2008/8/25 Daniel Lenski <[EMAIL PROTECTED]>: > On Mon, 25 Aug 2008 03:48:54 +0000, Daniel Lenski wrote: >> * it's fast enough for 100,000 determinants, but it bogs due to >> all the temporary arrays when I try to do 1,000,000 determinants >> (=72 MB array) > > I've managed to reduce the memory usage significantly by getting the > number of temporary arrays down to exactly two: > > def det3(ar): > a=ar[...,0,0]; b=ar[...,0,1]; c=ar[...,0,2] > d=ar[...,1,0]; e=ar[...,1,1]; f=ar[...,1,2] > g=ar[...,2,0]; h=ar[...,2,1]; i=ar[...,2,2] > > t=a.copy(); t*=e; t*=i; tot =t > t=b.copy(); t*=f; t*=g; tot+=t > t=c.copy(); t*=d; t*=h; tot+=t > t=g.copy(); t*=e; t*=c; tot-=t > t=h.copy(); t*=f; t*=a; tot-=t > t=i.copy(); t*=d; t*=b; tot-=t > > return tot > > Now it runs very fast with 1,000,000 determinants to do (<10X the time > required to do 100,000) but I'm still worried about the stability with > real-world data.
As far as I know, that's the best you can do (though come to think of it, a 3D determinant is a cross-product followed by a dot product, so you might be able to cheat and use some built-in routines). It's a current limitation of numpy that there is not much support for doing many linear algebra problems. We do have perennial discussions about improving support for array-of-matrices calculations, and in fact currently in numpy SVN is code to allow C code doing a 3x3 determinant to be easily made into a "generalized ufunc" that does ufunc-like broadcasting and iteration over all but the last two axes. Anne _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
