+1 to that Often, one is only interested in the largest or smallest eigenvalues/vectors of a problem. Then the method of choice are iterative solvers, e.g. Lanczos algorithm. If only the largest eigenvalue/vector is needed, you could try the power iteration.
On Wed, Apr 29, 2009 at 7:49 AM, Zhenxin Zhan <andyjian430...@gmail.com> wrote: > Thanks. My mistake. > > The os is 32-bit. I am doing a network-simulation for my teacher. The > average degree of the network topology is about 6.0. So I think it is > sparse. > > The paper needs the eigen values and the eigen vectors which are necessary > for the further simulation. I use the following procedure: > > 1. read the network vertices information from a txt file to a 10,000*10,000 > list 'lists'. > 2. And then use numpy.array(lits, dtype=float) to get a array object 'A' > 3. Finally, use numpy.linalg.eig(A) to get the eigen values and eigen > vectors. > 4. Using 'tofile' function to write them to local file. > > I will refer to scipy. > > Thanks so much. > > > > > 2009-04-29 > ________________________________ > Zhenxin Zhan > ________________________________ > 发件人： Charles R Harris > 发送时间： 2009-04-29 00:36:03 > 收件人： Discussion of Numerical Python > 抄送： > 主题： Re: [Numpy-discussion] MemoryError for computing eigen-vector on > 10,000*10, 000 matrix > > 2009/4/28 Zhenxin Zhan <andyjian430...@gmail.com> >> >> Thanks for your reply. >> >> My os is Windows XP SP3. I tried to use array(ojb, dtype=float), but it >> didn't work. And I tried 'float32' as you told me. And here is the error >> message: >> >> >> File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 791, in >> eig >> a, t, result_t = _convertarray(a) # convert to double or cdouble type >> >> File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 727, in >> _con >> vertarray >> a = _fastCT(a.astype(t)) >> MemoryError >> > > Looks like only a double routine is available for eig. Eigh is better for > symmetric routines and if you only want the eigenvalues and not the > eigenvectors then you should use eigvals or eigvalsh and save the space > devoted to the eigenvectors, which in themselves will put you over the > memory limit. > > The os question is whether or not you are running a 64 bit or 32 bit os. A > 64 bit os could use swap, although the routine would take forever to finish. > Really, you don't have enough memory for a problem that size. Perhaps if you > tell us what you want to achieve we can suggest a better approach. Also, if > your matrix is sparse other algorithms might be more appropriate. > > Chuck > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
