Hi, On Wed, 28 Jun 2006, Jon Wright wrote:
> > >>This strikes me as a little bit odd. Why not just provide the > >>best-performing > >>function to both SciPy and NumPy? Would NumPy be more difficult to install > >>if the SciPy algorithm for inv() was incorporated? > >> > >> > Having spent a few days recently trying out various different > eigenvector routines in Lapack I would have greatly appreciated having a > choice of which one to use which routine are you trying to use? > from without having to create my own > wrappers, compiling atlas and lapack under windows (ouch). I noted that > Numeric (24.2) seemed to be converting Float32 to double meaning my > problem no longer fits in memory, which was the motivation for the work. > Poking around in the svn of numpy.linalg appears to find the same lapack > routine as Numeric (dsyevd). Perhaps I miss something in the code logic? if you can convince the code to get ssyevd instead of dsyevd it might do what you want> > The divide and conquer (*evd) uses more memory than the (*ev), as well > as a factor of 2 for float/double, hence my problem, and the reason why > "best performing" is a hard choice. I thought matlab has a look at the > matrix dimensions and problem before deciding what to do (eg: the \ > operator). Hmm, this is a hard choice, which might better left in the hands of the knowledgeable user. (e.g., aren't the divide and conquer routines substantially faster?) Best, Arnd Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
