Eigen's use of expression templates results in potentially every expression being of a different type. If you pass such an expression to a function taking a parameter of type Matrix, your expression will implicitly be evaluated into a temporary Matrix, which will then be passed to the function. This means that you lose the benefit of expression templates. Concretely, this has two drawbacks:
Fortunately, all this myriad of expression types have in common that they all inherit a few common, templated base classes. By letting your function take templated parameters of these base types, you can let them play nicely with Eigen's expression templates.
This section will provide simple examples for different types of objects Eigen is offering. Before starting with the actual examples, we need to recapitulate which base objects we can work with (see also The class hierarchy).
ArrayBase. It can be used in functions that are meant to work on both matrices and arrays.
Prints the dimensions of the most generic object present in Eigen. It could be any matrix expressions, any dense or sparse matrix and any array.
using namespace Eigen;
template <typename Derived>
void print_size(const EigenBase<Derived>& b)
<< ", " << b.cols() << ")" << std::endl;
// v.asDiagonal() returns a 3x3 diagonal matrix pseudo-expression
size (rows, cols): 3 (3, 1) size (rows, cols): 9 (3, 3)
Prints a sub-block of the dense expression. Accepts any dense matrix or array expression, but no sparse objects and no special matrix classes such as DiagonalMatrix.
Prints the maximum coefficient of the array or array-expression.
Prints the inverse condition number of the given matrix or matrix-expression.
Multiple templated arguments example
Calculate the Euclidean distance between two points.
Notice that we used two template parameters, one per argument. This permits the function to handle inputs of different types, e.g.,
where the first argument
v1 is a vector and the second argument
2*v2 is an expression.
These examples are just intended to give the reader a first impression of how functions can be written which take a plain and constant Matrix or Array argument. They are also intended to give the reader an idea about the most common base classes being the optimal candidates for functions. In the next section we will look in more detail at an example and the different ways it can be implemented, while discussing each implementation's problems and advantages. For the discussion below, Matrix and Array as well as MatrixBase and ArrayBase can be exchanged and all arguments still hold.
In all the previous examples, the functions had to be template functions. This approach allows to write very generic code, but it is often desirable to write non templated function and still keep some level of genericity to avoid stupid copies of the arguments. The typical example is to write functions accepting both a MatrixXf or a block of a MatrixXf. This exactly the purpose of the Ref class. Here is a simple example:
using namespace Eigen;
using namespace std;
float inv_cond(const Ref<const MatrixXf>& a)
const VectorXf sing_vals = a.jacobiSvd().singularValues();
return sing_vals(sing_vals.size()-1) / sing_vals(0);
cout << "matrix m:" << endl << m << endl << endl;
cout << "inv_cond(m): " << inv_cond(m) << endl;
cout << "inv_cond(m(1:3,1:3)): " << inv_cond(m.topLeftCorner(3,3)) << endl;
cout << "inv_cond(m+I): " << inv_cond(m+Matrix4f::Identity()) << endl;
matrix m: 0.68 0.823 -0.444 -0.27 -0.211 -0.605 0.108 0.0268 0.566 -0.33 -0.0452 0.904 0.597 0.536 0.258 0.832 inv_cond(m): 0.0562343 inv_cond(m(1:3,1:3)): 0.0836819 inv_cond(m+I): 0.160203
In the first two calls to inv_cond, no copy occur because the memory layout of the arguments matches the memory layout accepted by Ref<MatrixXf>. However, in the last call, we have a generic expression that will be automatically evaluated into a temporary MatrixXf by the Ref<> object.
A Ref object can also be writable. Here is an example of a function computing the covariance matrix of two input matrices where each row is an observation:
and here are two examples calling cov without any copy:
The Ref<> class has two other optional template arguments allowing to control the kind of memory layout that can be accepted without any copy. See the class Ref documentation for the details.
Without using template functions, and without the Ref class, a naive implementation of the previous cov function might look like this
and contrary to what one might think at first, this implementation is fine unless you require a generic implementation that works with double matrices too and unless you do not care about temporary objects. Why is that the case? Where are temporaries involved? How can code as given below compile?
In this special case, the example is fine and will be working because both parameters are declared as const references. The compiler creates a temporary and evaluates the expression x+z into this temporary. Once the function is processed, the temporary is released and the result is assigned to C.
Here, we consider a slightly modified version of the function given above. This time, we do not want to return the result but pass an additional non-const paramter which allows us to store the result. A first naive implementation might look as follows.
When trying to execute the following code
the compiler will fail, because it is not possible to convert the expression returned by
MatrixXf::block() into a non-const
MatrixXf&. This is the case because the compiler wants to protect you from writing your result to a temporary object. In this special case this protection is not intended – we want to write to a temporary object. So how can we overcome this problem?
The solution which is preferred at the moment is based on a little hack. One needs to pass a const reference to the matrix and internally the constness needs to be cast away. The correct implementation for C98 compliant compilers would be
The implementation above does now not only work with temporary expressions but it also allows to use the function with matrices of arbitrary floating point scalar types.
One might think we are done now, right? This is not completely true because in order for our covariance function to be generically applicable, we want the follwing code to work
This is not the case anymore, when we are using an implementation taking MatrixBase as a parameter. In general, Eigen supports automatic resizing but it is not possible to do so on expressions. Why should resizing of a matrix Block be allowed? It is a reference to a sub-matrix and we definitely don't want to resize that. So how can we incorporate resizing if we cannot resize on MatrixBase? The solution is to resize the derived object as in this implementation.
This implementation is now working for parameters being expressions and for parameters being matrices and having the wrong size. Resizing the expressions does not do any harm in this case unless they actually require resizing. That means, passing an expression with the wrong dimensions will result in a run-time error (in debug mode only) while passing expressions of the correct size will just work fine.