KNOWN_BUGS_AND_PITFALLS.rst

Rationale:

This text collects common pitfalls and known bugs that may lead to incorrect computation of derivatives.

Pitfalls

1. Inplace Numpy Array Operations

   One has to be careful with the operations += , *=, ... of numpy arrays. The problem is best explained at an example:

   In [1]: import numpy
   
   In [2]: import adolc
   
   In [3]: x = adolc.adouble(numpy.array([1,2,3],dtype=float))
   
   In [4]: y = numpy.array([4,5,6],dtype=float)
   
   In [5]: x
   Out[5]: array([1(a), 2(a), 3(a)], dtype=object)
   
   In [6]: y
   Out[6]: array([ 4.,  5.,  6.])
   
   In [7]: x * y
   Out[7]: array([4(a), 10(a), 18(a)], dtype=object)
   
   In [8]: y *= x
   
   In [9]: y
   
   Out[9]: array([ 4.,  5.,  6.])

   That means that the inplace operation x *= y is not the same as x = x * y.

   This is not a bug of PYADOLC but a design choice in numpy's implementation of the augmented assignment statements *=, etc. for arrays of objects.

   Numpy tries to cast the dtype of y to the dtype x. If x has dtype float then on each element y[i].__float__() is called.