eigen

I can't figure out if and how it's possible to map a two-dimensional double array to an Eigen::Matrix. Is it possible to map an array double d[][] which I receive as double** p to an Eigen::Matrix? While one-dimensinal arrays work fine, I wasn't able to map p to Eigen::Map<Eigen::Matrix<double, n, n>> . Is that possible and how could it be done? The size n is not really constant, but I could accept a hard coded size. I tried several versions, but none worked. I thought the following should work (assume the size n would be 4). Eigen::Map<Eigen::Matrix<double, 4, 4>> p_OUTPUT(&p[0][0]); The code

I am currently working on a project where we need to solve |Ax - b|^2 . In this case, A is a very sparse matrix and A'A has at most 5 nonzero elements in each row. We are working with images and the dimension of A'A is NxN where N is the number of pixels. In this case N = 76800 . We plan to go to RGB and then the dimension will be 3Nx3N . In matlab solving (A'A)\(A'b) takes about 0.15 s, using doubles. I have now done some experimenting with Eigens sparse solvers. I have tried: SimplicialLLT SimplicialLDLT SparseQR ConjugateGradient and some different orderings. The by far best so far is

I'm making use of Eigen library which promises vectorization of matrix operations. I don't know how to use the files given in Eigen and write a makefile. The source files which make use of Eigen include files as listed below, these are not even header files (They are just some text files)- <Eigen/Core> <Eigen/Dense> <Eigen/Eigen> and so on. On Eigen's webpage, it's mentioned that, in order to use its functions I don't have to build the project, then how can I include these files in my makefile to build my project. My example main.c file looks like this. Can anyone show me how to write a

Consider the exponential smoother template class below. This class is for smoothing/filtering sequential data exponentially (see update method). Elemtype might be an vector and Floattype is usually a scalar. E.g. ExponentialSmoother<Eigen::Vector2f, float> x(0.1, Vector2f(0.5, 0.5)); In this example the second template parameter Floattype could be avoided because Eigen's Matrix class contains a nested typedef to get the scalar base type: Vector2f::Scalar It is also reasonable to instantiate both Elemtype and Floatype as floats to smooth one dimensional data. In this case the second template

I implemented a comparison operator operator< for Eigen::VectorXd , and sometimes, I need to pass a compare function to another of my function, I am tired of wrapping the operator< into [](const VectorXd& v1, const VectorXd& v2)->bool{return v1 < v2} , so I think the std::less class would be useful, as, to my understanding, it can generate the lambda function as long as operator< is defined. However, I found that std::less<VectorXd> didn't work for me, for example, the below code works fine: #include "Eigen/Dense" #include <iostream> #include <functional> using namespace std; using namespace

I have a do_magic method which takes a double and adds 42 to it. I'd like to apply this method to each coefficient of a Eigen::Matrix or Eigen::Array (that means, I wouldn't mind if it's only possible with one of both types). Is this possible? Like this: Eigen::MatrixXd m(2, 2); m << 1,2,1,2; m.applyCoefficientWise(do_magic); // m is now 43, 44, 43, 44 You can use unaryExpr , though this returns a new view onto the matrix, rather than allowing you to modify the elements in place. Copying the example out of the documentation: double ramp(double x) { if (x > 0) return x; else return 0; } int

Suppose I have Eigen::VectorXd x; //{1,2,3,4,5,6,7,8} and Eigen::VectorXd ind_vec; //{0,2,4,5} Is there a way an easy way to extract the ind_vec elements of x? Something like: x.extract(ind_vec) returning {1, 3, 5, 6} Since the current answer was not satisfactory for me, I googled a bit and I found this tutorial in the Eigen documentation. #include <Eigen/Dense> #include <iostream> using namespace std; int main() { Eigen::ArrayXf v(6); v << 1, 2, 3, 4, 5, 6; cout << "v.head(3) =" << endl << v.head(3) << endl << endl; cout << "v.tail<3>() = " << endl << v.tail<3>() << endl << endl; v.segment(1

I am used to Eigen for almost all my mathematical linear algebra work. Recently, I have discovered that Boost also provides a C++ template class library that provides Basic Linear Algebra Library ( Boost::uBLAS ). This got me wondering if I can get all my work based only on boost as it is already a major library for my code. A closer look at both didn't really got me a clearer distinction between them: Boost::uBLAS : uBLAS provides templated C++ classes for dense, unit and sparse vectors, dense, identity, triangular, banded, symmetric, hermitian and sparse matrices. Views into vectors and