What is the fastest method for matrix multiplication of an Eigen::Matrix over a random set of column indices? Eigen::MatrixXd mat = Eigen::MatrixXd::Random(100,
I want to implement some matrix operations such as product and inverse computation over a Galois Field GF(64) in C++ language. I have normally used Eigen libra
Suppose you have a matrix A: 1 2 3 4 There are two flattenings: 1 2 3 4 and 1 3 2 4 If the default (ColMajor) storage type is used, we can get the latt
I'm a complete beginner at Eigen, including headers and coding in general. I tried installing Eigen's libraries to do some stuff in Visual Studio Code but I can
Lets say I have a small sparse matrix B. I want to build a bigger sparse matrix like BtB 0 0 (BtB)^-1 I want to know if Eigen provides some functio
So I need to re-write matrix right-handed division from Matlab to C++: At = (xPow*yPow')/(yPow*yPow'); I mocked some matrices: >> xPow*yPow' ans =
I'm trying to implement a functionality that can convert an Euler angle into an Quaternion and back "YXZ"-convention using Eigen. Later this should be used to l
I was installing the library Eigen on Ubuntu. I followed the instructions by creating a "build directory" alongside the downloaded source directory. That is, I
I have created a simple numpy array with shape (4, 2) called A. import numpy as np A = np.array([[1, 2], [2, 2], [3, 2],
To inialize for example Eigen::Matrix3i we can use syntax: Eigen::Matrix3i T; T << 1, 0, 0, 0, 2, 0, 0, 0, 3; However, when using clang-forma