On Wed, Mar 2, 2011 at 10:21 AM, geremy condra <debat...@gmail.com> wrote: > 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 >
Apologies, forgot the links: http://www.sagemath.org/doc/constructions/linear_algebra.html http://www.sagemath.org/doc/reference/sage/rings/complex_field.html Geremy Condra -- http://mail.python.org/mailman/listinfo/python-list
