I want to get eigenvalue of (Symmetric) Matrix. Is there any source for Matrix Diagonalize? (QR method etc..) I want to see the source, please help me. Thanks.
C source must be out there; it might be fairly straightforward to port it to ASM. APL is an excellent language if you have a lot of work to do with vectors, matrices, and polynomials.
Download the Intel Math library at www.intel.com
I don't suppose anyone can send my that library, or post alternate link for it. I just don't want to have to register.
I looked for it with Google. Bad news: MKL50.EXE (Math Kernel Library) from Intel is 47.1 megs, and that's the library and documentation, with (I assume) no source.
You can find C++ sources at www.simtel.net. Do a search for "matrix library". Good luck! This message was edited by karim, on 6/11/2001 7:05:32 AM
Hey!! I'm working in a code to calculete eigenvalues and eigenvectors!! Maybe two weeks to finish. :( I'll post it here later.
Just curious... Are you working with finite elements? That matrix is a tensor? forget this if it doesn't matter...
Thank you for your reply. I use matrix, eigenvalue for solid state physics simulation.(Called D matrix) It is symmetric, real. And it represents physical value, so it is a tensor, of course. Thank you for your interest.
Go to http://www.ire.pw.edu.pl/numrcp/ and use your mouse to paste the code you want to a text file. You want part (or all) of Chap. 11 of "Numerical Recipes in C". I have the book, but didn't want to spend $50 for the code in computer-readable format. Fortunately, the above site (with the publisher's blessing) lets you download everything. I downloaded the entire pdf file (section by section), edited the file quite extensively, and built a Windows dll file. This was not quite trivial, took me a few days. The numerical algorithms are excellent, the C code less so (requires editing). Great Monte Carlo, FFT, multivariate minimization code, etc. If you *must* use assembler, just translate the algorithms from Numerical Recipes into assembler. There's little to gain from this, though -- good modern compilers generate quite efficient code.