

must return either u(n) or [u(1),...,u(n)]
in precision `prec`

. Also available is
`GEN `

, where u is a closure
as above or a vector of sufficient length.**asympnum0**(GEN u, GEN alpha, long prec)

Return the N+1 first terms of asymptotic expansion of *expr*,
corresponding to a sequence u(n), as floating point numbers. Assume
that the expansion has the shape
u(n) ~ ∑_{i ≥ 0} a_{i} n^{-iα}
and return approximation of [a_{0}, a_{1},..., a_{N}].
The algorithm is heuristic and performs repeated calls to limitnum, with
`alpha`

as in `limitnum`

. As in `limitnum`

, u(n) may be
given either by a closure n`: — >`

u(n) or as a closure N`: — >`

[u(1),...,u(N)], the latter being often more efficient. This function
is related to, but more flexible than, `asympnum`

, which requires
rational asymptotic expansions.

? f(n) = n! / (n^n*exp(-n)*sqrt(n)); ? asympnum(f) %2 = [] \\ failure ! ? v = asympnumraw(f, 10); ? v[1] - sqrt(2*Pi) %4 = 0.E-37 ? bestappr(v / v[1], 2^60) %5 = [1, 1/12, 1/288, -139/51840, -571/2488320, 163879/209018880,...]

and we indeed get a few terms of Stirling's expansion (the
first 9 terms are correct).
If u(n) has an asymptotic expansion in n^{-α} with α not an
integer, the default alpha = 1 is inaccurate:

? f(n) = (1+1/n^(7/2))^(n^(7/2)); ? v1 = asympnumraw(f,10); ? v1[1] - exp(1) %8 = 4.62... E-12 ? v2 = asympnumraw(f,10,7/2); ? v2[1] - exp(1) %7 0.E-37

As in `asympnum`

, if `alpha`

is not a rational number,
loss of accuracy is expected, so it should be precomputed to double
accuracy, say.

The library syntax is

, where **asympnumraw**(void *E, GEN (*u)(void *,GEN,long), long N, GEN alpha, long prec)`u(E, n, prec)`

must return either u(n) or
[u(1),...,u(n)] in precision `prec`

.
Also available is
`GEN `

where u is either
a closure as above or a vector of sufficient length.**asympnumraw0**(GEN u, GEN alpha, long prec)

Given a continued fraction `CF`

output by `contfracinit`

, evaluate
the first `lim`

terms of the continued fraction at `t`

(all
terms if `lim`

is negative or omitted; if positive, `lim`

must be
less than or equal to the length of `CF`

.

The library syntax is `GEN `

.**contfraceval**(GEN CF, GEN t, long lim)

Given M representing the power series S = ∑_{n ≥ 0} M[n+1]z^n,
transform it into a continued fraction; restrict to n ≤ `lim`

if latter is non-negative. M can be a vector, a power
series, a polynomial; if the limiting parameter `lim`

is present, a
rational function is also allowed (and converted to a power series of that
accuracy).

The result is a 2-component vector [A,B] such that S = M[1] / (1+A[1]z+B[1]z^2/(1+A[2]z+B[2]z^2/(1+...1/(1+A[lim/2]z)))). Does not work if any coefficient of M vanishes, nor for series for which certain partial denominators vanish.

The library syntax is `GEN `

.**contfracinit**(GEN M, long lim)

Numerical derivation of *expr* with respect to X at X = a. The
order of derivation is 1 by default.

? derivnum(x=0, sin(exp(x))) - cos(1) %1 = 0.E-38

A clumsier approach, which would not work in library mode, is

? f(x) = sin(exp(x)) ? f'(0) - cos(1) %2 = 0.E-38

***** When a is a numerical type (integer, rational number, real number or
`t_COMPLEX`

of such), performs numerical derivation.

***** When a is a (polynomial, rational function or) power series, compute
`derivnum(t = a,f)`

as f'(a) = (f(a))'/a':

? derivnum(x = 1 + t, sqrt(x)) %1 = 1/2 - 1/4*t + 3/16*t^2 - 5/32*t^3 + ... + O(t^16) ? derivnum(x = 1/(1 + t), sqrt(x)) %2 = 1/2 + 1/4*t - 1/16*t^2 + 1/32*t^3 + ... + O(t^16) ? derivnum(x = 1 + t + O(t^17), sqrt(x)) %3 = 1/2 - 1/4*t + 3/16*t^2 - 5/32*t^3 + ... + O(t^16)

If the parameter *ind* is present, it can be

***** a non-negative integer m, in which case we return f^{(m)}(x);

***** or a vector of orders, in which case we return the vector of
derivatives.

? derivnum(x = 0, exp(sin(x)), 16) \\ 16-th derivative %1 = -52635599.000000000000000000000000000000 ? round( derivnum(x = 0, exp(sin(x)), [0..13]) ) \\ 0-13-th derivatives %2 = [1, 1, 1, 0, -3, -8, -3, 56, 217, 64, -2951, -12672, 5973, 309376]

The library syntax is

.
Also available is
**derivfunk**(void *E, GEN (*eval)(void*,GEN), GEN a, GEN ind, long prec)`GEN `

.
If a is a numerical type (**derivfun**(void *E, GEN (*eval)(void *, GEN), GEN a, long prec)`t_INT`

, `t_FRAC`

, `t_REAL`

or
`t_COMPLEX`

of such, we have
`GEN `

and
**derivnumk**(void *E, GEN (*eval)(void *, GEN, long), GEN a, GEN ind, long prec)`GEN `

**derivnum**(void *E, GEN (*eval)(void *, GEN, long prec), GEN a, long prec)

Numerical
integration of (2iπ)^{-1}*expr* with respect to X on the circle
|X-a |= R.
In other words, when *expr* is a meromorphic
function, sum of the residues in the corresponding disk; *tab* is as in
`intnum`

, except that if computed with `intnuminit`

it should be with
the endpoints `[-1, 1]`

.

? \p105 ? intcirc(s=1, 0.5, zeta(s)) - 1 time = 496 ms. %1 = 1.2883911040127271720 E-101 + 0.E-118*I

The library syntax is

.**intcirc**(void *E, GEN (*eval)(void*,GEN), GEN a,GEN R,GEN tab, long prec)

Initialize tables for use with integral transforms (such as Fourier,
Laplace or Mellin transforms) in order to compute
∫_{a}^b f(t) k(t,z) dt
for some kernel k(t,z).
The endpoints a and b are coded as in `intnum`

, f is the
function to which the integral transform is to be applied and the
non-negative integer m is as in `intnum`

: multiply the number of
sampling points roughly by 2^m, hopefully increasing the accuracy. This
function is particularly useful when the function f is hard to compute,
such as a gamma product.

**Limitation.** The endpoints a and b must be at infinity,
with the same asymptotic behavior. Oscillating types are not supported.
This is easily overcome by integrating vectors of functions, see example
below.

**Examples.**

***** numerical Fourier transform
F(z) = ∫_{- oo }^{+ oo } f(t)e^{-2iπ z t} dt.
First the easy case, assume that f decrease exponentially:

f(t) = exp(-t^2); A = [-oo,1]; B = [+oo,1]; \p200 T = intfuncinit(t = A,B , f(t)); F(z) = { my(a = -2*I*Pi*z); intnum(t = A,B, exp(a*t), T); } ? F(1) - sqrt(Pi)*exp(-Pi^2) %1 = -1.3... E-212

Now the harder case, f decrease slowly: we must specify the oscillating behavior. Thus, we cannot precompute usefully since everything depends on the point we evaluate at:

f(t) = 1 / (1+ abs(t)); \p200 \\ Fourier cosine transform FC(z) = { my(a = 2*Pi*z); intnum(t = [-oo, a*I], [+oo, a*I], cos(a*t)*f(t)); } FC(1)

***** Fourier coefficients: we must integrate over a period, but
`intfuncinit`

does not support finite endpoints.
The solution is to integrate a vector of functions !

FourierSin(f, T, k) = \\ first k sine Fourier coeffs { my (w = 2*Pi/T); my (v = vector(k+1)); intnum(t = -T/2, T/2, my (z = exp(I*w*t)); v[1] = z; for (j = 2, k, v[j] = v[j-1]*z); f(t) * imag(v)) * 2/T; } FourierSin(t->sin(2*t), 2*Pi, 10)

The same technique can be used instead of `intfuncinit`

to integrate f(t) k(t,z) whenever the list of z-values is known
beforehand.

Note that the above code includes an unrelated optimization: the sin(j w t) are computed as imaginary parts of exp(i j w t) and the latter by successive multiplications.

***** numerical Mellin inversion
F(z) = (2iπ)^{-1} ∫_{c -i oo }^{c+i oo } f(s)z^{-s} ds
= (2π)^{-1} ∫_{- oo }^{+ oo }
f(c + i t)e^{-log z(c + it)} dt.
We take c = 2 in the program below:

f(s) = gamma(s)^3; \\ f(c+it) decrease as exp(-3Pi|t|/2) c = 2; \\ arbitrary A = [-oo,3*Pi/2]; B = [+oo,3*Pi/2]; T = intfuncinit(t=A,B, f(c + I*t)); F(z) = { my (a = -log(z)); intnum(t=A,B, exp(a*I*t), T)*exp(a*c) / (2*Pi); }

The library syntax is

.**intfuncinit**(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b,long m, long prec)

Numerical integration
of *expr* on ]a,b[ with respect to X, using the
double-exponential method, and thus O(Dlog D) evaluation of
the integrand in precision D. The integrand may have values
belonging to a vector space over the real numbers; in particular, it can be
complex-valued or vector-valued. But it is assumed that the function is
regular on ]a,b[. If the endpoints a and b are finite and the
function is regular there, the situation is simple:

? intnum(x = 0,1, x^2) %1 = 0.3333333333333333333333333333 ? intnum(x = 0,Pi/2, [cos(x), sin(x)]) %2 = [1.000000000000000000000000000, 1.000000000000000000000000000]

An endpoint equal to ± oo is coded as `+oo`

or `-oo`

, as
expected:

? intnum(x = 1,+oo, 1/x^2) %3 = 1.000000000000000000000000000

In basic usage, it is assumed that the function does not decrease exponentially fast at infinity:

? intnum(x=0,+oo, exp(-x)) *** at top-level: intnum(x=0,+oo,exp(- *** ^ — — — — — — -- *** exp: overflow in expo().

We shall see in a moment how to avoid that last problem, after describing
the last *optional* argument *tab*.

**The tab.**

***** a non-negative integer m, we multiply the number of sampling points
by 2^m, hopefully increasing accuracy. Note that the running time
increases roughly by a factor 2^m. One may try consecutive values of m
until they give the same value up to an accepted error.

***** a set of integration tables containing precomputed x_{i} and w_{i}
as output by `intnuminit`

. This is useful if several integrations of
the same type are performed (on the same kind of interval and functions,
for a given accuracy): we skip a precomputation of O(Dlog D)
elementary functions in accuracy D, whose running time has the same order
of magnitude as the evaluation of the integrand. This is in particular
useful for multivariate integrals.

**Specifying the behavior at endpoints.** This is done as follows.
An endpoint a is either given as such (a scalar,
real or complex, `oo`

or `-oo`

for ± oo ), or as a two
component vector [a,α], to indicate the behavior of the integrand in a
neighborhood of a.

If a is finite, the code [a,α] means the function has a
singularity of the form (x-a)^{α}, up to logarithms. (If α \ge
0, we only assume the function is regular, which is the default assumption.)
If a wrong singularity exponent is used, the result will lose decimals:

? c = -9/10; ? intnum(x=0, 1, x^c) \\ assume x^{-9/10}is regular at 0 %1 = 9.9999839078827082322596783301939063944 ? intnum(x=[0,c], 1, x^c) \\ no, it's not %2 = 10.000000000000000000000000000000000000 ? intnum(x=[0,c/2], 1, x^c) \\ using a wrong exponent is bad %3 = 9.9999999997122749095442279375719919769

If a is ± oo , which is coded as `+oo`

or `-oo`

,
the situation is more complicated, and [±`oo`

,α] means:

***** α = 0 (or no α at all, i.e. simply ±`oo`

)
assumes that the integrand tends to zero moderately quickly, at least as
O(x^{-2}) but not exponentially fast.

***** α > 0 assumes that the function tends to zero exponentially fast
approximately as exp(-α x). This includes oscillating but quickly
decreasing functions such as exp(-x)sin(x).

? intnum(x=0, +oo, exp(-2*x)) *** at top-level: intnum(x=0,+oo,exp(- *** ^ — — — — — — -- *** exp: exponent (expo) overflow ? intnum(x=0, [+oo, 2], exp(-2*x)) \\ OK! %1 = 0.50000000000000000000000000000000000000 ? intnum(x=0, [+oo, 3], exp(-2*x)) \\ imprecise exponent, still OK ! %2 = 0.50000000000000000000000000000000000000 ? intnum(x=0, [+oo, 10], exp(-2*x)) \\ wrong exponent ==> disaster %3 = 0.49999999999952372962457451698256707393

As the last exemple shows, the exponential decrease rate
*must* be indicated to avoid overflow, but the method is robust enough
for a rough guess to be acceptable.

***** α < -1 assumes that the function tends to 0 slowly, like
x^{α}. Here the algorithm is less robust and it is essential to give a
sharp α, unless α ≤ -2 in which case we use
the default algorithm as if α were missing (or equal to 0).

? intnum(x=1, +oo, x^(-3/2)) \\ default %1 = 1.9999999999999999999999999999646391207 ? intnum(x=1, [+oo,-3/2], x^(-3/2)) \\ precise decrease rate %2 = 2.0000000000000000000000000000000000000 ? intnum(x=1, [+oo,-11/10], x^(-3/2)) \\ worse than default %3 = 2.0000000000000000000000000089298011973

The last two codes are reserved for oscillating functions. Let k > 0 real, and g(x) a non-oscillating function tending slowly to 0 (e.g. like a negative power of x), then

***** α = k * I assumes that the function behaves like cos(kx)g(x).

***** α = -k* I assumes that the function behaves like sin(kx)g(x).

Here it is critical to give the exact value of k. If the oscillating part is not a pure sine or cosine, one must expand it into a Fourier series, use the above codings, and sum the resulting contributions. Otherwise you will get nonsense. Note that cos(kx), and similarly sin(kx), means that very function, and not a translated version such as cos(kx+a).

**Note.** If f(x) = cos(kx)g(x) where g(x) tends to zero
exponentially fast as exp(-α x), it is up to the user to choose
between [±`oo`

,α] and [±`oo`

,k* I], but a good rule of
thumb is that
if the oscillations are weaker than the exponential decrease, choose
[±`oo`

,α], otherwise choose [±`oo`

,k*I], although the
latter can reasonably be used in all cases, while the former cannot. To take
a specific example, in most inverse Mellin transforms, the integrand is a
product of an exponentially decreasing and an oscillating factor. If we
choose the oscillating type of integral we perhaps obtain the best results,
at the expense of having to recompute our functions for a different value of
the variable z giving the transform, preventing us to use a function such
as `intfuncinit`

. On the other hand using the exponential type of
integral, we obtain less accurate results, but we skip expensive
recomputations. See `intfuncinit`

for more explanations.

**Power series limits.**
The limits a and b can be power series of non-negative valuation,
giving a power series expansion for the integral -- provided it exists.

? intnum(t=0,X + O(X^3), exp(t)) %4 = 1.000...*X - 0.5000...*X^2 + O(X^3) ? bestappr( intnum(t=0,X + O(X^17), exp(t)) )- exp(X) + 1 %5 = O(X^17)

The valuation of the limit cannot be negative
since ∫_{0}^{1/X}(1+t^2)^{-1} dt = π/2 - `sign`

(X)+O(X^2).

Polynomials and rational functions are also allowed and
converted to power series using current `seriesprecision`

:

? bestappr( intnum(t=1,1+X, 1/t) ) %6 = X - 1/2*X^2 + 1/3*X^3 - 1/4*X^4 + [...] + 1/15*X^15 + O(X^16)

The function does not work if the integral is singular with the constant coefficient of the series as limit:

? intnum(t=X^2+O(X^4),1, 1/sqrt(t)) %8 = 2.000... - 6.236608109630992528 E28*X^2 + O(X^4)

however you can use

? intnum(t=[X^2+O(X^4),-1/2],1, 1/sqrt(t)) %10 = 2.000000000000000000000000000-2.000000000000000000000000000*X^2+O(X^4)

whis is translated internally to

? intnum(t=[0,-1/2],1, 1/sqrt(t))-intnum(t=[0,-1/2],X^2+O(X^4), 1/sqrt(t))

For this form the argument *tab* can be used only as an integer, not a
table precomputed by `intnuminit`

.

We shall now see many examples to get a feeling for what the various parameters achieve. All examples below assume precision is set to 115 decimal digits. We first type

? \p 115

**Apparent singularities.** In many cases, apparent singularities
can be ignored. For instance, if f(x) = 1
/(exp(x)-1) - exp(-x)/x, then ∫_{0}^ oo f(x)dx = γ, Euler's
constant `Euler`

. But

? f(x) = 1/(exp(x)-1) - exp(-x)/x ? intnum(x = 0, [oo,1], f(x)) - Euler %1 = 0.E-115

But close to 0 the function f is computed with an enormous loss of accuracy, and we are in fact lucky that it get multiplied by weights which are sufficiently close to 0 to hide this:

? f(1e-200) %2 = -3.885337784451458142 E84

A more robust solution is to define the function differently near special points, e.g. by a Taylor expansion

? F = truncate( f(t + O(t^10)) ); \\ expansion around t = 0 ? poldegree(F) %4 = 7 ? g(x) = if (x > 1e-18, f(x), subst(F,t,x)); \\ note that 7.18 > 105 ? intnum(x = 0, [oo,1], g(x)) - Euler %2 = 0.E-115

It is up to the user to determine constants such as the
10^{-18} and 10 used above.

**True singularities.** With true singularities the result is worse.
For instance

? intnum(x = 0, 1, x^(-1/2)) - 2 %1 = -3.5... E-68 \\ only 68 correct decimals ? intnum(x = [0,-1/2], 1, x^(-1/2)) - 2 %2 = 0.E-114 \\ better

**Oscillating functions.**

? intnum(x = 0, oo, sin(x) / x) - Pi/2 %1 = 16.19.. \\ nonsense ? intnum(x = 0, [oo,1], sin(x)/x) - Pi/2 %2 = -0.006.. \\ bad ? intnum(x = 0, [oo,-I], sin(x)/x) - Pi/2 %3 = 0.E-115 \\ perfect ? intnum(x = 0, [oo,-I], sin(2*x)/x) - Pi/2 \\ oops, wrong k %4 = 0.06... ? intnum(x = 0, [oo,-2*I], sin(2*x)/x) - Pi/2 %5 = 0.E-115 \\ perfect ? intnum(x = 0, [oo,-I], sin(x)^3/x) - Pi/4 %6 = -0.0008... \\ bad ? sin(x)^3 - (3*sin(x)-sin(3*x))/4 %7 = O(x^17)

We may use the above linearization and compute two oscillating integrals with
endpoints `[oo, -I]`

and `[oo, -3*I]`

respectively, or
notice the obvious change of variable, and reduce to the single integral
(1/2)∫_{0}^ oo sin(x)/xdx. We finish with some more complicated
examples:

? intnum(x = 0, [oo,-I], (1-cos(x))/x^2) - Pi/2 %1 = -0.0003... \\ bad ? intnum(x = 0, 1, (1-cos(x))/x^2) \ + intnum(x = 1, oo, 1/x^2) - intnum(x = 1, [oo,I], cos(x)/x^2) - Pi/2 %2 = 0.E-115 \\ perfect ? intnum(x = 0, [oo, 1], sin(x)^3*exp(-x)) - 0.3 %3 = -7.34... E-55 \\ bad ? intnum(x = 0, [oo,-I], sin(x)^3*exp(-x)) - 0.3 %4 = 8.9... E-103 \\ better. Try higher m ? tab = intnuminit(0,[oo,-I], 1); \\ double number of sampling points ? intnum(x = 0, oo, sin(x)^3*exp(-x), tab) - 0.3 %6 = 0.E-115 \\ perfect

**Warning.** Like `sumalt`

, `intnum`

often assigns a
reasonable value to diverging integrals. Use these values at your own risk!
For example:

? intnum(x = 0, [oo, -I], x^2*sin(x)) %1 = -2.0000000000...

Note the formula
∫_{0}^ oo sin(x)/x^sdx = cos(π s/2) Γ(1-s) ,
a priori valid only for 0 < Re(s) < 2, but the right hand side provides an
analytic continuation which may be evaluated at s = -2...

**Multivariate integration.**
Using successive univariate integration with respect to different formal
parameters, it is immediate to do naive multivariate integration. But it is
important to use a suitable `intnuminit`

to precompute data for the
*internal* integrations at least!

For example, to compute the double integral on the unit disc x^2+y^2 ≤ 1 of the function x^2+y^2, we can write

? tab = intnuminit(-1,1); ? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2, tab),tab) - Pi/2 %2 = -7.1... E-115 \\ OK

The first *tab* is essential, the second optional. Compare:

? tab = intnuminit(-1,1); time = 4 ms. ? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2)); time = 3,092 ms. \\ slow ? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2, tab), tab); time = 252 ms. \\ faster ? intnum(x=-1,1, intnum(y=-sqrt(1-x^2),sqrt(1-x^2), x^2+y^2, tab)); time = 261 ms. \\ theinternalintegral matters most

The library syntax is

,
where an omitted **intnum**(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b,GEN tab, long prec)*tab* is coded as `NULL`

.

Numerical integration of *expr* on the compact interval [a,b] with
respect to X using Gauss-Legendre quadrature; `tab`

is either omitted
or precomputed with `intnumgaussinit`

. As a convenience, it can be an
integer n in which case we call
`intnumgaussinit`

(n) and use n-point quadrature.

? test(n, b = 1) = T=intnumgaussinit(n);\ intnumgauss(x=-b,b, 1/(1+x^2),T) - 2*atan(b); ? test(0) \\ default %1 = -9.490148553624725335 E-22 ? test(40) %2 = -6.186629001816965717 E-31 ? test(50) %3 = -1.1754943508222875080 E-38 ? test(50, 2) \\ double interval length %4 = -4.891779568527713636 E-21 ? test(90, 2) \\ n must almost be doubled as well! %5 = -9.403954806578300064 E-38

On the other hand, we recommend to split the integral and change variables rather than increasing n too much:

? f(x) = 1/(1+x^2); ? b = 100; ? intnumgauss(x=0,1, f(x)) + intnumgauss(x=1,1/b, f(1/x)*(-1/x^2)) - atan(b) %3 = -1.0579449157400587572 E-37

The library syntax is `GEN `

.**intnumgauss0**(GEN X, GEN b, GEN expr, GEN tab = NULL, long prec)

Initialize tables for n-point Gauss-Legendre integration of
a smooth function f on a compact
interval [a,b] at current `realprecision`

. If n is omitted, make a
default choice n ~ `realprecision`

, suitable for analytic
functions on [-1,1]. The error is bounded by

((b-a)^{2n+1} (n!)^4)/((2n+1)[(2n)!]^3) f^{(2n)} (ξ) ,
a < ξ < b

so, if the interval length increases, n should be increased as well.

? T = intnumgaussinit(); ? intnumgauss(t=-1,1,exp(t), T) - exp(1)+exp(-1) %1 = -5.877471754111437540 E-39 ? intnumgauss(t=-10,10,exp(t), T) - exp(10)+exp(-10) %2 = -8.358367809712546836 E-35 ? intnumgauss(t=-1,1,1/(1+t^2), T) - Pi/2 %3 = -9.490148553624725335 E-22 ? T = intnumgaussinit(50); ? intnumgauss(t=-1,1,1/(1+t^2), T) - Pi/2 %5 = -1.1754943508222875080 E-38 ? intnumgauss(t=-5,5,1/(1+t^2), T) - 2*atan(5) %6 = -1.2[...]E-8

On the other hand, we recommend to split the integral and change variables
rather than increasing n too much, see `intnumgauss`

.

The library syntax is `GEN `

.**intnumgaussinit**(long n, long prec)

Initialize tables for integration from
a to b, where a and b are coded as in `intnum`

. Only the
compactness, the possible existence of singularities, the speed of decrease
or the oscillations at infinity are taken into account, and not the values.
For instance intnuminit(-1,1) is equivalent to intnuminit(0,Pi),
and intnuminit([0,-1/2],oo) is equivalent to
intnuminit([-1,-1/2], -oo); on the other hand, the order matters
and
intnuminit([0,-1/2], [1,-1/3]) is *not* equivalent to
intnuminit([0,-1/3], [1,-1/2]) !

If m is present, it must be non-negative and we multiply the default number of sampling points by 2^m (increasing the running time by a similar factor).

The result is technical and liable to change in the future, but we document
it here for completeness. Let x = φ(t), t ∈ ]- oo , oo [ be an
internally chosen change of variable, achieving double exponential decrease of
the integrand at infinity. The integrator `intnum`

will compute
h ∑_{|n| < N} φ'(nh) F(φ(nh))
for some integration step h and truncation parameter N.
In basic use, let

[h, x0, w0, xp, wp, xm, wm] = intnuminit(a,b);

***** h is the integration step

***** x_{0} = φ(0) and w_{0} = φ'(0),

***** *xp* contains the φ(nh), 0 < n < N,

***** *xm* contains the φ(nh), 0 < -n < N, or is empty.

***** *wp* contains the φ'(nh), 0 < n < N,

***** *wm* contains the φ'(nh), 0 < -n < N, or is empty.

The arrays *xm* and *wm* are left empty when φ is an odd
function. In complicated situations when non-default behavior is specified at
end points, `intnuminit`

may return up to 3 such arrays, corresponding
to a splitting of up to 3 integrals of basic type.

If the functions to be integrated later are of the form F = f(t) k(t,z)
for some kernel k (e.g. Fourier, Laplace, Mellin,...), it is
useful to also precompute the values of f(φ(nh)), which is accomplished
by `intfuncinit`

. The hard part is to determine the behavior
of F at endpoints, depending on z.

The library syntax is `GEN `

.**intnuminit**(GEN a, GEN b, long m, long prec)

Numerical integration of *expr* (smooth in ]a,b[), with respect to
X. Suitable for low accuracy; if *expr* is very regular (e.g. analytic
in a large region) and high accuracy is desired, try `intnum`

first.

Set *flag* = 0 (or omit it altogether) when a and b are not too large, the
function is smooth, and can be evaluated exactly everywhere on the interval
[a,b].

If *flag* = 1, uses a general driver routine for doing numerical integration,
making no particular assumption (slow).

*flag* = 2 is tailored for being used when a or b are infinite using the
change of variable t = 1/X. One *must* have ab > 0, and in fact if
for example b = + oo , then it is preferable to have a as large as
possible, at least a ≥ 1.

If *flag* = 3, the function is allowed to be undefined
at a (but right continuous) or b (left continuous),
for example the function sin(x)/x between x = 0 and 1.

The user should not require too much accuracy: `realprecision`

about
30 decimal digits (`realbitprecision`

about 100 bits) is OK,
but not much more. In addition, analytical cleanup of the integral must have
been done: there must be no singularities in the interval or at the
boundaries. In practice this can be accomplished with a change of
variable. Furthermore, for improper integrals, where one or both of the
limits of integration are plus or minus infinity, the function must decrease
sufficiently rapidly at infinity, which can often be accomplished through
integration by parts. Finally, the function to be integrated should not be
very small (compared to the current precision) on the entire interval. This
can of course be accomplished by just multiplying by an appropriate constant.

Note that infinity can be represented with essentially no loss of
accuracy by an appropriate huge number. However beware of real underflow
when dealing with rapidly decreasing functions. For example, in order to
compute the ∫_{0}^ oo e^{-x^2}dx to 28 decimal digits, then one can
set infinity equal to 10 for example, and certainly not to `1e1000`

.

The library syntax is

, where **intnumromb_bitprec**(void *E, GEN (*eval)(void*,GEN), GEN a, GEN b, long flag, long bitprec)`eval`

(x, E) returns the value of the
function at x. You may store any additional information required by
`eval`

in E, or set it to `NULL`

. The historical variant
`intnumromb`

, where `prec`

is expressed in words, not bits, is
obsolete and should no longer be used.

Expand f as a Laurent series around x = 0 to order M. This function computes f(x + O(x^n)) until n is large enough: it must be possible to evaluate f on a power series with 0 constant term.

? laurentseries(t->sin(t)/(1-cos(t)), 5) %1 = 2*x^-1 - 1/6*x - 1/360*x^3 - 1/15120*x^5 + O(x^6) ? laurentseries(log) *** at top-level: laurentseries(log) *** ^ — — — — — — *** in function laurentseries: log *** ^ — *** log: domain error in log: series valuation != 0

Note that individual Laurent coefficients of order ≤ M
can be retrieved from s = `laurentseries`

(f,M) via `polcoeff(s,i)`

for any i ≤ M. The series s may occasionally be more precise that
the required O(x^{M+1}).

With respect to successive calls to `derivnum`

,
`laurentseries`

is both faster and more precise:

? laurentseries(t->log(3+t),1) %1 = 1.0986122886681096913952452369225257047 + 1/3*x - 1/18*x^2 + O(x^3) ? derivnum(t=0,log(3+t),1) %2 = 0.33333333333333333333333333333333333333 ? derivnum(t=0,log(3+t),2) %3 = -0.11111111111111111111111111111111111111 ? f = x->sin(exp(x)); ? polcoeff(laurentseries(x->f(x+2), 1), 1) %5 = 3.3129294231043339804683687620360224365 ? exp(2) * cos(exp(2)); %6 = 3.3129294231043339804683687620360224365 ? derivnum(x = 2, f(x)) %7 = 3.3129294231043339804683687620360224364 \\ 1 ulp off ? default(realprecision,115); ? for(i=1,10^4, laurentseries(x->f(x+2),1)) time = 279 ms. ? for(i=1,10^4, derivnum(x=2,f(x))) \\ ... and slower time = 1,134 ms.

The library syntax is

.**laurentseries**(void *E, GEN (*f)(void*,GEN,long), long M, long v, long prec)

Lagrange-Zagier numerical extrapolation of *expr*, corresponding to
a sequence u_{n}, either given by a closure `n- > u(n)`

. I.e., assuming
that u_{n} tends to a finite limit ℓ, try to determine ℓ.

The routine assume that u_{n} has an asymptotic expansion in n^{-α} :
u_{n} = ℓ + ∑_{i ≥ 1} a_{i} n^{-iα}
for some a_{i}. It is purely numerical and heuristic, thus may or may not
work on your examples. The expression will be evaluated for n = 1, 2,
..., N for an N = O(B) at a bit accuracy bounded by 1.612 B.

? limitnum(n -> n*sin(1/n)) %1 = 1.0000000000000000000000000000000000000 ? limitnum(n -> (1+1/n)^n) - exp(1) %2 = 0.E-37 ? limitnum(n -> 2^(4*n+1)*(n!)^4 / (2*n)! /(2*n+1)! ) - Pi %3 = 0.E -37

It is not mandatory to specify α when the u_{n} have an asymptotic
expansion in n^{-1}. However, if the series in n^{-1} is lacunary,
specifying α allows faster computation:

? \p1000 ? limitnum(n->(1+1/n^2)^(n^2)) - exp(1) time = 1min, 44,681 ms. %4 = 0.E-1001 ? limitnum(n->(1+1/n^2)^(n^2), 2) - exp(1) time = 27,271 ms. %5 = 0.E-1001 \\ still perfect, 4 times faster

When u_{n} has an asymptotic expansion in n^{-α} with α not an
integer, leaving α unspecified will bring an inexact limit. Giving a
satisfying optional argument improves precision; the program runs faster when
the optional argument gives non lacunary series.

? \p50 ? limitnum(n->(1+1/n^(7/2))^(n^(7/2))) - exp(1) time = 982 ms. %6 = 4.13[...] E-12 ? limitnum(n->(1+1/n^(7/2))^(n^(7/2)), 1/2) - exp(1) time = 16,745 ms. %7 = 0.E-57 ? limitnum(n->(1+1/n^(7/2))^(n^(7/2)), 7/2) - exp(1) time = 105 ms. %8 = 0.E-57

Alternatively, u_{n} may be given by a closure N`: — >`

[u_{1},..., u_{N}]
which can often be programmed in a more efficient way, for instance
when u_{n+1} is a simple function of the preceding terms:

? \p2000 ? limitnum(n -> 2^(4*n+1)*(n!)^4 / (2*n)! /(2*n+1)! ) - Pi time = 1,755 ms. %9 = 0.E-2003 ? vu(N) = \\ exploit hypergeometric property { my(v = vector(N)); v[1] = 8./3;\ for (n=2, N, my(q = 4*n^2); v[n] = v[n-1]*q/(q-1));\ return(v); } ? limitnum(vu) - Pi \\ much faster time = 106 ms. %11 = 0.E-2003

All sums and recursions can be handled in the same way.
In the above it is essential that u_{n} be defined as a closure because
it must be evaluated at a higher precision than the one expected for the
limit. Make sure that the closure does not depend on a global variable which
would be computed at a priori fixed accuracy. For instance, precomputing
`v1 = 8.0/3`

first and using `v1`

in `vu`

above would be wrong
because the resulting vector of values will use the accuracy of `v1`

instead of the ambient accuracy at which `limitnum`

will call it.

Alternatively, and more clumsily, u_{n} may be given by a vector of values:
it must be long and precise enough for the extrapolation
to make sense. Let B be the current `realbitprecision`

, the vector
length must be at least 1.102 B and the values computed with bit accuracy
1.612 B.

? limitnum(vector(10,n,(1+1/n)^n)) *** ^ — — — — — — -- *** limitnum: non-existent component in limitnum: index < 43 \\ at this accuracy, we must have at least 43 values ? limitnum(vector(43,n,(1+1/n)^n)) - exp(1) %12 = 0.E-37 ? v = vector(43); ? s = 0; for(i=1,#v, s += 1/i; v[i]= s - log(i)); ? limitnum(v) - Euler %15 = -1.57[...] E-16 ? v = vector(43); \\ ~ 128 bit * 1.612 ? localbitprec(207);\ s = 0; for(i=1,#v, s += 1/i; v[i]= s - log(i)); ? limitnum(v) - Euler %18 = 0.E-38

Because of the above problems, the preferred format is thus a closure,
given either a single value or the vector of values [u_{1},...,u_{N}]. The
function distinguishes between the two formats by evaluating the closure
at N != 1 and 1 and checking whether it yields vectors of respective
length N and 1 or not.

**Warning.** The expression is evaluated for n = 1, 2,..., N
for an N = O(B) if the current bit accuracy is B. If it is not defined
for one of these values, translate or rescale accordingly:

? limitnum(n->log(1-1/n)) \\ can't evaluate at n = 1 ! *** at top-level: limitnum(n->log(1-1/n)) *** ^ — — — — — — — -- *** in function limitnum: log(1-1/n) *** ^ — — — - *** log: domain error in log: argument = 0 ? limitnum(n->-log(1-1/(2*n))) %19 = -6.11[...] E-58

We conclude with a complicated example. Since the function is heuristic,
it is advisable to check whether it produces the same limit for
u_{n}, u_{2n},...u_{km} for a suitable small multiplier k.
The following function implements the recursion for the Motzkin numbers
M_{n} which count the number of ways to draw non intersecting chords between
n points on a circle:
M_{n} = M_{n-1} + ∑_{i < n-1} M_{i} M_{n-2-i}
= ((n+1)M_{n-1}+(3n-3)M_{n-2}) / (n+2).
It is known that M_{n} ~ (3^{n+1})/(sqrt{12π n^3}).

\\ [M_{k}, M_{k*2}, ..., M_{k*N}] / (3^n / n^(3/2)) vM(N, k = 1) = { my(q = k*N, V); if (q == 1, return ([1/3])); V = vector(q); V[1] = V[2] = 1; for(n = 2, q - 1, V[n+1] = ((2*n + 1)*V[n] + 3*(n - 1)*V[n-1]) / (n + 2)); f = (n -> 3^n / n^(3/2)); return (vector(N, n, V[n*k] / f(n*k))); } ? limitnum(vM) - 3/sqrt(12*Pi) \\ complete junk %1 = 35540390.753542730306762369615276452646 ? limitnum(N->vM(N,5)) - 3/sqrt(12*Pi) \\ M_{5n}: better %2 = 4.130710262178469860 E-25 ? limitnum(N->vM(N,10)) - 3/sqrt(12*Pi) \\ M_{10n}: perfect %3 = 0.E-38 ? \p2000 ? limitnum(N->vM(N,10)) - 3/sqrt(12*Pi) \\ also at high accuracy time = 409 ms. %4 = 1.1048895470044788191 E-2004

In difficult cases such as the above a multiplier of 5 to 10 is usually sufficient. The above example is typical: a good multiplier usually remains sufficient when the requested precision increases!

The library syntax is

, where **limitnum**(void *E, GEN (*u)(void *,GEN,long), GEN alpha, long prec)`u(E, n, prec)`

must return u(n) in precision `prec`

.
Also available is
`GEN `

, where u
must be a vector of sufficient length as above.**limitnum0**(GEN u, GEN alpha, long prec)

Product of expression
*expr*, initialized at x, the formal parameter X going from a to
b. As for `sum`

, the main purpose of the initialization parameter x
is to force the type of the operations being performed. For example if it is
set equal to the integer 1, operations will start being done exactly. If it
is set equal to the real 1., they will be done using real numbers having
the default precision. If it is set equal to the power series 1+O(X^k) for
a certain k, they will be done using power series of precision at most k.
These are the three most common initializations.

As an extreme example, compare

? prod(i=1, 100, 1 - X^i); \\ this has degree 5050 !! time = 128 ms. ? prod(i=1, 100, 1 - X^i, 1 + O(X^101)) time = 8 ms. %2 = 1 - X - X^2 + X^5 + X^7 - X^12 - X^15 + X^22 + X^26 - X^35 - X^40 + \ X^51 + X^57 - X^70 - X^77 + X^92 + X^100 + O(X^101)

Of course, in this specific case, it is faster to use `eta`

,
which is computed using Euler's formula.

? prod(i=1, 1000, 1 - X^i, 1 + O(X^1001)); time = 589 ms. ? \ps1000 seriesprecision = 1000 significant terms ? eta(X) - % time = 8ms. %4 = O(X^1001)

The library syntax is

.**produit**(GEN a, GEN b, char *expr, GEN x)

Product of expression *expr*, initialized at `1.0`

(i.e. to a floating point number equal to 1 to the
current `realprecision`

), the formal parameter p ranging over the prime
numbers between a and b.

? prodeuler(p = 2, 10^4, 1 - p^-2) %1 = 0.60793306911405513018380499671124428015 ? P = 1; forprime(p = 2, 10^4, P *= (1 - p^-2)) ? exponent(numerator(P)) %3 = 22953

The function returns a floating point number because,
as the second expression shows, such products are usually intractably
large rational numbers when computed symbolically.
If the expression is a rational funtction, `prodeulerrat`

computes the
product over all primes:

? prodeulerrat(1 - p^-2) %4 = 0.60792710185402662866327677925836583343 ? 6/Pi^2 %3 = 0.60792710185402662866327677925836583343

The library syntax is

.**prodeuler**(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b, long prec)

∏_{p ≥ a, p prime}F(p^s), where F is a rational function.

? prodeulerrat(1+1/q^3,1) %1 = 1.1815649490102569125693997341604542605 ? zeta(3)/zeta(6) %2 = 1.1815649490102569125693997341604542606

The library syntax is `GEN `

.**prodeulerrat**(GEN F, GEN s = NULL, long a, long prec)

infinite product of
expression *expr*, the formal parameter X starting at a. The evaluation
stops when the relative error of the expression minus 1 is less than the
default precision. In particular, non-convergent products result in infinite
loops. The expressions must always evaluate to an element of ℂ.

If *flag* = 1, do the product of the (1+*expr*) instead.

The library syntax is

(**prodinf**(void *E, GEN (*eval)(void*,GEN), GEN a, long prec)*flag* = 0), or `prodinf1`

with the same arguments (*flag* = 1).

∏_{n ≥ a}F(n), where F-1 is a rational function of degree less
than or equal to -2.

? prodnumrat(1+1/x^2,1) %1 = 3.6760779103749777206956974920282606665

The library syntax is `GEN `

.**prodnumrat**(GEN F, long a, long prec)

Find a real root of expression
*expr* between a and b, under the condition
*expr*(X = a) * *expr*(X = b) ≤ 0. (You will get an error message
`roots must be bracketed in solve`

if this does not hold.)
This routine uses Brent's method and can fail miserably if *expr* is
not defined in the whole of [a,b] (try `solve(x = 1, 2, tan(x))`

).

The library syntax is

.**zbrent**(void *E,GEN (*eval)(void*,GEN),GEN a,GEN b,long prec)

Find zeros of a continuous function in the real interval [a,b] by naive
interval splitting. This function is heuristic and may or may not find the
intended zeros. Binary digits of *flag* mean

***** 1: return as soon as one zero is found, otherwise return all
zeros found;

***** 2: refine the splitting until at least one zero is found
(may loop indefinitely if there are no zeros);

***** 4: do a multiplicative search (we must have a > 0 and *step* >
1), otherwise an additive search; *step* is the multiplicative or
additive step.

***** 8: refine the splitting until at least one zero is very close to an
integer.

? solvestep(X=0,10,1,sin(X^2),1) %1 = 1.7724538509055160272981674833411451828 ? solvestep(X=1,12,2,besselj(4,X),4) %2 = [7.588342434..., 11.064709488...]

The library syntax is

.**solvestep**(void *E, GEN (*eval)(void*,GEN), GEN a,GEN b, GEN step,long flag,long prec)

Sum of expression *expr*,
initialized at x, the formal parameter going from a to b. As for
`prod`

, the initialization parameter x may be given to force the type
of the operations being performed.

As an extreme example, compare

? sum(i=1, 10^4, 1/i); \\ rational number: denominator has 4345 digits. time = 236 ms. ? sum(i=1, 5000, 1/i, 0.) time = 8 ms. %2 = 9.787606036044382264178477904

Numerical summation of the series *expr*, which should be an
alternating series (-1)^k a_{k}, the formal variable X starting at
a. Use an algorithm of Cohen, Villegas and Zagier (*Experiment. Math.*
**9** (2000), no. 1, 3--12).

If *flag* = 0, assuming that the a_{k} are the moments of a positive
measure on [0,1], the relative error is O(3+sqrt8)^{-n} after using
a_{k} for k ≤ n. If `realprecision`

is p, we thus set
n = log(10)p/log(3+sqrt8) ~ 1.3 p; besides the time needed to
compute the a_{k}, k ≤ n, the algorithm overhead is negligible: time
O(p^2) and space O(p).

If *flag* = 1, use a variant with more complicated polynomials, see
`polzagier`

. If the a_{k} are the moments of w(x)dx where w
(or only xw(x^2)) is a smooth function extending analytically to the whole
complex plane, convergence is in O(14.4^{-n}). If xw(x^2) extends
analytically to a smaller region, we still have exponential convergence,
with worse constants. Usually faster when the computation of a_{k} is
expensive. If `realprecision`

is p, we thus set
n = log(10)p/log(14.4) ~ 0.86 p; besides the time needed to
compute the a_{k}, k ≤ n, the algorithm overhead is *not*
negligible: time O(p^3) and space O(p^2). Thus, even if the analytic
conditions for rigorous use are met, this variant is only worthwile if the
a_{k} are hard to compute, at least O(p^2) individually on average:
otherwise we gain a small constant factor (1.5, say) in the number of
needed a_{k} at the expense of a large overhead.

The conditions for rigorous use are hard to check but the routine is best used heuristically: even divergent alternating series can sometimes be summed by this method, as well as series which are not exactly alternating (see for example Section se:user_defined). It should be used to try and guess the value of an infinite sum. (However, see the example at the end of Section se:userfundef.)

If the series already converges geometrically,
`suminf`

is often a better choice:

```
? \p28
? sumalt(i = 1, -(-1)^i / i) - log(2)
time = 0 ms.
%1 = -2.524354897 E-29
? suminf(i = 1, -(-1)^i / i) \\ Had to hit
````C-C`

*** at top-level: suminf(i=1,-(-1)^i/i)
*** ^ — —
*** suminf: user interrupt after 10min, 20,100 ms.
? \p1000
? sumalt(i = 1, -(-1)^i / i) - log(2)
time = 90 ms.
%2 = 4.459597722 E-1002
? sumalt(i = 0, (-1)^i / i!) - exp(-1)
time = 670 ms.
%3 = -4.03698781490633483156497361352190615794353338591897830587 E-944
? suminf(i = 0, (-1)^i / i!) - exp(-1)
time = 110 ms.
%4 = -8.39147638 E-1000 \\ faster and more accurate

The library syntax is

. Also
available is **sumalt**(void *E, GEN (*eval)(void*,GEN),GEN a,long prec)`sumalt2`

with the same arguments (*flag* = 1).

Sum of expression *expr* over the positive divisors of n.
This function is a trivial wrapper essentially equivalent to

D = divisors(n); sum (i = 1, #D, my(X = D[i]); eval(expr))

If *expr* is a multiplicative function, use `sumdivmult`

.

Sum of *multiplicative* expression *expr* over the positive
divisors d of n. Assume that *expr* evaluates to f(d)
where f is multiplicative: f(1) = 1 and f(ab) = f(a)f(b) for coprime
a and b.
The library syntax is **sumdivmultexpr**(void *E, GEN (*eval)(void*,GEN), GEN d)

∑_{p ≥ a, p prime}F(p^s), where F is a rational function.

? sumeulerrat(1/p^2) %1 = 0.45224742004106549850654336483224793417 ? sumeulerrat(1/p, 2) %2 = 0.45224742004106549850654336483224793417

The library syntax is `GEN `

.**sumeulerrat**(GEN F, GEN s = NULL, long a, long prec)

Naive summation of expression *expr*, the formal parameter X
going from a to infinity. The evaluation stops when the relative error of
the expression is less than the default bit precision for 3 consecutive
evaluations. The expressions must evaluate to a complex number.

If the expression tends slowly to 0, like n^{-a} for some a > 1,
make sure b = `realbitprecision`

is low: indeed, the algorithm will
require O(2^{b/a}) function evaluations and we expect only about b(1-1/a)
correct bits in the answer. If the series is alternating, we can expect b
correct bits but the `sumalt`

function should be used instead since its
complexity is polynomial in b, instead of exponential. More generally,
`sumpos`

should be used if the terms have a constant sign and
`sumnum`

if the function is C^ oo .

? \pb25 realbitprecision = 25 significant bits (7 decimal digits displayed) ? exponent(suminf(i = 1, (-1)^i / i) + log(2)) time = 2min, 2,602 ms. %1 = -29 ? \pb45 realbitprecision = 45 significant bits (13 decimal digits displayed) ? exponent(suminf(i = 1, 1 / i^2) - zeta(2)) time = 2,186 ms. %2 = -23 \\ alternatives are much faster ? \pb 10000 realbitprecision = 10000 significant bits (3010 decimal digits displayed) ? exponent(sumalt(i = 1, (-1)^i / i) + log(2)) time = 25 ms. %3 = -10043 ? \pb 4000 realbitprecision = 4000 significant bits (1204 decimal digits displayed))) ? exponent(sumpos(i = 1, 1 / i^2) - zeta(2)) time = 22,593 ms. %4 = -4030 ? exponent(sumnum(i = 1, 1 / i^2) - zeta(2)) time = 7,032 ms. %5 = -4031 \\ but suminf is perfect for geometrically converging series ? exponent(suminf(i = 1, 2^-i) - 1) time = 25 ms. %6 = -4003

The library syntax is

.
The historical variant **suminf_bitprec**(void *E, GEN (*eval)(void*,GEN), GEN a, long prec)`GEN `

, where **suminf**(..., long prec)`prec`

is
expressed in words, not bits, is obsolete and should no longer be used.

Numerical summation of f(n) at high accuracy using Euler-MacLaurin,
the variable n taking values from a to + oo , where f is assumed to
have positive values and is a C^ oo function; `a`

must be an integer
and `tab`

, if given, is the output of `sumnuminit`

. The latter
precomputes abscissas and weights, speeding up the computation; it also allows
to specify the behavior at infinity via `sumnuminit([+oo, asymp])`

.

? \p500 ? z3 = zeta(3); ? sumpos(n = 1, n^-3) - z3 time = 2,332 ms. %2 = 2.438468843 E-501 ? sumnum(n = 1, n^-3) - z3 \\ here slower than sumpos time = 2,752 ms. %3 = 0.E-500

**Complexity.**
The function f will be evaluated at O(D log D) real arguments,
where D ~ `realprecision`

.log(10). The routine is geared
towards slowly decreasing functions: if f decreases exponentially fast,
then one of `suminf`

or `sumpos`

should be preferred.
If f satisfies the stronger hypotheses required for Monien summation,
i.e. if f(1/z) is holomorphic in a complex neighbourhood of [0,1],
then `sumnummonien`

will be faster since it only requires O(D/log D)
evaluations:

? sumnummonien(n = 1, 1/n^3) - z3 time = 1,985 ms. %3 = 0.E-500

The `tab`

argument precomputes technical data
not depending on the expression being summed and valid for a given accuracy,
speeding up immensely later calls:

? tab = sumnuminit(); time = 2,709 ms. ? sumnum(n = 1, 1/n^3, tab) - z3 \\ now much faster than sumpos time = 40 ms. %5 = 0.E-500 ? tabmon = sumnummonieninit(); \\ Monien summation allows precomputations too time = 1,781 ms. ? sumnummonien(n = 1, 1/n^3, tabmon) - z3 time = 2 ms. %7 = 0.E-500

The speedup due to precomputations becomes less impressive when the function f is expensive to evaluate, though:

? sumnum(n = 1, lngamma(1+1/n)/n, tab); time = 14,180 ms. ? sumnummonien(n = 1, lngamma(1+1/n)/n, tabmon); \\ fewer evaluations time = 717 ms.

**Behaviour at infinity.**
By default, `sumnum`

assumes that *expr* decreases slowly at infinity,
but at least like O(n^{-2}). If the function decreases like n^{α}
for some -2 < α < -1, then it must be indicated via

tab = sumnuminit([+oo, alpha]); /* alpha < 0 slow decrease */

otherwise loss of accuracy is expected. If the functions decreases quickly, like exp(-α n) for some α > 0, then it must be indicated via

tab = sumnuminit([+oo, alpha]); /* alpha > 0 exponential decrease */

otherwise exponent overflow will occur.

? sumnum(n=1,2^-n) *** at top-level: sumnum(n=1,2^-n) *** ^ — - *** _^_: overflow in expo(). ? tab = sumnuminit([+oo,log(2)]); sumnum(n=1,2^-n, tab) %1 = 1.000[...]

As a shortcut, one can also input

sumnum(n = [a, asymp], f)

instead of

tab = sumnuminit(asymp); sumnum(n = a, f, tab)

**Further examples.**

? \p200 ? sumnum(n = 1, n^(-2)) - zeta(2) \\ accurate, fast time = 200 ms. %1 = -2.376364457868949779 E-212 ? sumpos(n = 1, n^(-2)) - zeta(2) \\ even faster time = 96 ms. %2 = 0.E-211 ? sumpos(n=1,n^(-4/3)) - zeta(4/3) \\ now much slower time = 13,045 ms. %3 = -9.980730723049589073 E-210 ? sumnum(n=1,n^(-4/3)) - zeta(4/3) \\ fast but inaccurate time = 365 ms. %4 = -9.85[...]E-85 ? sumnum(n=[1,-4/3],n^(-4/3)) - zeta(4/3) \\ with decrease rate, now accurate time = 416 ms. %5 = -4.134874156691972616 E-210 ? tab = sumnuminit([+oo,-4/3]); time = 196 ms. ? sumnum(n=1, n^(-4/3), tab) - zeta(4/3) \\ faster with precomputations time = 216 ms. %5 = -4.134874156691972616 E-210 ? sumnum(n=1,-log(n)*n^(-4/3), tab) - zeta'(4/3) time = 321 ms. %7 = 7.224147951921607329 E-210

Note that in the case of slow decrease (α < 0), the exact
decrease rate must be indicated, while in the case of exponential decrease,
a rough value will do. In fact, for exponentially decreasing functions,
`sumnum`

is given for completeness and comparison purposes only: one
of `suminf`

or `sumpos`

should always be preferred.

? sumnum(n=[1, 1], 2^-n) \\ pretend we decrease as exp(-n) time = 240 ms. %8 = 1.000[...] \\ perfect ? sumpos(n=1, 2^-n) %9 = 1.000[...] \\ perfect and instantaneous

**Beware cancellation.** The function f(n) is evaluated for huge
values of n, so beware of cancellation in the evaluation:

? f(n) = 2 - 1/n - 2*n*log(1+1/n); \\ result is O(1/n^2) ? z = -2 + log(2*Pi) - Euler; ? sumnummonien(n=1, f(n)) - z time = 149 ms. %12 = 0.E-212 \\ perfect ? sumnum(n=1, f(n)) - z time = 116 ms. %13 = -948.216[...] \\ junk

As `sumnum(n = 1, print(n))`

shows, we evaluate f(n) for
n > 1e233 and our implementation of f suffers from massive cancellation
since we are summing two terms of the order of O(1) for a result in
O(1/n^2). You can either rewrite your sum so that individual terms are
evaluated without cancellation or locally replace f(n) by an accurate
asymptotic expansion:

? F = truncate( f(1/x + O(x^30)) ); ? sumnum(n=1, if(n > 1e7, subst(F,x,1/n), f(n))) - z %15 = 1.1 E-212 \\ now perfect

The library syntax is

where an omitted **sumnum**((void *E, GEN (*eval)(void*, GEN), GEN a, GEN tab, long prec))*tab* is coded as `NULL`

.

Numerical summation of f(n) at high accuracy using Abel-Plana,
the variable n taking values from a to + oo , where f is
holomorphic in the right half-place Re(z) > a; `a`

must be an integer
and `tab`

, if given, is the output of `sumnumapinit`

. The latter
precomputes abscissas and weights, speeding up the computation; it also allows
to specify the behavior at infinity via `sumnumapinit([+oo, asymp])`

.

? \p500 ? z3 = zeta(3); ? sumpos(n = 1, n^-3) - z3 time = 2,332 ms. %2 = 2.438468843 E-501 ? sumnumap(n = 1, n^-3) - z3 \\ here slower than sumpos time = 2,565 ms. %3 = 0.E-500

**Complexity.**
The function f will be evaluated at O(D log D) real arguments
and O(D) complex arguments,
where D ~ `realprecision`

.log(10). The routine is geared
towards slowly decreasing functions: if f decreases exponentially fast,
then one of `suminf`

or `sumpos`

should be preferred.
The default algorithm `sumnum`

is usually a little *slower*
than `sumnumap`

but its initialization function `sumnuminit`

becomes much faster as `realprecision`

increases.

If f satisfies the stronger hypotheses required for Monien summation,
i.e. if f(1/z) is holomorphic in a complex neighbourhood of [0,1],
then `sumnummonien`

will be faster since it only requires O(D/log D)
evaluations:

? sumnummonien(n = 1, 1/n^3) - z3 time = 1,128 ms. %3 = 0.E-500

The `tab`

argument precomputes technical data
not depending on the expression being summed and valid for a given accuracy,
speeding up immensely later calls:

? tab = sumnumapinit(); time = 2,567 ms. ? sumnumap(n = 1, 1/n^3, tab) - z3 \\ now much faster than sumpos time = 39 ms. %5 = 0.E-500 ? tabmon = sumnummonieninit(); \\ Monien summation allows precomputations too time = 1,125 ms. ? sumnummonien(n = 1, 1/n^3, tabmon) - z3 time = 2 ms. %7 = 0.E-500

The speedup due to precomputations becomes less impressive when the function f is expensive to evaluate, though:

? sumnumap(n = 1, lngamma(1+1/n)/n, tab); time = 10,762 ms. ? sumnummonien(n = 1, lngamma(1+1/n)/n, tabmon); \\ fewer evaluations time = 205 ms.

**Behaviour at infinity.**
By default, `sumnumap`

assumes that *expr* decreases slowly at
infinity, but at least like O(n^{-2}). If the function decreases
like n^{α} for some -2 < α < -1, then it must be indicated via

tab = sumnumapinit([+oo, alpha]); /* alpha < 0 slow decrease */

otherwise loss of accuracy is expected. If the functions decreases quickly, like exp(-α n) for some α > 0, then it must be indicated via

tab = sumnumapinit([+oo, alpha]); /* alpha > 0 exponential decrease */

otherwise exponent overflow will occur.

? sumnumap(n=1,2^-n) *** at top-level: sumnumap(n=1,2^-n) *** ^ — - *** _^_: overflow in expo(). ? tab = sumnumapinit([+oo,log(2)]); sumnumap(n=1,2^-n, tab) %1 = 1.000[...]

As a shortcut, one can also input

sumnumap(n = [a, asymp], f)

instead of

tab = sumnumapinit(asymp); sumnumap(n = a, f, tab)

**Further examples.**

? \p200 ? sumnumap(n = 1, n^(-2)) - zeta(2) \\ accurate, fast time = 169 ms. %1 = -4.752728915737899559 E-212 ? sumpos(n = 1, n^(-2)) - zeta(2) \\ even faster time = 79 ms. %2 = 0.E-211 ? sumpos(n=1,n^(-4/3)) - zeta(4/3) \\ now much slower time = 10,518 ms. %3 = -9.980730723049589073 E-210 ? sumnumap(n=1,n^(-4/3)) - zeta(4/3) \\ fast but inaccurate time = 309 ms. %4 = -2.57[...]E-78 ? sumnumap(n=[1,-4/3],n^(-4/3)) - zeta(4/3) \\ decrease rate: now accurate time = 329 ms. %6 = -5.418110963941205497 E-210 ? tab = sumnumapinit([+oo,-4/3]); time = 160 ms. ? sumnumap(n=1, n^(-4/3), tab) - zeta(4/3) \\ faster with precomputations time = 175 ms. %5 = -5.418110963941205497 E-210 ? sumnumap(n=1,-log(n)*n^(-4/3), tab) - zeta'(4/3) time = 258 ms. %7 = 9.125239518216767153 E-210

Note that in the case of slow decrease (α < 0), the exact
decrease rate must be indicated, while in the case of exponential decrease,
a rough value will do. In fact, for exponentially decreasing functions,
`sumnumap`

is given for completeness and comparison purposes only: one
of `suminf`

or `sumpos`

should always be preferred.

? sumnumap(n=[1, 1], 2^-n) \\ pretend we decrease as exp(-n) time = 240 ms. %8 = 1.000[...] \\ perfect ? sumpos(n=1, 2^-n) %9 = 1.000[...] \\ perfect and instantaneous

The library syntax is

where an omitted **sumnumap**((void *E, GEN (*eval)(void*,GEN), GEN a, GEN tab, long prec))*tab* is coded as `NULL`

.

Initialize tables for Abel-Plana summation of a series ∑ f(n),
where f is holomorphic in a right half-plane.
If given, `asymp`

is of the form [`+oo`

, α],
as in `intnum`

and indicates the decrease rate at infinity of functions
to be summed. A positive
α > 0 encodes an exponential decrease of type exp(-α n) and
a negative -2 < α < -1 encodes a slow polynomial decrease of type
n^{α}.

? \p200 ? sumnumap(n=1, n^-2); time = 163 ms. ? tab = sumnumapinit(); time = 160 ms. ? sumnumap(n=1, n^-2, tab); \\ faster time = 7 ms. ? tab = sumnumapinit([+oo, log(2)]); \\ decrease like 2^-n time = 164 ms. ? sumnumap(n=1, 2^-n, tab) - 1 time = 36 ms. %5 = 3.0127431466707723218 E-282 ? tab = sumnumapinit([+oo, -4/3]); \\ decrease like n^(-4/3) time = 166 ms. ? sumnumap(n=1, n^(-4/3), tab); time = 181 ms.

The library syntax is `GEN `

.**sumnumapinit**(GEN asymp = NULL, long prec)

Initialize tables for Euler-MacLaurin delta summation of a series with
positive terms. If given, `asymp`

is of the form [`+oo`

, α],
as in `intnum`

and indicates the decrease rate at infinity of functions
to be summed. A positive
α > 0 encodes an exponential decrease of type exp(-α n) and
a negative -2 < α < -1 encodes a slow polynomial decrease of type
n^{α}.

? \p200 ? sumnum(n=1, n^-2); time = 200 ms. ? tab = sumnuminit(); time = 188 ms. ? sumnum(n=1, n^-2, tab); \\ faster time = 8 ms. ? tab = sumnuminit([+oo, log(2)]); \\ decrease like 2^-n time = 200 ms. ? sumnum(n=1, 2^-n, tab) time = 44 ms. ? tab = sumnuminit([+oo, -4/3]); \\ decrease like n^(-4/3) time = 200 ms. ? sumnum(n=1, n^(-4/3), tab); time = 221 ms.

The library syntax is `GEN `

.**sumnuminit**(GEN asymp = NULL, long prec)

Numerical summation of f(n) from n = a to + oo using Lagrange
summation; a must be an integer, and the optional argument `tab`

is
the output of `sumnumlagrangeinit`

. By default, the program assumes that
the Nth remainder has an asymptotic expansion in integral powers of 1/N.
If not, initialize `tab`

using `sumnumlagrangeinit(al)`

, where
the asymptotic expansion of the remainder is integral powers of 1/N^{al};
al can be equal to 1 (default), 1/2, 1/3, or 1/4, and also
equal to 2, but in this latter case it is the Nth remainder minus one
half of the last summand which has an asymptotic expansion in integral
powers of 1/N^2.

? \p1000 ? z3 = zeta(3); ? sumpos(n = 1, n^-3) - z3 time = 4,440 ms. %2 = -2.08[...] E-1001 ? sumnumlagrange(n = 1, n^-3) - z3 \\ much faster than sumpos time = 25 ms. %3 = 0.E-1001 ? tab = sumnumlagrangeinit(); time = 21 ms. ? sumnumlagrange(n = 1, n^-3, tab) - z3 time = 2 ms. /* even faster */ %5 = 0.E-1001 ? \p115 ? tab = sumnumlagrangeinit([1/3,1/3]); time = 316 ms. ? sumnumlagrange(n = 1, n^-(7/3), tab) - zeta(7/3) time = 24 ms. %7 = 0.E-115 ? sumnumlagrange(n = 1, n^(-2/3) - 3*(n^(1/3)-(n-1)^(1/3)), tab) - zeta(2/3) time = 32 ms. %8 = 1.0151767349262596893 E-115

**Complexity.**
The function f is evaluated at O(D) integer arguments,
where D ~ `realprecision`

.log(10).

The library syntax is

where an omitted **sumnumlagrange**((void *E, GEN (*eval)(void*, GEN), GEN a, GEN tab, long prec))*tab* is coded as `NULL`

.

Initialize tables for Lagrange summation of a series. By
default, assume that the remainder R(n) = ∑_{m ≥ n} f(m)
has an asymptotic expansion
R(n) = ∑_{m ≥ n} f(n) ~ ∑_{i ≥ 1} a_{i} / n^i
at infinity. The argument `asymp`

allows to specify different
expansions:

***** a real number β means
R(n) = n^{-β} ∑_{i ≥ 1} a_{i} / n^i

***** a `t_CLOSURE`

g means
R(n) = g(n) ∑_{i ≥ 1} a_{i} / n^i
(The preceding case corresponds to g(n) = n^{-β}.)

***** a pair [α,β] where β is as above and
α ∈ {2, 1, 1/2, 1/3, 1/4}. We let R_{2}(n) = R(n) - f(n)/2
and R_α(n) = R(n) for α != 2. Then
R_α(n) = g(n) ∑_{i ≥ 1} a_{i} / n^{iα}
Note that the initialization times increase considerable for the α
is this list (1/4 being the slowest).

The constant c1 is technical and computed by the program, but can be set by the user: the number of interpolation steps will be chosen close to c1.B, where B is the bit accuracy.

? \p2000 ? sumnumlagrange(n=1, n^-2); time = 173 ms. ? tab = sumnumlagrangeinit(); time = 172 ms. ? sumnumlagrange(n=1, n^-2, tab); time = 4 ms. ? \p115 ? sumnumlagrange(n=1, n^(-4/3)) - zeta(4/3); %1 = -0.1093[...] \\ junk: expansion in n^(1/3) time = 84 ms. ? tab = sumnumlagrangeinit([1/3,0]); \\ alpha = 1/3 time = 336 ms. ? sumnumlagrange(n=1, n^(-4/3), tab) - zeta(4/3) time = 84 ms. %3 = 1.0151767349262596893 E-115 \\ now OK ? tab = sumnumlagrangeinit(1/3); \\ alpha = 1, beta = 1/3: much faster time = 3ms ? sumnumlagrange(n=1, n^(-4/3), tab) - zeta(4/3) \\ ... but wrong %5 = -0.273825[...] \\ junk ! ? tab = sumnumlagrangeinit(-2/3); \\ alpha = 1, beta = -2/3 time = 3ms ? sumnumlagrange(n=1, n^(-4/3), tab) - zeta(4/3) %6 = 2.030353469852519379 E-115 \\ now OK

in The final example with ζ(4/3), the remainder
R_{1}(n) is of the form n^{-1/3} ∑_{i ≥ 0} a_{i} / n^i, i.e.
n^{2/3} ∑_{i ≥ 1} a_{i} / n^i. The explains the wrong result
for β = 1/3 and the correction with β = -2/3.

The library syntax is `GEN `

.**sumnumlagrangeinit**(GEN asymp = NULL, GEN c1 = NULL, long prec)

Numerical summation ∑_{n ≥ a} f(n) at high accuracy, the variable
n taking values from the integer a to + oo using Monien summation,
which assumes that f(1/z) has a complex analytic continuation in a (complex)
neighbourhood of the segment [0,1].

The function f is evaluated at O(D / log D) real arguments,
where D ~ `realprecision`

.log(10).
By default, assume that f(n) = O(n^{-2}) and has a non-zero asymptotic
expansion
f(n) = ∑_{i ≥ 2} a_{i} n^{-i}
at infinity. To handle more complicated behaviors and allow time-saving
precomputations (for a given `realprecision`

), see `sumnummonieninit`

.

The library syntax is `GEN `

.**sumnummonien0**(GEN n, GEN f, GEN tab = NULL, long prec)

Initialize tables for Monien summation of a series ∑_{n ≥ n0}
f(n) where f(1/z) has a complex analytic continuation in a (complex)
neighbourhood of the segment [0,1].

By default, assume that f(n) = O(n^{-2}) and has a non-zero asymptotic
expansion
f(n) = ∑_{i ≥ 2} a_{i} / n^i
at infinity. Note that the sum starts at i = 2! The argument `asymp`

allows to specify different expansions:

***** a real number β > 0 means
f(n) = ∑_{i ≥ 1} a_{i} / n^{i + β}
(Now the summation starts at 1.)

***** a vector [α,β] of reals, where we must have α > 0
and α + β > 1 to ensure convergence, means that
f(n) = ∑_{i ≥ 1} a_{i} / n^{α i + β}
Note that `asymp`

= [1, β] is equivalent to
`asymp`

= β.

? \p57 ? s = sumnum(n = 1, sin(1/sqrt(n)) / n); \\ reference point ? \p38 ? sumnummonien(n = 1, sin(1/sqrt(n)) / n) - s %2 = -0.001[...] \\ completely wrong ? t = sumnummonieninit(1/2); \\ f(n) = sum_{i}1 / n^(i+1/2) ? sumnummonien(n = 1, sin(1/sqrt(n)) / n, t) - s %3 = 0.E-37 \\ now correct

(As a matter of fact, in the above summation, the
result given by `sumnum`

at `\p38`

is slighly incorrect,
so we had to increase the accuracy to `\p57`

.)

The argument w is used to sum expressions of the form
∑_{n ≥ n0} f(n) w(n),
for varying f *as above*, and fixed weight function w, where we
further assume that the auxiliary sums
g_{w}(m) = ∑_{n ≥ n0} w(n) / n^{α m + β}
converge for all m ≥ 1. Note that for non-negative integers k,
and weight w(n) = (log n)^k, the function g_{w}(m) = ζ^{(k)}(α m +
β) has a simple expression; for general weights, g_{w} is
computed using `sumnum`

. The following variants are available

***** an integer k ≥ 0, to code w(n) = (log n)^k;

***** a `t_CLOSURE`

computing the values w(n), where we
assume that w(n) = O(n^ε) for all ε > 0;

***** a vector [w, `fast`

], where w is a closure as above
and `fast`

is a scalar;
we assume that w(n) = O(n^{fast+ε}); note that
`w`

= [w, 0] is equivalent to `w`

= w. Note that if
w decreases exponentially, `suminf`

should be used instead.

The subsequent calls to `sumnummonien`

*must* use the same value
of n_{0} as was used here.

? \p300 ? sumnummonien(n = 1, n^-2*log(n)) + zeta'(2) time = 328 ms. %1 = -1.323[...]E-6 \\ completely wrong, f does not satisfy hypotheses ! ? tab = sumnummonieninit(, 1); \\ codes w(n) = log(n) time = 3,993 ms. ? sumnummonien(n = 1, n^-2, tab) + zeta'(2) time = 41 ms. %3 = -5.562684646268003458 E-309 \\ now perfect ? tab = sumnummonieninit(, n->log(n)); \\ generic, slower time = 9,808 ms. ? sumnummonien(n = 1, n^-2, tab) + zeta'(2) time = 40 ms. %5 = -5.562684646268003458 E-309 \\ identical result

The library syntax is `GEN `

.**sumnummonieninit**(GEN asymp = NULL, GEN w = NULL, GEN n0 = NULL, long prec)

∑_{n ≥ a}F(n), where F is a rational function of degree less
than or equal to -2 and where poles of F at integers ≥ a are
omitted from the summation. The argument a must be a `t_INT`

or `-oo`

.

? sumnumrat(1/(x^2+1)^2,0) %1 = 1.3068369754229086939178621382829073480 ? sumnumrat(1/x^2, -oo) \\ value at x=0 is discarded %2 = 3.2898681336964528729448303332920503784 ? 2*zeta(2) %3 = 3.2898681336964528729448303332920503784

When deg F = -1, we define
∑_{- oo }^{ oo } F(n) := ∑_{n ≥ 0} (F(n) + F(-1-n)):

? sumnumrat(1/x, -oo) %4 = 0.E-38

The library syntax is `GEN `

.**sumnumrat**(GEN F, GEN a, long prec)

Numerical summation of the series *expr*, which must be a series of
terms having the same sign, the formal variable X starting at a. The
algorithm uses Van Wijngaarden's trick for converting such a series into
an alternating one, then `sumalt`

. For regular functions, the
function `sumnum`

is in general much faster once the initializations
have been made using `sumnuminit`

. Contrary to `sumnum`

,
`sumpos`

allows functions defined only at integers:

? sumnum(n = 0, 1/n!) *** at top-level: sumnum(n=1,1/n!) *** ^ — *** incorrect type in gtos [integer expected] (t_FRAC). ? sumpos(n = 0, 1/n!) - exp(1) %2 = -1.0862155548773347717 E-33

On the other hand, when the function accepts general real
numbers, it is usually advantageous to replace n by `n * 1.0`

in the
sumpos call in particular when rational functions are involved:

? \p500 ? sumpos(n = 0, n^7 / (n^9+n+1)); time = 6,108 ms. ? sumpos(n = 0, n *= 1.; n^7 / (n^9+n+1)); time = 2,788 ms. ? sumnumrat(n^7 / (n^9+n+1), 0); time = 4 ms.

In the last example, `sumnumrat`

is of course much
faster but it only applies to rational functions.

The routine is heuristic and assumes that *expr* is more or less a
decreasing function of X. In particular, the result will be completely
wrong if *expr* is 0 too often. We do not check either that all terms
have the same sign: as `sumalt`

, this function should be used to
try and guess the value of an infinite sum.

If *flag* = 1, use `sumalt`

(,1) instead of `sumalt`

(,0), see
Section se:sumalt. Requiring more stringent analytic properties for
rigorous use, but allowing to compute fewer series terms.

To reach accuracy 10^{-p}, both algorithms require O(p^2) space;
furthermore, assuming the terms decrease polynomially (in O(n^{-C})), both
need to compute O(p^2) terms. The `sumpos`

(,1) variant has a smaller
implied constant (roughly 1.5 times smaller). Since the `sumalt`

(,1)
overhead is now small compared to the time needed to compute series terms,
this last variant should be about 1.5 faster. On the other hand, the
achieved accuracy may be much worse: as for `sumalt`

, since
conditions for rigorous use are hard to check, the routine is best used
heuristically.

The library syntax is

. Also
available is **sumpos**(void *E, GEN (*eval)(void*,GEN),GEN a,long prec)`sumpos2`

with the same arguments (*flag* = 1).