I have been working on some linear algebra algorithms and have hit a situation that I don't know if it has been addressed by people before. Any thoughts would be appreciated.
In my system I have a base object TensorProps with shapes, ranks, and the usual + - * / overloaded with the __foo__ routines. Currently the __div__ and __rdiv__ routines will check to see if an expr has an inverse and if not throw an error. For example, given a matrix A with .has_inverse=True then 1/A is legal, but given vector x then 1/x is not (since there is never an inverse of rank 1 objects). Under this system A*x will not have an inverse but doing 1/(A*x) will not throw an error, Expr.__rdiv__ gets called not TensorProps.__rdiv__. Right now the only way I see to rectify this is to create my own TensorMul operators and limit my use to objects inheriting TensorProps, since even with a TensorMul 1/(a*TensorMul(A, x)) would still be legal. Does anyone have any thoughts on how to make the Expr.__rdiv__ check its arguments a bit better? We could have the operators call the users specified Mul, Add, and Pow, but this would probably have a significant impact in performance. (In fact a friend shared a code that does something similar in Mathematica, it checks registered patterns and then calls the appropriate matching function.) -- Andy -- You received this message because you are subscribed to the Google Groups "sympy" group. To post to this group, send email to sy...@googlegroups.com. To unsubscribe from this group, send email to sympy+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
