There are a few points that I think are worth mentioning.
1. The Risch/ etc/ methods determine algebraic anti-derivatives.
It is a bit of a stretch sometimes to identify them with functions
with certain properties. It is typically possible to multiply or add
or otherwise muck around with these functions by, for example, use
of delta or step functions. Or adding strange constants.
The results of a subsequent differentiation need not be affected.
2. Either we are dealing with functions of a single variable (e.g. x)
or multiple variables / parameterized or multidimensional integral.
a. If multiple variables, the notion of a singularity becomes much
more complicated, and insisting on a continuous function much more
hazardous. Try to find useful theorems about what amounts to
functions of several complex variables. Not too many that I have found.
b. If a single variable, you are presumably being presented with a
function that is computable and continuous [yeah, well some version of
continuous for some version of integration] within the limits of
integration (yes, I know you might not know the limits. But you really
have a lot of nerve asking for the integral of a function you are not
willing to define adequately between the limits of interest.)
Continuing on this -- a continuous function of the kind being discussed
here can almost always be integrated by numerical methods. That is
given f(x), excellent methods exist to produce a program F(a,b)
which returns the integral of f from a to b (where a and b are
numerical constants).
Arbitrarily high precision methods are available, as are error
bounds. Chance of being fooled very low.
These methods avoid the problems encountered with symbolic methods which
include (i) the uselessness of an implicit proof that there is no
elementary expression for the antiderivative or (ii) there may be one
but we can't tell for sure because there is a bug in our complicated
program or (iii) because the procedure requires a sub-algorithm that is
not computable [zero equivalence] we can't say for sure about (i) or (ii).
3. You might argue (certainly I have) that symbolic results display
more information than (say) a table of numbers for various a,b. Yet
if the result is going to be run through a plotting program, not so
convincing. A partial symbolic result can be obtained by doing a
numerical-partial-fraction decomposition of some expressions --
any denominator that looks like a polynomial in one variable with
coefficients that can be resolved to floating-point numbers can be
factored into a product of linear factors, thereby requiring only
integration of <numerator stuff>/ (x-r)^n.
4. You might argue that you need the results for a high-dimension
multiple integration (calculation of Feynman diagrams was a major
selling point of integration in the Reduce system). Eh, then you
have multiple variables, but given limits, I think.
Now if you want to posit a problem that is to find an antiderivative
that has the fewest singularities, that may be useful. Another challenge
is to find an antiderivative that is, by some data-structure complexity
measure, "the simplest". This seems to be the objective of Rubi.
These problems are interesting in a computer-sciency computer algebra
systems algebraic-geometry context.
I'm not aware of any current clamor from actual or potential users of
computer algebra systems for solution of these problems. Are there
any Feynman diagram people still out there?
I'm not saying that Bronstein's work, or the programmers attempting to
solve the problems in this thread are "wrong", just that the context and
relevance to (say) scientific computation is easy to misconstrue.
Cheers.
RJF
>>> so something is wrong...