Hi Toby, could you specify which C++ standard you are using?
The possible implementations for a free function that replaces your lambda depends on this. E.g. since C++14 you should be able to just declare the function as auto func(...) Cheers, David > On 17. Dec 2019, at 13:15, Wood, Tobias <[email protected]> wrote: > > Hello, > > I am trying to write a finite difference function for Eigen::Tensors. > Currently I am using a lambda: > > auto diff = [](Eigen::Tensor<std::complex<float>, 3> const &a, Eigen::Index > const d) { > Dims3 const sz{a.dimension(0) - 2, a.dimension(1) - 2, a.dimension(2) - 2}; > Dims3 const st1{1, 1, 1}; > Dims3 fwd{1, 1, 1}; > Dims3 bck{1, 1, 1}; > fwd[d] = 2; > bck[d] = 0; > > return (a.slice(fwd, sz) - a.slice(bck, sz)) / a.slice(st1, > sz).constant(2.f); > }; > > This works okay. However, I would like to do two things: > > 1 – Change this from a lambda into a free function. What should the return > type of the function be, so that it returns the expression/operation and does > not evaluate the tensor into a temporary? > 2 – I would prefer to pass in a TensorRef, so I can pass in a .chip() from a > 4D tensor without a temporary. When I try to do this with the current lambda, > and I am assigning to a slice, e.g. > > b.chip<3>(0).slice(st1, sz) = diff(a, 0); > > I get the following error: > > TensorRef.h:413:51: error: cannot initialize return object of type > 'Eigen::TensorEvaluator<const > Eigen::TensorRef<Eigen::Tensor<std::__1::complex<float>, 3, 0, long> >, > Eigen::ThreadPoolDevice>::Scalar *' (aka 'std::__1::complex<float> *') with > an rvalue of type 'const > Eigen::TensorRef<Eigen::Tensor<std::__1::complex<float>, 3, 0, long> > >::Scalar *' (aka 'const std::__1::complex<float> *') > > This appears to be complaining that I can’t assign a `const > std::complex<float> *` to a `std::complex<float> *`? > > Thanks in advance, > Toby
