Hi all, Does PETSc has some automatic matrix sparsity detection algorithm available? Something like: https://urldefense.us/v3/__https://docs.sciml.ai/NonlinearSolve/stable/basics/sparsity_detection/__;!!G_uCfscf7eWS!ccEx6zmuNrVADqtN50hO2N0k4Qs-A70nztAjMLu-JElnjhK5w84BpYC8CAINd6KihSxaS2rx_LgpqUVM49U$
The background is that I use finite differencing plus matrix coloring to (efficiently) get the Jacobian. For the matrix coloring part, I color the matrix based on mesh connectivity and variable dependencies, which is not bad, but just try to be lazy to even eliminating this part. A related but different question, how much does PETSc support automatic differentiation? I see some old paper: https://ftp.mcs.anl.gov/pub/tech_reports/reports/P922.pdf and discussion in the roadmap: https://urldefense.us/v3/__https://petsc.org/release/community/roadmap/__;!!G_uCfscf7eWS!ccEx6zmuNrVADqtN50hO2N0k4Qs-A70nztAjMLu-JElnjhK5w84BpYC8CAINd6KihSxaS2rx_Lgpw6v6hKE$ I am thinking that if AD works so I don’t even need to do finite differencing Jacobian, or have it as another option. Best, -Ling
