  The basic framework for pPCx is  <A HREF="http://www.iems.nwu.edu/faculty/mehrotra.html">Mehrotra</A>'s predictor corrector interior point method for linear programming. Most of the work in an  interior point method lies in solving a symmetric sparse positive (semi-) definite system of linear equations: we use a new <A  HREF="http://www.cs.cornell.edu/Info/People/csun/psspd/index.html"> parallel multifrontal Cholesky factorization</A> developed by <A HREF="http://www.cs.cornell.edu/Info/People/csun/sun.html"> Chunguang Sun</A> that efficiently handles near-degeneracies. We also take care of dense rows and columns efficiently so that we can exploit sparsity in the normal equations as much as possible. The constraint matrix is stored in a distributed form, thus enabling the solution of very large problems that cannot be solved on a single processor.  </P>
