Skip functions or derivatives in cse

[Jason Moore]

Hi,

I tried:

In [18]: import sympy as sm

In [19]: a, b, c = sm.symbols('a, b, c')

In [20]: f = sm.Function('f')(a)

In [21]: expr = f + f.diff() - f.diff()/(f + f.diff()) - a*b + (a*b)**2

In [22]: sm.cse(expr)
Out[22]:
([(x0, f(a)), (x1, Derivative(x0, a)), (x2, x0 + x1)],
 [a**2*b**2 - a*b - x1/x2 + x2])

In [23]: sm.cse(expr, ignore=[f.diff()])
Out[23]:
([(x0, f(a)), (x1, Derivative(x0, a)), (x2, x0 + x1)],
 [a**2*b**2 - a*b - x1/x2 + x2])

The outcome I desire is:

([(x0, f(a)), (x1, Derivative(f(a), a)), (x2, x0 + x1)],
 [a**2*b**2 - a*b - x1/x2 + x2])

or:

([(x1, Derivative(f(a), a)), (x2, f(a) + x1)],
 [a**2*b**2 - a*b - x1/x2 + x2])

that is, that the functions or derivatives are treated like symbols.

The ignore flag is cse does not seem to do that.

Is there a way to use the pre/post processors in cse to do this?

Jason

moorepants.info
+01 530-601-9791

[Aaron Meurer]

It might be possible to work around this with the optimizations flag,
but I'd say cse should just be updated to treat derivatives as
separate symbols.

Aaron Meurer

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[Björn Dahlgren]

On Wednesday, 30 July 2025 at 19:55:29 UTC+2 asme...@gmail.com wrote:

It might be possible to work around this with the optimizations flag,
but I'd say cse should just be updated to treat derivatives as
separate symbols.

I agree that this would be preferable. In the mean time I typically find all Derivative and AppliedUndef instances, create dummies for them and substitute them before cse (substituting the derivatives first), and then do a final back-substitution. This is probably what we would do internally.