DOC: Update documentation of np.linalg.det() #27599
Conversation
…ating-point precision This update modifies the documentation in _linalg.py for the np.linalg.det() function to clarify the effects of floating-point precision limitations when calculating the determinant of a matrix. It includes an explanation that small numerical errors (i.e. floating point rounding error) may occur, leading to very small non-zero values instead of exactly zero for singular matrices. Additionally, an example is provided using np.isclose() to check if the determinant is effectively zero.
|
I think it is helpful to raise awareness of numerical precision by adding a paragraph to the documentation. For the particular case of First, it might not be a great choice to use Secondly, it is somewhat tricky to use |
|
close/reopen |
|
@amitsubhashchejara the suggestion by @rkern #27554 (comment) was my first thought, too. There are many other functions for which similar notes would be equally appropriate. When we are faced with the decision of whether we want to add to all of them or just add a general section in a tutorial, the tutorial is much more maintainable. I also agree that the absolute beginners tutorial would be the right place - is there a section there where this would fit (if it's not mentioned already)? |
|
See also #27871 |
|
I'm going to close this. The idea for a tutorial section on floating point precision would be good to have and |
This update modifies the documentation in _linalg.py for the np.linalg.det() function to clarify the effects of floating-point precision limitations when calculating the determinant of a matrix.
It includes an explanation that small numerical errors (i.e. floating point rounding error) may occur, leading to very small non-zero values instead of exactly zero for singular matrices. Additionally, an example is provided using np.isclose() to check if the determinant is effectively zero.