I typed the following into the python console:
>>>from numpy import float64
>>>x=float64(1.98682855148322934369)
>>>x
np.float64(1.9868285514832293)
>>>y=float64(1)/x
>>>x*y
np.float64(0.9999999999999999)
My argument for why the above code should return 1.0 is as follows:
- The numbers follow IEEE double precision, meaning that its $\epsilon_{mach}$ (for round to nearest) is $2^{-53}$ (i.e. the minimum $\epsilon$ so that $fl(1+\epsilon)\ne1$)
- We know 1/x is in the range $(2^{-1},1)$, so that the maximum absolute error for fl(1/x) is $2^{-1}*2^{-53}=2^{-54}$
- Let $fl(1/x)=1/x+\epsilon$ where $\epsilon$ is the error with absolute value $<=2^{-54}$
- Then $x*fl(1/x)=1+x\epsilon$. Note that $x<2$, so $|x\epsilon|<2*2^{-54}=2^{-53}=\epsilon_{mach}$
- Therefore, $fl(x*fl(1/x))=1$
I checked my reasoning with deepseek r1 and it agreed with me. I also performed all sorts of hardware checks to make sure all intermediate steps were 64 bit precision and everything seemed to be fine. So this is perplexing. Why didn't python return 1 as the answer?