[Numpy-discussion] OpenOpt Suite release 0.45

[Dmitrey]

--- Исходное сообщение ---

> От кого: "Alan G Isaac" <alan.isaac at gmail.com>
Дата: 15 марта 2013, 20:38:38

On 3/15/2013 9:21 AM, Dmitrey wrote:
> Temporary walkaround for a serious bug in FuncDesigner automatic differentiation kernel due to a bug in some versions of Python or NumPy,


Are the suspected bugs documented somewhere?

the suspected bugs are not documented yet, I guess it will be fixed in
future versions of Python or numpy
the bug is hard to locate and isolate, it looks like this:

derivative_items = list(pointDerivative.items())

# temporary walkaround for a bug in Python or numpy
derivative_items.sort(key=lambda elem: elem[0])
######################################

for key, val in derivative_items:
indexes = oovarsIndDict[key]

# this line is not reached in the involved buggy case
if not involveSparse and isspmatrix(val): val = val.A

if r.ndim == 1:
r[indexes[0]:indexes[1]] = val.flatten() if type(val) == ndarray else val
else:
# this line is not reached in the involved buggy case
r[:, indexes[0]:indexes[1]] = val if val.shape == r.shape else
val.reshape((funcLen, prod(val.shape)/funcLen))

so, pointDerivative is Python dict of pairs (F_i, N_i), where F_i are
hashable objects, and even for the case when N_i are ordinary scalars
(they can be numpy arrays or scipy sparse matrices) results of this code
are different wrt was or was not derivative_items.sort() performed; total
number of nonzero elements is same for both cases. oovarsIndDict is dict
of pairs (F_i, (n_start_i, n_end_i)), and for the case N_i are all
scalars for all i n_end_i = n_start_i - 1.


 Alan
PS The word 'banausic' is very rare in English.
Perhaps you meant 'unsophisticated'?


google translate tells me "banausic" is more appropriate translation than
"unsophisticated" for the sense I meant (those frameworks are aimed on
modelling only numerical optimization problems, while FuncDesigner is
suitable for modelling of systems of linear, nonlinear, ordinary
differential equations, eigenvalue problems, interval analysis and much
more).
D.
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