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Changes:
* Removed unnecessary types from the Functor by inferring from its types
* Removed inputs() function reference, replaced with .rows()
* Updated the forward constructor to use variadic templates
* Added optional parameters to the Fuctor for passing parameters,
control signals, etc
* Has been tested with fixed size and dynamic matricies
Ammendment by chtz: overload operator() for compatibility with not fully conforming compilers
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As discussed on the list (too long to explain here).
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construction of generic expressions working
for both dense and sparse matrix. A nicer solution
would be to use CwiseBinaryOp for any kind of matrix.
To this end we either need to change the overall design
so that the base class(es) depends on the kind of matrix,
or we could add a template parameter to each expression
type (e.g., int Kind = ei_traits<MatrixType>::Kind)
allowing to specialize each expression for each kind of matrix.
* Extend AutoDiffScalar to work with sparse vector expression
for the derivatives.
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* fix namespace issue
* simplify Jacobian code
* fix issue with "Dynamic derivatives"
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AutoDiffJacobian::operator()(x,value) exactly as the original functor
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it never made very precise sense. but now does it still make any?
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mode but the advantage of using Eigen's expression template to compute
the derivatives (unless you nest an AutoDiffScalar into an Eigen's
matrix).
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Emil Fresk
Benoit Jacob
Gael Guennebaud
Thomas Capricelli