Hi all: I'm going to post this question to a couple of lists as it does not strictly relate to spatial processing:
We are evaluating the SparseM and Matrix packages as we port MATLAB R14 scripts into R. The MATLAB code basically evaluates the AX=B system on sparse matrices that result in output matrices of 100 to 1,000,000 rows/columns. Our R prototype script uses Matrix() methods qr() and qr.coeff() and produces the same answer as the MATLAB code for small (60x60) problems. But, execution times are much longer (40 minutes, compared to 2 minutes for the MATLAB code) Also, the R version cannot accommodate a solutioj matrix greater than aprox 10,000 x 10,000 elements, while the MATLAB script has generated solutions for 10**6 x 10**6 solution matrices. Question is: Has anyone experiences in improving the performance of the R Matrix and SparseM packages? Also, has anyone explored (and returned alive from) the upper limits of R Matrix and SparseM problem sizes? Thanks in advance for any insights! Rick Reeves -- Rick Reeves Scientific Programmer Analyst National Center for Ecological Analysis and Synthesis (NCEAS) University of California, Santa Barbara [EMAIL PROTECTED] 805 892 2533 _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
