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- Eigen - tuxfamily. org
minieigen is small boost::python wrapper for Eigen's core classes (dense fixed and dynamic-sized vectors, matrices, aligned boxes, quaternions; with integer, floating and complex scalars) including operators and subset of Eigen's API methods
- Eigen: Getting started - tuxfamily. org
This is a very short guide on how to get started with Eigen It has a dual purpose It serves as a minimal introduction to the Eigen library for people who want to start coding as soon as possible
- Eigen: Main Page - tuxfamily. org
They are themselves composed of user manual pages describing the different features in a comprehensive way, and reference pages that gives you access to the API documentation through the related Eigen's modules and classes
- Eigen: Quick reference guide - tuxfamily. org
The Eigen library is divided in a Core module and several additional modules Each module has a corresponding header file which has to be included in order to use the module The Dense and Eigen header files are provided to conveniently gain access to several modules at once
- Eigen: The Matrix class - tuxfamily. org
In Eigen, all matrices and vectors are objects of the Matrix template class Vectors are just a special case of matrices, with either 1 row or 1 column The first three template parameters of Matrix The Matrix class takes six template parameters, but for now it's enough to learn about the first three first parameters
- Eigen: Matrix and vector arithmetic - tuxfamily. org
Eigen offers matrix vector arithmetic operations either through overloads of common C++ arithmetic operators such as +, -, *, or through special methods such as dot(), cross(), etc For the Matrix class (matrices and vectors), operators are only overloaded to support linear-algebraic operations
- 3. 4 - Eigen - tuxfamily. org
Eigen 3 4 was released on August 18 2021 It can be downloaded from the Download section on the Main Page or from Gitlab Notice: that 3 4 x will be the last major release series of Eigen that will support c++03
- Eigen: Linear algebra and decompositions - tuxfamily. org
Eigen provides two implementations The recommended one is the BDCSVD class, which scales well for large problems and automatically falls back to the JacobiSVD class for smaller problems For both classes, their solve() method solved the linear system in the least-squares sense
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