eigen3

问题 I am trying to use Boost.Units with Eigen 3.3.1, but after following the instructions here, and some pieces of informations found around, I still cannot figure out how to make norm() work. Here is what I have so far (sorry for the long code block): #include <boost/units/quantity.hpp> #include <boost/units/systems/si/length.hpp> #include <boost/units/systems/si/area.hpp> #include <boost/units/cmath.hpp> #include <Eigen/Geometry> namespace Eigen { //specialization of numeric traits using boost:

问题 I am trying to use Boost.Units with Eigen 3.3.1, but after following the instructions here, and some pieces of informations found around, I still cannot figure out how to make norm() work. Here is what I have so far (sorry for the long code block): #include <boost/units/quantity.hpp> #include <boost/units/systems/si/length.hpp> #include <boost/units/systems/si/area.hpp> #include <boost/units/cmath.hpp> #include <Eigen/Geometry> namespace Eigen { //specialization of numeric traits using boost:

问题 As said I want to replace all < 0 elements in a eigen3 matrix in C++ with zero in most efficient manner. I check that there are negative elements using: (result.array() < 0).any() 回答1: A nicer and more efficient way than your proposed method would be to use the select method. result = (result.array() < 0).select(0, result); 回答2: I found a way: Create a matrix of zeros of same shape, zero_matrix.setZero(); And find coeff wise maximum between zero matrix and your matrix. result = result.array()

问题 As said I want to replace all < 0 elements in a eigen3 matrix in C++ with zero in most efficient manner. I check that there are negative elements using: (result.array() < 0).any() 回答1: A nicer and more efficient way than your proposed method would be to use the select method. result = (result.array() < 0).select(0, result); 回答2: I found a way: Create a matrix of zeros of same shape, zero_matrix.setZero(); And find coeff wise maximum between zero matrix and your matrix. result = result.array()

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . I am working on large scale data like 300000 x 300000 matrix currently may interest. It is really hard to process in matlab due to "Out of memory" error so I decide to use EIGEN. Is there any restriciton for eigen in the matrix size? 回答1: The dense matrices in EIGEN are stored in a contiguous block of memory,

问题 I've been using the ConjugateGradient solver in Eigen 3.2 and decided to try upgrading to Eigen 3.3.3 with the hope of benefiting from the new multi-threading features. Sadly, the solver seems slower (~10%) when I enable -fopenmp with GCC 4.8.4. Looking at xosview, I see that all 8 cpus are being used, yet performance is slower... After some testing, I discovered that if I disable compiler optimization (use -O0 instead of -O3 ), then -fopenmp does speed up the solver by ~50%. Of course, it's

问题 I've been using the ConjugateGradient solver in Eigen 3.2 and decided to try upgrading to Eigen 3.3.3 with the hope of benefiting from the new multi-threading features. Sadly, the solver seems slower (~10%) when I enable -fopenmp with GCC 4.8.4. Looking at xosview, I see that all 8 cpus are being used, yet performance is slower... After some testing, I discovered that if I disable compiler optimization (use -O0 instead of -O3 ), then -fopenmp does speed up the solver by ~50%. Of course, it's

问题 I have some complex, dense vectors/matrices in the Eigen3 library, and I want to extract the real and imaginary parts into separate arrays. In Matlab, I could do something like cplxFoo = [1, 1i; -1i -1] re = real(cplxFoo) im = imag(cplxFoo) which expectantly yields cplxFoo = 1.0000 + 0.0000i 0.0000 + 1.0000i 0.0000 - 1.0000i -1.0000 + 0.0000i re = 1 0 0 -1 im = 0 1 -1 0 Is there anything like the real() and imag() Matlab functions in Eigen3? Right now, the only thing I know will work is

问题 I was trying to explore the option of "solveInPlace()" function while using LLT in Eigen3.3.7 to speed up the matrix inverse computation in my application. I used the following code to test it. int main() { const int M=3; Eigen::Matrix<MyType,Eigen::Dynamic,Eigen::Dynamic> R = Eigen::Matrix<MyType,Eigen::Dynamic,Eigen::Dynamic>::Zero(M,M); // to make sure full rank for(int i=0; i<M*2; i++) { const Eigen::Matrix<MyType, Eigen::Dynamic,1> tmp = Eigen::Matrix<MyType,Eigen::Dynamic,1>::Random(M);

问题 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} 回答1: 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