Re: [eigen] Iterators with dense matrices: (was: Areas of eigen3 variation and using eigen2/3 in generic code)

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On Sun, Jan 3, 2010 at 8:59 PM, Benoit Jacob <jacob.benoit..1@xxxxxxxxx> wrote:
2010/1/3 Paul C. Leopardi <paul.leopardi@xxxxxxxxxxxx>:

So Eigen's approach has been to let the user write higher-level code in
terms of /expressions/ and /functors/. The user can provide his own
expressions and functors, which describe how coefficient(i,j) is computed,
optionnally providing a SIMD implementation for which we have a portable
infrastructure; and then the user uses that with Eigen's built-in loops..

Definitely the right approach.  The higher level and closer to linear algebra the better... But for cases where you have not implemented an algorithm and default generic algorithms exist, it is necessary to be able to use them without initially implementing extensions to Eigen.

For example:  is there an Eigen equivalent to the following things I do all day long:
std::vector<double> vec(4);
//... fill it
std::vector<double> vec2(4);
std::transform(vec.begin(), vec.end(), vec2.begin(), [](double x){return my_func(x)}); //for some function

//or do a binary search from the 2nd item for the number 1.0, with it sorted with some functor
std::binary_search(vec.begin() + 1, vec.end(), 1.0, my_strict_weak_ordering_functor());

 
I still wonder:
general-purpose Array class, maybe people will also need general purpose array
processing, so our built-in loops might no longer be enough and
iterators migth become
really needed?

I think they are necessary for me to standardize on the library to have the option (at least for Vectors).  I have too much of my brain and code invested in generic code around std patterns, iterators, and eventually ranges.


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