eigen3

问题 As the documentation in Eigen C++ library points out at many places, to get the maximum performance in terms of the computation time, we need to avoid temporary objects as far as possible. In my application, I deals with Dynamic size matrices. I would like to know the creation of temporary matrices in my calculations. Is there any general method to identify the creation of the temporary matrices? For example, Eigen::MatrixXf B, C, D; ....some initialization for B, C, D Eigen::MatrixXf A = B*C

问题 I am trying to index a matrix in indexes which follow an arithmetic sequence. According to the Eigen tutorial on the official website, I should use Eigen::seq(firstVal, lastVal, step) to generate this sequence. After calling this the error, as pasted in the title of this thread pops up. I checked all the files of my local eigen folder, for the 'seq' method, but no luck. It wasn't anywhere. I guess this means that some file is missing, right? Code goes smth like this. Headers at the top

问题 In my application I need to avoid dynamic memory allocation (malloc like) except in the class constructors. I have a sparse semidefinite matrix M whose elements change during the program execution but it mantains a fixed sparsity pattern. In order to solve many linear systems M * x = b as fast as possible, the idea is to use inplace decomposition in my class constructor as described in Inplace matrix decompositions, then call factorize method whenever M changes: struct MyClass { private:

问题 In my application I need to avoid dynamic memory allocation (malloc like) except in the class constructors. I have a sparse semidefinite matrix M whose elements change during the program execution but it mantains a fixed sparsity pattern. In order to solve many linear systems M * x = b as fast as possible, the idea is to use inplace decomposition in my class constructor as described in Inplace matrix decompositions, then call factorize method whenever M changes: struct MyClass { private:

问题 I would like to create a matrix of size 2N x 9 where N is a dynamic value by vertical stacking 2N 1x9 matrices. Here's what I tried doing. using CoefficientMatrix = Eigen::Matrix<T, Eigen::Dynamic, 9>; using CoefficientRow = Eigen::Matrix<T, 1, 9>; CoefficientMatrix A(2*N, 9); for (int i = 0; i < N; i++) { CoefficientRow ax; CoefficientRow ay; // fill in ax and ay A << ax, ay; } But, I get the following runtime error. Assertion failed: (((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr

问题 In the past when I've needed to solve the Sylvester equation, AX + XB = C , I've used scipy 's function, solve_sylvester [1], which apparently works by using the Bartels-Stewart algorithm to get things into upper triangular form, and then solving the equation using lapack . I now need to solve the equation using eigen . eigen provides an function, matrix_function_solve_triangular_sylvester [2], which seems by the documentation to be similar to the lapack function which scipy calls. I'm

问题 In the past when I've needed to solve the Sylvester equation, AX + XB = C , I've used scipy 's function, solve_sylvester [1], which apparently works by using the Bartels-Stewart algorithm to get things into upper triangular form, and then solving the equation using lapack . I now need to solve the equation using eigen . eigen provides an function, matrix_function_solve_triangular_sylvester [2], which seems by the documentation to be similar to the lapack function which scipy calls. I'm

问题 In the past when I've needed to solve the Sylvester equation, AX + XB = C , I've used scipy 's function, solve_sylvester [1], which apparently works by using the Bartels-Stewart algorithm to get things into upper triangular form, and then solving the equation using lapack . I now need to solve the equation using eigen . eigen provides an function, matrix_function_solve_triangular_sylvester [2], which seems by the documentation to be similar to the lapack function which scipy calls. I'm

问题 In the past when I've needed to solve the Sylvester equation, AX + XB = C , I've used scipy 's function, solve_sylvester [1], which apparently works by using the Bartels-Stewart algorithm to get things into upper triangular form, and then solving the equation using lapack . I now need to solve the equation using eigen . eigen provides an function, matrix_function_solve_triangular_sylvester [2], which seems by the documentation to be similar to the lapack function which scipy calls. I'm

问题 I want to use Eigen::Ref to have non-template functions using Eigen::Matrix arguments. My problem is that in these functions, I may have to resize the matrices referenced by the Eigen::Ref. I understand that for generality an Eigen::Ref should not be resized because it can map to an expression or a matrix block, but In my case, I am sure that what is behind my Eigen::Ref is an Eigen::Matrix. To illustrate this: #include "Eigen/Dense" void add(Eigen::Ref<Eigen::MatrixXd> M, const Eigen::Ref