<html><head></head><body style="zoom: 0%;"><div dir="auto">I just use the standard eigs function (<a href="https://www.mathworks.com/help/matlab/ref/eigs.html">https://www.mathworks.com/help/matlab/ref/eigs.html</a>) as a black box. I think it uses a lanczos type method under the hood.<br><br></div>
<div dir="auto">Nidish</div>
<div class="gmail_quote" >On Aug 15, 2020, at 21:42, Barry Smith <<a href="mailto:bsmith@petsc.dev" target="_blank">bsmith@petsc.dev</a>> wrote:<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<pre class="blue"><br> Exactly what algorithm are you using in Matlab to get the 10 smallest eigenvalues and their corresponding eigenvectors? <br><br> Barry<br><br><br><blockquote class="gmail_quote" style="margin: 0pt 0pt 1ex 0.8ex; border-left: 1px solid #729fcf; padding-left: 1ex;"> On Aug 15, 2020, at 8:53 PM, Nidish <nb25@rice.edu> wrote:<br> <br> The section on solving singular systems in the manual starts with assuming that the singular eigenvectors are already known.<br> <br> I have a large system where finding the singular eigenvectors is not trivially written down. How would you recommend I proceed with making initial estimates? In MATLAB (with MUCH smaller matrices), I conduct an eigensolve for the first 10 smallest eigenvalues and take the eigenvectors corresponding to the zero eigenvalues from this. This approach doesn't work here since I'm unable to use SLEPc for solving<br> <br> K.v = lam*M.v<br> <br> for cases where K is positive semi-definite (contains a few "rigid body modes") and M is strictly positive definite.<br> <br> I'd appreciate any assistance you may provide with this.<br> <br> Thank you,<br> Nidish<br></blockquote><br></pre></blockquote></div></body></html>