eigen

问题 I am trying to use a custom preconditioner for an iterative solver (CG for instance) with Eigen. Specifically, I have to solve a similar problem multiple times: the matrix changes slightly but stays close to a mean matrix. I would like to compute a Cholesky decomposition of my mean matrix and then use this as a preconditioner. What I had in mind is something like: ConjugateGradient< SparseMatrix<double>, Lower, CholmodSupernodalLLT<SparseMatrix<double>> > solver(meanMatrix); solver

问题 I like to place Eigen fixed-size matrices into the shared memory. But on execution I am getting the following error: /usr/include/eigen3/Eigen/src/Core/DenseStorage.h:78: Eigen::internal::plain_array<T, Size, MatrixOrArrayOptions, 16>::plain_array() [with T = double; int Size = 2; int MatrixOrArrayOptions = 0]: Assertion `(reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & 0xf) == 0 && "this assertion is explained here: " "http://eigen.tuxfamily.org/dox-devel

问题 I'm trying to implement a modifyable custom expression using Eigen, similar to this question. Basically, what I want is something similar to the indexing example in the tutorial, but with the possibility to assign new values to the selected coefficients. As suggested in the accepted answer in the question mentioned above, I have looked into the Transpose implementation and tried many things, yet without success. Basically, my attempts are failing with errors like 'Eigen::internal::evaluator

问题 I am writing a custom C++ numerical library that relies heavily on linear algebra routines. I am also using Eigen to cater for the actual matrix operations. I want to decouple my library from the Eigen implementation so that it is unaware of Eigen. This will allow me to keep Eigen references in one place and make it easy to change the linear algebra library to another implementation in the near future. In java, this would be relatively simple. However I am running into difficulties with Eigen

问题 I am writing a wrapper to Eigen for my personal use and I encountered the following weird behavior: void get_QR(MatrixXd A, MatrixXd& Q, MatrixXd& R) { HouseholderQR<MatrixXd> qr(A); Q = qr.householderQ()*(MatrixXd::Identity(A.rows(),A.cols())); R = qr.matrixQR().block(0,0,A.cols(),A.cols()).triangularView<Upper>(); } void get_QR(double* A, int m, int n, double*& Q, double*& R) { // Maps the double to MatrixXd. Map<MatrixXd> A_E(A, m, n); // Obtains the QR of A_E. MatrixXd Q_E, R_E; get_QR(A

问题 I do want to write a custom nullary-expression in Eigen3, which is modifyable. As described in the Eigen documentation it seems like nullary-expression are not modifiable. Is there an easy way to get something like a modifyable custom nullary-expression? I would like to use it to write a custom data wrapper. 回答1: Nullary-expressions are indeed read-only. For read-write expressions, you currently has to write your own expression. To this end you can start with this tutorial and take

问题 I have browsed the tutorial of eigen at https://eigen.tuxfamily.org/dox-devel/group__TutorialMatrixArithmetic.html it said "Note: for BLAS users worried about performance, expressions such as c.noalias() -= 2 * a.adjoint() * b; are fully optimized and trigger a single gemm-like function call." but how about computation like H.transpose() * H , because it's result is a symmetric matrix so it should only need half time as normal A*B, but in my test, H.transpose() * H spend same time as H

问题 Eigen is a popular C++ library, but icpc seems to have a problem generating debugging info from code that uses Eigen. I'm using the compiler icpc version 13.1.1. I checked with both Eigen 3.2.8 and 3.1.3. It's going to be hard to recompile all the libraries I need with another compiler, so does anyone see a good solution to get Eigen to work with a debugger? The problem is that variable values don't always get updated in the debugger. Here is main.cpp #include "stdio.h" #include "/home

问题 This is related to this question. I have today experimented a bit with Conjugate Gradient, in particular I experimented with max_iterations and tolerance . It is faster but not fast enough. According to the documentation it should be enough to add -fopenmp in the compilation to enable multi-threading . I have tested using both `omp_set_num_threads(nbrThreads); Eigen::setNbThreads(nbrThreads);` It makes no difference in time if I use 5 threads or 1 thread, and that I think is a bit strange.

问题 Is it possible in Eigen to do the equivalent of the following operation in Matlab? A=rand(10,10); indices = [2,5,6,8,9]; B=A(indices,indices) I want to have a submatrix as a view on the original matrix with given, non consecutive indices. The best option would be to have a shared memory view of the original matrix, is this possible? I've figured out a method that works but is not very fast, since it involves non vectorized for loops: MatrixXi slice(const MatrixXi &A, const std::set<int>