On Wed, Mar 2, 2011 at 6:42 AM, Ben123 <ben.is.loca...@gmail.com> wrote: > Hello. I have a written Python program which currently uses numpy to > perform linear algebra operations. Specifically, I do matrix*matrix, > matrix*vector, numpy.linalg.inv(matrix), and linalg.eig(matrix) > operations. Now I am interested in allowing arbitrary precision. I > have tried gmpy, bigfloat, mpmath, and decimal but I have been unable > to easily implement any with my current program. I suspect I have to > change some commands but I am unsure what. > > My question is which of the arbitrary precision implementations will > most easily handle linear algebra? I don't care about speed, just ease > of use. Online tutorials for arbitrary precision linear algebra > operations would be useful. > > For example, it looks like mpmath can handle matrix operations > http://fredrik-j.blogspot.com/search?q=matrix > but I was unable to find a clear tutorial. The tutorials for most of > the arbitrary precision implementations demonstrate simple scalar > examples. > > Thanks in advance
Have you looked at Sage[0]? I don't know for a fact, but you should be able to define a matrix over RealField(precision_in_bits) and then take the eigenvalue of it. I don't know if it will actually produce the precision you need though. Geremy Condra -- http://mail.python.org/mailman/listinfo/python-list
