That works very well.
I missed the default value of 0 earlier
Thanks,
Ajo
On Wed, Aug 28, 2013 at 11:05 AM, Luc Maisonobe <Luc.Maisonobe@free.fr>wrote:
> Le 28/08/2013 19:59, Ajo Fod a écrit :
> > Its a=0 that bothers me. x > 0 in my case.
>
> Then everything should be OK with the current code, which reads:
>
> final double[] function = new double[1 + order];
> if (a == 0) {
> if (operand[operandOffset] == 0) {
> function[0] = 1;
> double infinity = Double.POSITIVE_INFINITY;
> for (int i = 1; i < function.length; ++i) {
> infinity = infinity;
> function[i] = infinity;
> }
> } else if (operand[operandOffset] < 0) {
> Arrays.fill(function, Double.NaN);
> }
> } else {
> function[0] = FastMath.pow(a, operand[operandOffset]);
> final double lnA = FastMath.log(a);
> for (int i = 1; i < function.length; ++i) {
> function[i] = lnA * function[i  1];
> }
> }
>
> So when a == 0 and operand[operandOffset] > 0, you don't enter in any of
> the branches above (you enter the top level if, but don't enter any of
> the second level if/else if). This means that you skip directly from the
> allocation of the array function to its use, and hence you have the
> default values of a newly allocated array, which is guaranteed to be 0.
>
> Does this fits your needs?
>
> Luc
>
> >
> > In the code I use, the DerivativeStructure evaluates to NaN for a=0
> when x
> >> 0 . I think we agree that in this condition the derivative should
> > evaluate to 0.
> >
> > Perhaps I wrote something to mislead you on this detail.
> >
> > Ajo
> >
> >
> > On Wed, Aug 28, 2013 at 10:36 AM, Luc Maisonobe <Luc.Maisonobe@free.fr
> >wrote:
> >
> >> Hi Ajo,
> >>
> >> Le 28/08/2013 16:56, Ajo Fod a écrit :
> >>> To define things precisely:
> >>> y = f(a,x) = a^x
> >>>
> >>> Can we agree that:
> >>> df(a,x)/dx > 0 when a>0 and x > 0 :[ NOTE: x > 0]
> >>
> >> Yes, of course, it is perfectly true.
> >>
> >>>
> >>> If this is acceptable, we get this very useful property that df
> (a,x)/dx
> >> is
> >>> defined and continuous for all a provided x>0 because we use the
> modulus
> >> of
> >>> a in the function definition.
> >>
> >> Yes, as long as we don't have x = 0, we remain in a smooth, indefinitely
> >> differentiable domain.
> >>
> >>> In optimization, with this patch at a=0, I
> >>> can set an optimizer to search the whole real line without worrying
> about
> >>> a=0 otherwise I've to look out for a=0 explicitly. It seems unnecessary
> >> to
> >>> add a constraint to make a>0. I already have a constraint for x >0.
> >>
> >> I don't understand what you mean here. If you already know that x > 0,
> >> then you don't have to worry about a=0 or a>0 since in this case both
> >> approaches lead to the same result.
> >>
> >> If you look at the graph for df(a,x)/dx for a few values of a, you will
> >> see that we have:
> >>
> >> lim a>0+ df(a,x)/dx = 0 for x > 0
> >> lim a>0+ df(a,x)/dx = infinity for x = 0
> >>
> >> and this despite df(a,x)/dx = ln(a) a^x is a continuous function,
> >> indefinitely differentiable. The limit of a continuous indefinitely
> >> differentiable function may be a non continuous function. It is a
> >> counterintuitive result, I agree, but thre are many other examples of
> >> such strange behaviour in mathematics (if I remember well, Fourier
> >> transforms of step function exhibit the same paroble, backward).
> >>
> >> If you have x>0, you are already on the safe side of the singularity, so
> >> this is were I lose your tracks and don't understand how the singular
> >> point x=0 bothers you.
> >>
> >> best regards,
> >> Luc
> >>
> >>>
> >>> Cheers,
> >>> Ajo.
> >>>
> >>>
> >>>
> >>> On Tue, Aug 27, 2013 at 1:49 PM, Luc Maisonobe <Luc.Maisonobe@free.fr
> >>> wrote:
> >>>
> >>>> Hi Ajo,
> >>>>
> >>>> Le 27/08/2013 16:44, Ajo Fod a écrit :
> >>>>> Thanks for the constant structure.
> >>>>>
> >>>>> No. The limit value when x>0+ is 1, not O.
> >>>>>
> >>>>> I agree with this. I was just going for the derivatives = 0.
> >>>>>
> >>>>>
> >>>>>> The nth derivative of a^x can be computed analytically as ln(a)^n
> a^x,
> >>>>>> so the initial slope at x=0 is simply ln(a), positive for a
> 1,
> zero
> >>>>>> for a = 1, negative for 0 < a < 1 with a limit at inifnity
when a
> >
> >>>> 0+.
> >>>>>>
> >>>>>
> >>>>> Lets think about this for a sec:
> >>>>> Derivative of a^x wrt x at x=2.0 for various values of a
> >>>>> Derivative@0.031250=0.003384
> >>>>> Derivative@0.015625=0.001015
> >>>>> Derivative@0.007813=0.000296
> >>>>> Derivative@0.003906=0.000085
> >>>>> Derivative@0.001953=0.000024
> >>>>> ... tends to 0
> >>>>
> >>>> yes, because 2.0 > 0.
> >>>>
> >>>>>
> >>>>> Derivative of a^x wrt x at x=0.5 for various values of a
> >>>>> Derivative@0.031250=0.612555
> >>>>> Derivative@0.007813=0.428759
> >>>>> Derivative@0.001953=0.275612
> >>>>> Derivative@0.000488=0.168418
> >>>>> Derivative@0.000122=0.099513
> >>>>> Derivative@0.000031=0.057407
> >>>>> Derivative@0.000008=0.032528
> >>>>> Derivative@0.000002=0.018176
> >>>>> ... tends to 0 when a>0
> >>>>
> >>>> yes because 0.5 > 0.
> >>>>
> >>>>>
> >>>>> The code I used for the print outs is:
> >>>>> static final double EPS = 0.0001d;
> >>>>>
> >>>>> public static void main(final String[] args) {
> >>>>> final double x = 0.5d;
> >>>>> int from = 5;
> >>>>> int to = 20;
> >>>>> System.out.println("Derivative of a^x wrt x at x=" + x);
> >>>>> for (int p = from; p < to; p+=2) {
> >>>>> double a = Math.pow(2d, p);
> >>>>> final double calc = (Math.pow(a, x + EPS)  Math.pow(a,
> >> x)) /
> >>>>> EPS;
> >>>>> System.out.format("Derivative@%f=%f \n", a, calc);
> >>>>> }
> >>>>> }
> >>>>>
> >>>>> As for the x=0 case:
> >>>>> 1^0 = 1
> >>>>> 0.5^0 = 1
> >>>>> 0.0001^0 = 1
> >>>>> 0^0 is technically undefined, but 1 is a good definition:
> >>>>> http://www.math.hmc.edu/funfacts/ffiles/10005.35.shtml
> >>>>
> >>>> Yes.
> >>>>
> >>>>> ... so, a good value for the differential of da^x/dx limit x>0
and
> >>>> a>0 =
> >>>>> 0
> >>>>
> >>>> I don't agree. What you wrote in the lines above is another way to say
> >>>> what I wrote in my previous message: the value at x=0 is always y=1,
> and
> >>>> the value for x > 0 tends to 0 as a>0+.
> >>>>
> >>>> So the function always starts at 1 and dives more and more steeply as
> a
> >>>> becomes smaller, and the derivative at 0 becomes more and more
> negative,
> >>>> up to infinity, *not* 0.
> >>>>
> >>>> The function is illbehaved and the fact the derivative is infinite
is
> >>>> consistent with this illbehaviour.
> >>>>
> >>>> The definition of the derivative is :
> >>>>
> >>>> f'(x) = lim (f(x+h)  f(x))/h when h > 0+
> >>>>
> >>>> when f(x) = 0^x and assuming 0^0 = 1 as you have agreed above, this
> >> gives:
> >>>>
> >>>> f'(0) = lim (0^(0+h)  0^0)/h = lim (0  1)/h = infinity
> >>>>
> >>>> which is exactly the same result as computing for a nonnull a and
> then
> >>>> reducing it: d(a^x)/dx = ln(a) a^x = ln(a) when x=0, diverges to
> >>>> infinity when a converges to 0.
> >>>>
> >>>>>
> >>>>>
> >>>>> As mentioned earlier, I think the cause for this is that loga
>
> >>>> infinity
> >>>>> slower than a^x > 0 as a>0 .
> >>>>
> >>>> But a^x does *not* converge to 0 for x = 0! a^0 is always 1
> (rigorously)
> >>>> regardless of the value of a as long as it is not 0, and then when we
> >>>> change a we can also consider the limit is 1 when a> 0. This
> convention
> >>>> is well accepted. This convention is implemented in the Java standard
> >>>> Math.pow function, and we followed this trend. This is the reason why
> >>>> the functions becomes more and more steep as a becomes smaller. At the
> >>>> end, it is a discontinuous function (and hence should not be
> >>>> differentiable, or it is differentiable only if we use extended real
> >>>> numbers with infinity added).
> >>>>
> >>>> This is the heart of the illbehaviour of 0^0. We want to compute it
> as
> >>>> a limit value for a^b when both parameters converge to 0, but we get
a
> >>>> different result if we first set a fixed and converge b to 0, and
> later
> >>>> reduce a down to zero (your approach), and when we do the opposite.
In
> >>>> one case we get 0, in the other case we get 1.
> >>>>
> >>>> Lets put it another way:
> >>>> If we consider the derivative f'(0) should be 0, then the value f(0)
> >>>> should also be considered equal to zero. This would mean as soon as
we
> >>>> get a tiny nonzero a (say the smallest number that can be represented
> >>>> as a double), then f(0) would jump from 0 to 1 instantly, and f'(0)
> >>>> would jump from 0 to infinity instantly. So we would have at a = 0
an
> >>>> initial null derivative, then a jump to a very negative derivative as
> a
> >>>> leaves 0, then the derivative would become less and less negative as
a
> >>>> increase up to 1, at a=1 the derivative would again be 0, then the
> >>>> derivative would continue to increase and becode positive as a grows
> >>>> larger than 1 (all these derivatives are computed at x=0, and as
> written
> >>>> previously, they are simply equal to log(a)).
> >>>>
> >>>> To summarize, the two choices are:
> >>>> 1)  first considering a fixed a, strictly positive,
> >>>>  then looking globally at the function a^x for all values x>=0,
> >>>>  then reducing a, noting that all functions start at the same
> >>>> point x=0, y=1 and the derivatives become more and more negative
> >>>> as the function becomes more and more illbehaved
> >>>> 2)  first considering a fixed x, strictly positive,
> >>>>  then reducing a and identifying the limit values is 0 for all
a,
> >>>>  then building a function by packing all the x>0, which is very
> >>>> smooth as it is identically 0 for all x>0
> >>>>  finally adding the limit value at x=0, which in this case would
> >>>> be 0 (and the derivative would also be 0).
> >>>>
> >>>> it seems well accepted to consider the value of 0^0 should be set to
> 1,
> >>>> and as a consequence the corresponding derivative with respect to x
> >>>> should be set to infinity.
> >>>>
> >>>> I fully agree it is not a perfect solution, it is an arbitrary choice.
> >>>> However, this choice is consistent with what all implementations of
> the
> >>>> pow function I have seen (i.e. 0^0 set to 1 instead of 0).
> >>>>
> >>>> Your approach is not wrong, it is as valid as the other one. It is
> >>>> simply not the common choice.
> >>>>
> >>>> I would say an even better choice would have been to say 0^0 *is not*
> >>>> defined and even the value should be set to NaN (not even speaking of
> >>>> the derivative).
> >>>>
> >>>> Does this seem acceptable to you?
> >>>>
> >>>> best regards,
> >>>> Luc
> >>>>
> >>>>>
> >>>>> Cheers,
> >>>>> Ajo.
> >>>>>
> >>>>>
> >>>>>> The limit curve corresponding to a = 0 is therefore a singular
> >> function
> >>>>>> with f(0) = 1 and f(x) = 0 for all x > 0. The fact f(0) =
1 and not
> 0
> >> is
> >>>>>> consistent with the derivative being negative infinity, as by
> >> definition
> >>>>>> the derivative is the limit of [f(0+h)  f(0)] / h when h>0+,
as
> the
> >>>>>> finite difference is 1/h.
> >>>>>>
> >>>>>>> }
> >>>>>>> }else{
> >>>>>>> for (int i = 0; i < function.length;
++i) {
> >>>>>>> function[i] = Double.NaN;
> >>>>>>> }
> >>>>>>
> >>>>>> This alternative case is a good improvement, thanks for it.
I forgot
> >> to
> >>>>>> handle negative cases properly. I have therefore changed the
code
> >>>>>> (committed as r1517788) with this improvement, together with
several
> >>>>>> test cases.
> >>>>>>
> >>>>>>> }
> >>>>>>> } else {
> >>>>>>>
> >>>>>>>
> >>>>>>> in place of :
> >>>>>>>
> >>>>>>> if (a == 0) {
> >>>>>>> if (operand[operandOffset] == 0) {
> >>>>>>> function[0] = 1;
> >>>>>>> double infinity = Double.POSITIVE_INFINITY;
> >>>>>>> for (int i = 1; i < function.length;
++i) {
> >>>>>>> infinity = infinity;
> >>>>>>> function[i] = infinity;
> >>>>>>> }
> >>>>>>> }
> >>>>>>> } else {
> >>>>>>>
> >>>>>>>
> >>>>>>> PS: I think you made a change to DSCompiler.pow too. If
so, what
> >>>> happens
> >>>>>>> when a=0 & x!=0 in that function?
> >>>>>>
> >>>>>> No, I didn't change the other signatures of the pow function.
So the
> >>>>>> value should be OK (i.e. 1) but all derivatives, including the
first
> >>>>>> one, should be NaN. What the new function brings is a correct
> negetive
> >>>>>> infinity first derivative at singularity point, better accuracy
for
> >>>>>> nonsingular points, and possibly faster computation.
> >>>>>>
> >>>>>> best regards,
> >>>>>> Luc
> >>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>> On Mon, Aug 26, 2013 at 12:38 AM, Luc Maisonobe <
> luc@spaceroots.org>
> >>>>>> wrote:
> >>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Ajo Fod <ajo.fod@gmail.com> a écrit :
> >>>>>>>>> Are you saying patched the code? Can you provide
the link?
> >>>>>>>>
> >>>>>>>> I committed it in the development version. You just
have to update
> >>>> your
> >>>>>>>> checked out copy from either the official
> >>>>>>>> Apache subversion repository or the git mirror we talked
about
> in a
> >>>>>>>> previous thread.
> >>>>>>>>
> >>>>>>>> The new method is a static one called pow and taking
a and x as
> >>>>>> arguments
> >>>>>>>> and returning a^x. Not to
> >>>>>>>> Be confused with the nonstatic methods that take only
the power
> as
> >>>>>>>> argument (either int, double or
> >>>>>>>> DerivativeStructure) and use the instance as the base
to apply
> power
> >>>> on.
> >>>>>>>>
> >>>>>>>> Best regards,
> >>>>>>>> Luc
> >>>>>>>>
> >>>>>>>>>
> >>>>>>>>> Ajo
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On Sun, Aug 25, 2013 at 1:20 PM, Luc Maisonobe <
> luc@spaceroots.org
> >>>
> >>>>>>>>> wrote:
> >>>>>>>>>
> >>>>>>>>>> Le 24/08/2013 11:24, Luc Maisonobe a écrit
:
> >>>>>>>>>>> Le 23/08/2013 19:20, Ajo Fod a écrit :
> >>>>>>>>>>>> Hello,
> >>>>>>>>>>>
> >>>>>>>>>>> Hi Ajo,
> >>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> This shows one way of interpreting the
derivative for strictly
> >> +ve
> >>>>>>>>>> numbers.
> >>>>>>>>>>>>
> >>>>>>>>>>>> public static void main(final String[]
args) {
> >>>>>>>>>>>> final double x = 1d;
> >>>>>>>>>>>> DerivativeStructure dsA = new
DerivativeStructure(1,
> 1,
> >> 0,
> >>>>>>>>> x);
> >>>>>>>>>>>> System.out.println("Derivative
of a^x wrt x");
> >>>>>>>>>>>> for (int p = 10; p < 21;
p++) {
> >>>>>>>>>>>> double a;
> >>>>>>>>>>>> if (p < 20) {
> >>>>>>>>>>>> a = 1d / Math.pow(2d,
p);
> >>>>>>>>>>>> } else {
> >>>>>>>>>>>> a = 0d;
> >>>>>>>>>>>> }
> >>>>>>>>>>>> final DerivativeStructure
a_ds = new
> >>>>>>>>> DerivativeStructure(1,
> >>>>>>>>>> 1,
> >>>>>>>>>>>> a);
> >>>>>>>>>>>> final DerivativeStructure
out = a_ds.pow(dsA);
> >>>>>>>>>>>> final double calc = (Math.pow(a,
x + EPS) 
> >>>>>>>>> Math.pow(a, x))
> >>>>>>>>>> /
> >>>>>>>>>>>> EPS;
> >>>>>>>>>>>> System.out.format("Derivative@%f=%f
%f\n", a,
> >> calc,
> >>>>>>>>>>>> out.getPartialDerivative(new int[]{1}));
> >>>>>>>>>>>> }
> >>>>>>>>>>>> }
> >>>>>>>>>>>>
> >>>>>>>>>>>> At this point I"m explicitly substituting
the rule that
> >>>>>>>>>> derivative(a^x) =
> >>>>>>>>>>>> 0 for a=0.
> >>>>>>>>>>>
> >>>>>>>>>>> Yes, but this fails for x = 0, as the limit
of the finite
> >>>>>>>>> difference is
> >>>>>>>>>>> infinity and not 0.
> >>>>>>>>>>>
> >>>>>>>>>>> You can build your own function which explicitly
assumes a is
> >>>>>>>>> constant
> >>>>>>>>>>> and takes care of special values as follows:
> >>>>>>>>>>>
> >>>>>>>>>>> public static DerivativeStructure aToX(final
double a,
> >>>>>>>>>>>
final
> DerivativeStructure
> >>>>>>>>> x) {
> >>>>>>>>>>> final double lnA = (a == 0 &&
x.getValue() == 0) ?
> >>>>>>>>>>> Double.NEGATIVE_INFINITY
:
> >>>>>>>>>>> FastMath.log(a);
> >>>>>>>>>>> final double[] function = new double[1
+ x.getOrder()];
> >>>>>>>>>>> function[0] = FastMath.pow(a, x.getValue());
> >>>>>>>>>>> for (int i = 1; i < function.length;
++i) {
> >>>>>>>>>>> function[i] = lnA * function[i
 1];
> >>>>>>>>>>> }
> >>>>>>>>>>> return x.compose(function);
> >>>>>>>>>>> }
> >>>>>>>>>>>
> >>>>>>>>>>> This will work and provides derivatives
to any order for almost
> >> any
> >>>>>>>>>>> values of a and x, including a=0, x=1 as
in your exemple, but
> >> also
> >>>>>>>>>>> slightly better for a=0, x=0. However, it
still has an
> important
> >>>>>>>>>>> drawback: it won't compute the nth order
derivative correctly
> >> for
> >>>>>>>>> a=0,
> >>>>>>>>>>> x=0 and n > 1. It will provide NaN for
these higher order
> >>>>>>>>> derivatives
> >>>>>>>>>>> instead of +/infinity according to parity
of n.
> >>>>>>>>>>
> >>>>>>>>>> I have added a similar function to the DerivativeStructure
class
> >>>>>>>>> (with
> >>>>>>>>>> some errors above corrected). The main interesting
property of
> >> this
> >>>>>>>>>> function is that it is more accurate that converting
a to a
> >>>>>>>>>> DerivativeStructure and using the general x^y
function. It does
> >> its
> >>>>>>>>> best
> >>>>>>>>>> to handle the special case, but as written above,
this does NOT
> >> work
> >>>>>>>>> for
> >>>>>>>>>> general combination (i.e. more than one variable
or more than
> one
> >>>>>>>>>> order). As soon as there is a combination, the
derivative will
> >>>>>>>>> involve
> >>>>>>>>>> something like df/dx * dg/dy and as infinities
and zeros are
> >>>>>>>>> everywheren
> >>>>>>>>>> NaN appears immediately for these partial derivatives.
This
> cannot
> >>>> be
> >>>>>>>>>> avoided.
> >>>>>>>>>>
> >>>>>>>>>> If you stay away from the singularity, the function
behaves
> >>>>>>>>> correctly.
> >>>>>>>>>>
> >>>>>>>>>> best regards,
> >>>>>>>>>> Luc
> >>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> This is a known problem that we already
encountered when
> dealing
> >>>>>>>>> with
> >>>>>>>>>>> rootN. Here is an extract of a comment in
the test case
> >>>>>>>>>>> testRootNSingularity, where similar NaN
appears instead of +/
> >>>>>>>>> infinity.
> >>>>>>>>>>> The dsZero instance in the comment is simple
the x parameter of
> >> the
> >>>>>>>>>>> function, as a derivativeStructure with
value 0.0 and depending
> >> on
> >>>>>>>>>>> itself (dsZero = new DerivativeStructure(1,
maxOrder, 0, 0.0)):
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> // the following checks shows a LIMITATION
of the current
> >>>>>>>>> implementation
> >>>>>>>>>>> // we have no way to tell dsZero is a pure
linear variable x =
> 0
> >>>>>>>>>>> // we only say: "dsZero is a structure with
value = 0.0,
> >>>>>>>>>>> // first derivative = 1.0, second and higher
derivatives =
> 0.0".
> >>>>>>>>>>> // Function composition rule for second
derivatives is:
> >>>>>>>>>>> // d2[f(g(x))]/dx2 = f''(g(x)) * [g'(x)]^2
+ f'(g(x)) * g''(x)
> >>>>>>>>>>> // when function f is the nth root and x
= 0 we have:
> >>>>>>>>>>> // f(0) = 0, f'(0) = +infinity, f''(0) =
infinity (and higher
> >>>>>>>>>>> // derivatives keep switching between +infinity
and infinity)
> >>>>>>>>>>> // so given that in our case dsZero represents
g, we have g(x)
> =
> >> 0,
> >>>>>>>>>>> // g'(x) = 1 and g''(x) = 0
> >>>>>>>>>>> // applying the composition rules gives:
> >>>>>>>>>>> // d2[f(g(x))]/dx2 = f''(g(x)) * [g'(x)]^2
+ f'(g(x)) * g''(x)
> >>>>>>>>>>> // = infinity * 1^2 + +infinity
* 0
> >>>>>>>>>>> // = infinity + NaN
> >>>>>>>>>>> // = NaN
> >>>>>>>>>>> // if we knew dsZero is really the x variable
and not the
> >> identity
> >>>>>>>>>>> // function applied to x, we would not have
computed f'(g(x)) *
> >>>>>>>>> g''(x)
> >>>>>>>>>>> // and we would have found that the result
was infinity and
> not
> >>>>>>>>> NaN
> >>>>>>>>>>>
> >>>>>>>>>>> Hope this helps
> >>>>>>>>>>> Luc
> >>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> Thanks,
> >>>>>>>>>>>> Ajo.
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> On Fri, Aug 23, 2013 at 9:39 AM, Luc
Maisonobe
> >>>>>>>>> <Luc.Maisonobe@free.fr
> >>>>>>>>>>> wrote:
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Hi Ajo,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Le 23/08/2013 17:48, Ajo Fod a écrit
:
> >>>>>>>>>>>>>> Try this and I'm happy to explain
if necessary:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> public class Derivative {
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> public static void main(final
String[] args) {
> >>>>>>>>>>>>>> DerivativeStructure
dsA = new DerivativeStructure(1,
> >> 1,
> >>>>>>>>> 0,
> >>>>>>>>>> 1d);
> >>>>>>>>>>>>>> System.out.println("Derivative
of constant^x wrt
> x");
> >>>>>>>>>>>>>> for (int a = 3; a <
3; a++) {
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> We have chosen the classical definition
which implies c^x is
> >> not
> >>>>>>>>>> defined
> >>>>>>>>>>>>> for real r and negative c.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Our implementation is based on the
decomposition c^r = exp(r
> *
> >>>>>>>>> ln(c)),
> >>>>>>>>>>>>> so the NaN comes from the logarithm
when c <= 0.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Noe also that as explained in the
documentation here:
> >>>>>>>>>>>>> <
> >>>>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>
> >>
> http://commons.apache.org/proper/commonsmath/userguide/analysis.html#a4.7_Differentiation
> >>>>>>>>>>>>>> ,
> >>>>>>>>>>>>> there are no concepts of "constants"
and "variables" in this
> >>>>>>>>> framework,
> >>>>>>>>>>>>> so we cannot draw a line between
c^r as seen as a univariate
> >>>>>>>>> function
> >>>>>>>>>> of
> >>>>>>>>>>>>> r, or as a univariate function of
c, or as a bivariate
> function
> >>>>>>>>> of c
> >>>>>>>>>> and
> >>>>>>>>>>>>> r, or even as a pentavariate function
of p1, p2, p3, p4, p5
> >> with
> >>>>>>>>> both c
> >>>>>>>>>>>>> and r being computed elsewhere from
p1...p5. So we don't make
> >>>>>>>>> special
> >>>>>>>>>>>>> cases for the case c = 0 for example.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Does this explanation make sense
to you?
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> best regards,
> >>>>>>>>>>>>> Luc
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>> final DerivativeStructure
a_ds = new
> >>>>>>>>>> DerivativeStructure(1,
> >>>>>>>>>>>>> 1,
> >>>>>>>>>>>>>> a);
> >>>>>>>>>>>>>> final DerivativeStructure
out = a_ds.pow(dsA);
> >>>>>>>>>>>>>> System.out.format("Derivative@%d=%f\n",
a,
> >>>>>>>>>>>>>> out.getPartialDerivative(new
int[]{1}));
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> On Fri, Aug 23, 2013 at 7:59
AM, Gilles
> >>>>>>>>> <gilles@harfang.homelinux.org
> >>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On Fri, 23 Aug 2013 07:17:35
0700, Ajo Fod wrote:
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Seems like the DerivativeCompiler
returns NaN.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> IMHO it should return
0.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> What should be 0? And Why?
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Is this worthy of an
issue?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> As is, no.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Gilles
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>>>> Ajo
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>
> >>>>
> >>
> ****
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> >>>>>>>>>
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