Re: [eigen] AutoDiffScalar

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Thanks for the quick reply!
I will have a look at the dev-branch.
You are probably right that constants can be avoided, but it would be
nice to be able to use code like

template<typename T>
void f(const T* in, T* out)
    const T& x = in[0];
    out[0] = 2*x + sin(2*pi*y) + pow(z, 3) +...;

It would be quite verbose to convert it to avoid constants and given
ADS<->constant overloads there shouldn't be any penalty

As for use cases,  I would think this class to be a perfect fit with
the Levenberg-Marquardt algorithm when the user doesn't supply
Basically all the Functors in
eigen2-cminpack/src/tip/unsupported/test/NonLinear.cpp could be called
with the ADS.

I've had a quick look at AutoDiffVector, but I'll check it out for
sure once I've got the Scalar working.


2009/10/15 Gael Guennebaud <gael.guennebaud@xxxxxxxxx>:
> 2009/10/15 Björn Piltz <bjornpiltz@xxxxxxxxxxxxxx>
>> Hi all,
>> I've been following the work on the cminpack branch with interest and
>> right now I'm looking at AutoDiffScalar.
>> I've written some tests and seen that it has the potential to be very
>> efficient, mainly thanks to the lazy evaluation I guess, but I have
>> some questions.
>> The fixed size implementation already seems to be mostly done, but
>> there are some problems with the dynamic version.
>> Look for example at the implementation of addition:
>> template<typename OtherDerType>
>> inline const
>> AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType>
>> >
>> operator+(const AutoDiffScalar<OtherDerType>& other) const
>> {
>>    return
>> AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType>
>> >(
>>    m_value + other.value(),
>>    m_derivatives + other.derivatives());
>> }
>> This implementation crashes when "other" was initialized through c'tor
>> AutoDiffScalar(const Scalar& value) and other.derivatives() is of size
>> zero.
> Oh I see, but do you have a use case for that ? I mean this happens only
> when you convert a constant to an active scalar type, and I think this could
> be avoided in most cases. For instance,
> Scalar sum = 0;
> for(i=0 ...) sum += v[i];
> can be changed to:
> Scalar sum = v[0];
> for(i=1....) sum += v[i];
> So following this idea, I guess we should also add overloads for
> "AutoDiffScalar + constant" and similars....
> Anyway, can you retry with the devel branch, I've just committed a fix which
> resize and set to zero the derivatives of one argument if the other is a
> null Matrix. Yes I know this is not optimal but I don't know how to do it
> more efficiently.
>> The fix is not obvious to me since we need to return a binary
>> expression and just m_derivatives won't do.
>> I could check the size of other.m_derivatives and fill it up with
>> zeros, when appropriate, but I would have to do that check at compile
>> time since "OtherDerType" could also be a binary expression or
>> something similar. I haven't found a way at compilation to check if an
>> type is a "normal matrix" or some kind of an expression.
>> I hope somebody has an idea of how to resolve this, because a fast
>> forward differentiation implementation with expression templates
>> supporting dynamic size vectors would be a really cool feature. I've
>> compared this implementation to Sacado, the only other good template
>> implementation I could find out there, and this one compares very
>> favorably(fast).
> I did not know about Sacado, but I'm glad we are already faster :) I've seen
> it is part of the trilineos framework. This framework really provide a lot
> of features !
> btw, about this module, there is still a work in progress AutoDiffVector
> class. Its goal is to efficiently representing a vector of active variables.
> Basically it will behave like a Matrix<AutoDiffScalar,Size,1> but internally
> it will directly store the Jacobian matrix allowing much higher
> performances, and ease of use (directly initialize the Jacobian to the
> identity, directly get the Jacobian as a matrix, etc...).
> gael.
>> Any feedback will be appreciated
>> Björn

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