A robust largescale index-1 DAE solver has been developed in Maple.
Details about our approach are provided in a paper just submitted.
See arxiv at
We encourage everyone to test these codes and report bugs.
Specific ways to contribute include
(1) Running the code in evalhf or compiled form (compiling some of the steps in single-step helps in older versions of Maple, not in 2022). Statseq helps (for smaller systems) to create procedures and possibly compile, but it bombs out for large systems.
(2) Providing options to run other parallel open-source linear solvers (eg., MUMPS). All the examples can be run with Intel's Pardiso (provided users have access to libraries) by calling DAESolverP.txt and by calling "IMPDAEP" instead of "IMPDAE". This is useful for large-scale problems.
(3) Reducing CPU time or RAM/memory usage significantly. In particular, do this for examples 5 and 6.
(4) Please avoid ~,*, etc (shortcuts) unless it improves the speed of calculation.
(5) Other examples that show the use of the developed solver. We are able to solve > 100,000 DAEs.
(6) The symbolic capability and ListTools search capability of Maple are very good and can be used for developing optimization solvers as well.
(7) Helping in converting the code to Maple 14 or earlier (by doing sparse LU Decomposition. Just using LinearSolve will slow down the code).
Dr. Venkat Subramanian