[SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with k=1
Sergio Rojas
sergio_r@mail....
Wed May 23 12:47:18 CDT 2012
I do not have an answer to your question Alejandro, but I am curious about why the timing
changes each time the code is executed. Is there any random allocation in the process of solving
your problem?
Sergio
---------------------------------------------------------------------- Message: 1 Date: Wed, 23 May 2012 09:33:15 -0600 From: Alejandro Weinstein <alejandro.weinstein@gmail.com> Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with k=1 To: scipy-user@scipy.org Message-ID: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA@mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 Hi: I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with 1144 nonzero entries. I only need the eigenvector corresponding to the larger eigenvalue, so I was running the function with k=1. However, I found that it is about 10 times faster to call the function with k=8. I am testing this with the following code (available here: https://gist.github.com/2775892): ############################################################# import timeit setup = """ import numpy as np import scipy.sparse import scipy.io from scipy.sparse.linalg import eigs P = scipy.io.mmread('P.mtx') """ n = 10 for k in range(1,20): code = 'eigs(P, k=%d)' % k t = timeit.timeit(stmt=code, setup=setup, number=n) / n print 'k: %2d, time: %5.1f ms' % (k, 1000*t) ############################################################# The output is k: 1, time: 301.7 ms k: 2, time: 242.6 ms k: 3, time: 352.0 ms k: 4, time: 168.8 ms k: 5, time: 148.1 ms k: 6, time: 93.2 ms k: 7, time: 70.0 ms k: 8, time: 29.3 ms k: 9, time: 45.2 ms k: 10, time: 63.0 ms k: 11, time: 209.1 ms k: 12, time: 170.8 ms k: 13, time: 120.2 ms k: 14, time: 104.6 ms k: 15, time: 115.0 ms k: 16, time: 97.0 ms k: 17, time: 94.0 ms k: 18, time: 94.4 ms k: 19, time: 74.3 ms Is this behavior typical for eigs? In other words, should I always use k set to values around 6 or 8, or is it matrix dependent? I am also curious about this. I would expect that computing 1 eigenvector should never be slower than computing more eigenvectors. Any about why is this happening? Alejandro. ------------------------------
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