Pierre, ex1.c is a toy test inherited from previous experimental pdipm. We simply sent centralised data to all other processes to test pdipm. It is not intended for performance. We should add more tests.
Current pdipm is not fully developed yet, especially its linear solver may fail to handle indefinite KKT matrix. We are working on it. We'll let you know after we get it updated. For your request about 'distributed Hessian with a Jacobian with a single row?', either someone else in petsc/tao team address this issue, or I'll check the details and get back to you later. Hong ________________________________ From: petsc-dev <petsc-dev-boun...@mcs.anl.gov> on behalf of Pierre Jolivet <pierre.joli...@enseeiht.fr> Sent: Monday, September 14, 2020 1:51 PM To: PETSc <petsc-dev@mcs.anl.gov> Subject: [petsc-dev] PDIPDM questions Hello, In my quest to help users migrate from Ipopt to Tao, I’ve a new question. When looking at src/tao/constrained/tutorials/ex1.c, it seems that almost everything is centralized on rank 0 (local sizes are 0 but on rank 0). I’d like to have my Hessian distributed more naturally, as in (almost?) all other SNES/TS examples, but still keep the Jacobian of my equality constraint, which is of dimension 1 x N (N >> 1), centralized on rank 0. Is this possible? If not, is it possible to supply the transpose of the Jacobian, of dimension N x 1, which could then be distributed row-wise like the Hessian? Or maybe use some trick to distribute a MatAIJ/MatDense of dimension 1 x N column-wise? Use a MatNest with as many blocks as processes? So, just to sum up, how can I have a distributed Hessian with a Jacobian with a single row? Thanks in advance for your help, Pierre
