Get matrix views/blocks from a Eigen::VectorXd without copying (shared memory)

Question

Does anyone know a good way how i can extract blocks from an Eigen::VectorXf that can be interpreted as a specific Eigen::MatrixXf without copying data? (the vector should contains several flatten matrices)

e.g. something like that (pseudocode):

VectorXd W = VectorXd::Zero(8);

// Use data from W and create a matrix view from first four elements
Block<2,2> A = W.blockFromIndex(0, 2, 2);
// Use data from W and create a matrix view from last four elements
Block<2,2> B = W.blockFromIndex(4, 2, 2);

// Should also change data in W
A(0,0) = 1.0
B(0,0) = 1.0

The purpose is simple to have several representations that point to the same data in memory.

This can be done e.g. in python/numpy by extracting submatrix views and reshape them.

A = numpy.reshape(W[0:0 + 2 * 2], (2,2))

I Don't know whether Eigen supports reshape methods for Eigen::Block.

I guess, Eigen::Map is very similar except it expects plain c-arrays / raw memory. (Link: Eigen::Map).

Chris

Answer 1

If you want to reinterpret a subvector as a matrix then yes, you have to use Map:

Map<Matrix2d> A(W.data());          // using the first 4 elements
Map<Matrix2d> B(W.tail(4).data());  // using the last 4 elements
Map<MatrixXd> C(W.data()+6, 2,2);   // using the 6th to 10th elements
                                    // with sizes defined at runtime.