Tutorial page 1 - The Matrix class

We assume that you have already read the quick "getting started" tutorial. This page is the first one in a much longer multi-page tutorial.

Table of contents

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. The three remaining parameters have default values, which for now we will leave untouched, and which we discuss below.

The three mandatory template parameters of Matrix are: 

Matrix<typename Scalar, int RowsAtCompileTime, int ColsAtCompileTime>

We offer a lot of convenience typedefs to cover the usual cases. For example, Matrix4f is a 4x4 matrix of floats. Here is how it is defined by Eigen: 

typedef Matrix<float, 4, 4> Matrix4f;

We discuss below these convenience typedefs.

Vectors

As mentioned above, in Eigen, vectors are just a special case of matrices, with either 1 row or 1 column. The case where they have 1 column is the most common; such vectors are called column-vectors, often abbreviated as just vectors. In the other case where they have 1 row, they are called row-vectors.

For example, the convenience typedef Vector3f is a (column) vector of 3 floats. It is defined as follows by Eigen: 

typedef Matrix<float, 3, 1> Vector3f;

We also offer convenience typedefs for row-vectors, for example: 

typedef Matrix<int, 1, 2> RowVector2i;

The special value Dynamic

Of course, Eigen is not limited to matrices whose dimensions are known at compile time. The RowsAtCompileTime and ColsAtCompileTime template parameters can take the special value Dynamic which indicates that the size is unknown at compile time, so must be handled as a run-time variable. In Eigen terminology, such a size is referred to as a dynamic size; while a size that is known at compile time is called a fixed size. For example, the convenience typedef MatrixXd, meaning a matrix of doubles with dynamic size, is defined as follows: 

typedef Matrix<double, Dynamic, Dynamic> MatrixXd;

And similarly, we define a self-explanatory typedef VectorXi as follows: 

typedef Matrix<int, Dynamic, 1> VectorXi;

You can perfectly have e.g. a fixed number of rows with a dynamic number of columns, as in: 

Matrix<float, 3, Dynamic>

Constructors

A default constructor is always available, never performs any dynamic memory allocation, and never initializes the matrix coefficients. You can do: 

Matrix3f a;
MatrixXf b;

Here,

Constructors taking sizes are also available. For matrices, the number of rows is always passed first. For vectors, just pass the vector size. They allocate the array of coefficients with the given size, but don't initialize the coefficients themselves: 

MatrixXf a(10,15);
VectorXf b(30);

Here,

In order to offer a uniform API across fixed-size and dynamic-size matrices, it is legal to use these constructors on fixed-size matrices, even if passing the sizes is useless in this case. So this is legal: 

Matrix3f a(3,3);

and is a no-operation.

Finally, we also offer some constructors to initialize the coefficients of small fixed-size vectors up to size 4: 

Vector2d a(5.0, 6.0);
Vector3d b(5.0, 6.0, 7.0);
Vector4d c(5.0, 6.0, 7.0, 8.0);

Coefficient accessors

The primary coefficient accessors and mutators in Eigen are the overloaded parenthesis operators. For matrices, the row index is always passed first. For vectors, just pass one index. The numbering starts at 0. This example is self-explanatory:

Example:Output: 
#include <iostream>
#include <Eigen/Dense>

using namespace Eigen;

int main()
{
  MatrixXd m(2,2);
  m(0,0) = 3;
  m(1,0) = 2.5;
  m(0,1) = -1;
  m(1,1) = m(1,0) + m(0,1);
  std::cout << "Here is the matrix m:\n" << m << std::endl;
  VectorXd v(2);
  v(0) = 4;
  v(1) = v(0) - 1;
  std::cout << "Here is the vector v:\n" << v << std::endl;
}

 




Note that the syntax  m(index)  is not restricted to vectors, it is also available for general matrices, meaning index-based access in the array of coefficients. This however depends on the matrix's storage order. All Eigen matrices default to column-major storage order, but this can be changed to row-major, see Storage orders.


The operator[] is also overloaded for index-based access in vectors, but keep in mind that C++ doesn't allow operator[] to take more than one argument. We restrict operator[] to vectors, because an awkwardness in the C++ language would make matrix[i,j] compile to the same thing as matrix[j] !



Comma-initialization


Matrix and vector coefficients can be conveniently set using the so-called comma-initializer syntax. For now, it is enough to know this example:





Example:Output: 




Matrix3f m;
m << 1, 2, 3,
     4, 5, 6,
     7, 8, 9;
std::cout << m;

  
  




The right-hand side can also contain matrix expressions as discussed in this page.



Resizing


The current size of a matrix can be retrieved by rows(), cols()  and size(). These methods return the number of rows, the number of columns and the number of coefficients, respectively. Resizing a dynamic-size matrix is done by the resize()  method.





Example:Output: 




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

using namespace Eigen;

int main()
{
  MatrixXd m(2,5);
  m.resize(4,3);
  std::cout << "The matrix m is of size "
            << m.rows() << "x" << m.cols() << std::endl;
  std::cout << "It has " << m.size() << " coefficients" << std::endl;
  VectorXd v(2);
  v.resize(5);
  std::cout << "The vector v is of size " << v.size() << std::endl;
  std::cout << "As a matrix, v is of size "
            << v.rows() << "x" << v.cols() << std::endl;
}

  
  




The resize() method is a no-operation if the actual matrix size doesn't change; otherwise it is destructive: the values of the coefficients may change. If you want a conservative variant of resize() which does not change the coefficients, use conservativeResize(), see this page for more details.


All these methods are still available on fixed-size matrices, for the sake of API uniformity. Of course, you can't actually resize a fixed-size matrix. Trying to change a fixed size to an actually different value will trigger an assertion failure; but the following code is legal:





Example:Output: 




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

using namespace Eigen;

int main()
{
  Matrix4d m;
  m.resize(4,4); // no operation
  std::cout << "The matrix m is of size "
            << m.rows() << "x" << m.cols() << std::endl;
}

  
  





Assignment and resizing


Assignment is the action of copying a matrix into another, using operator=. Eigen resizes the matrix on the left-hand side automatically so that it matches the size of the matrix on the right-hand size. For example:





Example:Output: 




MatrixXf a(2,2);
std::cout << "a is of size " << a.rows() << "x" << a.cols() << std::endl;
MatrixXf b(3,3);
a = b;
std::cout << "a is now of size " << a.rows() << "x" << a.cols() << std::endl;

  
  




Of course, if the left-hand side is of fixed size, resizing it is not allowed.


If you do not want this automatic resizing to happen (for example for debugging purposes), you can disable it, see this page.



Fixed vs. Dynamic size


When should one use fixed sizes (e.g. Matrix4f), and when should one prefer dynamic sizes (e.g. MatrixXf)? The simple answer is: use fixed sizes for very small sizes where you can, and use dynamic sizes for larger sizes or where you have to. For small sizes, especially for sizes smaller than (roughly) 16, using fixed sizes is hugely beneficial to performance, as it allows Eigen to avoid dynamic memory allocation and to unroll loops. Internally, a fixed-size Eigen matrix is just a plain static array, i.e. doing 


 Matrix4f mymatrix; 

 really amounts to just doing 


 float mymatrix[16]; 

 so this really has zero runtime cost. By contrast, the array of a dynamic-size matrix is always allocated on the heap, so doing 


 MatrixXf mymatrix(rows,columns); 

 amounts to doing 


 float *mymatrix = new float[rows*columns]; 

 and in addition to that, the MatrixXf object stores its number of rows and columns as member variables.


The limitation of using fixed sizes, of course, is that this is only possible when you know the sizes at compile time. Also, for large enough sizes, say for sizes greater than (roughly) 32, the performance benefit of using fixed sizes becomes negligible. Worse, trying to create a very large matrix using fixed sizes could result in a stack overflow, since Eigen will try to allocate the array as a static array, which by default goes on the stack. Finally, depending on circumstances, Eigen can also be more aggressive trying to vectorize (use SIMD instructions) when dynamic sizes are used, see Vectorization.



Optional template parameters


We mentioned at the beginning of this page that the Matrix class takes six template parameters, but so far we only discussed the first three. The remaining three parameters are optional. Here is the complete list of template parameters: 


Matrix<typename Scalar,
       int RowsAtCompileTime,
       int ColsAtCompileTime,
       int Options = 0,
       int MaxRowsAtCompileTime = RowsAtCompileTime,
       int MaxColsAtCompileTime = ColsAtCompileTime>

 


Convenience typedefs


Eigen defines the following Matrix typedefs: 



Where: 










libicr

Author(s): Robert Krug

autogenerated on Mon Jan 6 2014 11:34:07