See this page .
Don't miss this page on aliasing, especially if you got wrong results in statements where the destination appears on the right hand side of the expression.
Eigen does explicit vectorization, and while that is appreciated by many users, that also leads to some issues in special situations where data alignment is compromised. Indeed, prior to C++17, C++ does not have quite good enough support for explicit data alignment. In that case your program hits an assertion failure (that is, a "controlled crash") with a message that tells you to consult this page:
Have a look at it and see for yourself if that's something that you can cope with. It contains detailed information about how to deal with each known cause for that issue.
Now what if you don't care about vectorization and so don't want to be annoyed with these alignment issues? Then read how to get rid of them .
In short: do not use the auto keywords with Eigen's expressions, unless you are 100% sure about what you are doing. In particular, do not use the auto keyword as a replacement for a Matrix<>
type. Here is an example:
In this example, the type of C is not a MatrixXd
but an abstract expression representing a matrix product and storing references to A
and B
. Therefore, the product of A*B
will be carried out multiple times, once per iteration of the for loop. Moreover, if the coefficients of A
or B
change during the iteration, then C
will evaluate to different values as in the following example:
for which we end up with R1
≠ R2
.
Here is another example leading to a segfault:
The problem is that eval()
returns a temporary object (in this case a MatrixXd
) which is then referenced by the Transpose<>
expression. However, this temporary is deleted right after the first line, and then the C
expression references a dead object. One possible fix consists in applying eval()
on the whole expression:
The same issue might occur when sub expressions are automatically evaluated by Eigen as in the following example:
Here the normalized()
method has to evaluate the expensive product A*v
to avoid evaluating it twice. Again, one possible fix is to call .eval() on the whole expression:
In this case, C
will be a regular VectorXd
object. Note that DenseBase::eval() is smart enough to avoid copies when the underlying expression is already a plain Matrix<>
.
With all libraries, one must check the documentation for which header to include. The same is true with Eigen, but slightly worse: with Eigen, a method in a class may require an additional #include
over what the class itself requires! For example, if you want to use the cross()
method on a vector (it computes a cross-product) then you need to:
We try to always document this, but do tell us if we forgot an occurrence.
In short: avoid the use of the ternary operator (COND ? THEN : ELSE)
with Eigen's expressions for the THEN
and ELSE
statements. To see why, let's consider the following example:
This example will return B = 3, 2, 1
. Do you see why? The reason is that in c++ the type of the ELSE
statement is inferred from the type of the THEN
expression such that both match. Since THEN
is a Reverse<Vector3f>
, the ELSE
statement A is converted to a Reverse<Vector3f>
, and the compiler thus generates:
In this very particular case, a workaround would be to call A.reverse().eval() for the THEN
statement, but the safest and fastest is really to avoid this ternary operator with Eigen's expressions and use a if/else construct.
If you don't know why passing-by-value is wrong with Eigen, read this page first.
While you may be extremely careful and use care to make sure that all of your code that explicitly uses Eigen types is pass-by-reference you have to watch out for templates which define the argument types at compile time.
If a template has a function that takes arguments pass-by-value, and the relevant template parameter ends up being an Eigen type, then you will of course have the same alignment problems that you would in an explicitly defined function passing Eigen types by reference.
Using Eigen types with other third party libraries or even the STL can present the same problem. boost::bind
for example uses pass-by-value to store arguments in the returned functor. This will of course be a problem.
There are at least two ways around this:
The current behaviour of using Matrix
with boolean coefficients is inconsistent and likely to change in future versions of Eigen, so please use it carefully!
A simple example for such an inconsistency is
since calling foo<3>()
prints the zero matrix while calling foo<10>()
prints the identity matrix.