Getting Started with Eigen

Installing Eigen

You can download Eigen from its official website. 

The source code for the Eigen library will be downloaded using Git or extracted from the zip file into the user's home directory. Of course, you can use whatever directory you choose; simply change the tilde ~ in the following commands to the download path you like.

$ cd ~ 
$ git clone https :// github .com/ eigenteam /eigen -git - mirror

We don't need to create anything here, so if you're nervous about building libraries from the source like I am, don't be. All of Eigen's source code is contained in header files because it employs pure header libraries. 

Now open Visual Studio. If you have not installed Visual Studio on your system, we recommend installing it.

You’ll probably see this screen when you open Visual Studio.

Now right-click on the project, and you’ll see the following sections.

Now click on the properties. The following dialogue box will appear.

Make sure the path of the additional file that you cloned/extracted is in the additional include directories section. Click on Ok if the path is correct; otherwise, mention the path of cloned/extracted files.

The function and class definitions are all contained in .H files; there are no separate .CPP files for these purposes. Because of this, using the Eigen library is very easy. Simply #include these header files at the start of your own code to use it.

Now that you had successfully installed Eigen in your system, you are up for getting started with Eigen library.

Writing First Program

Following code given below is your first program which will help you getting started with Eigen library.

#include <iostream>
#include <Eigen/Dense>

using Eigen::MatrixXd;
using namespace std;

int main()
{
	 MatrixXd m(2,2);
		 m(0,0) = 4;	
		 m(1,0) = 6;
		 m(0,1) = 5;
		 m(1,1) = m(1,0) + m(0,1);
	 cout << m << endl;
}

You can also try this code with Online C++ Compiler

Run Code

Output:

Explanation:

We use "using namespace Eigen" to simplify the syntax and "#include<Eigen/Dense>" to include the Eigen library header files. If you don't, you'll need to use "Eigen::" in front of each command. The MatrixXd represents the matrix of arbitrary size in which every entry is of double type.

Try and compile by yourself with the help of online C++ Compiler for better understanding.

Matrices and Vectors

Now, let’s have a look at an example where matrices are combined with vectors when we set the size at run time. 

#include <iostream>
#include <Eigen/Dense>
using namespace std;

using Eigen::MatrixXd;
using Eigen::VectorXd;

int main()
{
	 MatrixXd m = MatrixXd::Random(3,3);
       m = (m + MatrixXd::Constant(3,3,2.2)) * 40;
	 cout << "m =" << endl << m << endl;
	   VectorXd v(3);
	   v << 1, 2, 3;
	 cout << "m * v =" << endl << m * v << endl;
}

You can also try this code with Online C++ Compiler

Run Code

Output:

Explanation:

The example begins by creating a 3-by-3 matrix m, which is initialised with random values between -1 and 1 using the Random() method. The values are mapped linearly to fall between 20 and 120 in the following line. The MatrixXd::Constant was used. A 3-by-3 matrix expression with all coefficients equal to 2.2 is produced using the function constant(3,3,2.2). The rest is simple math.

Also see, Application of Oops

Real-Life Use of Eigen

After getting started with Eigen library, you should know some real-life use of Eigen too. The following programs use the Eigen library in their production-level code:

• Tensorflow: One of the most widely used machine learning frameworks.
• Matplotlib: It is used to plot 2D charts.
• Koffice2: The KDE office program.

Check out most important Git Interview Questions here.

Frequently Asked Questions

What are the different types of libraries in C++?

Static libraries and dynamic libraries are the two types of libraries.

Is Eigen open source?

Since version 3.1, Eigen has been open-source software covered by Mozilla Public License 2.0. The GNU Lesser General Public License applied to earlier iterations. Version 1.0 became available in December 2006.

Which is better, Eigen or BLAS/LAPACK?

BLAS/LAPACK covers only a few topics that Eigen does. Fixed-size matrices and vectors, which are quite popular, are handled by Eigen. Eigen supports sparse matrices and vectors by default. Also, the Eigen library offers numerous practical capabilities that are also quite popular (see the Array module, Geometry module, etc.).

Does Eigen library employ CUDA?

Eigen library is now been used inside CUDA kernels since version 3.3.

Does Eigen make use of OpenMP?

Eigen utilises the number of threads supplied by OpenMP unless setNbThreads is called.

Conclusion

This article briefly discussed getting started with eigen library. We discussed the Eigen library, its installation and its implementation. We hope this blog has helped you getting started with Eigen. You can also refer to our blogs, What is Matrix base and how to inherit from a matrix and Eigen sparse matrix example, to have a better understanding of matrix operations. 

If you like to learn more, you can check out our articles: 

• How to use Eigen’s array class
• What are the different matrix operations?
• Eigen Geometry
• Assertions and Multi-threading in Eigen
• Introduction to Sparse Matrix
   

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