Timeline for Diverging floating point calculation
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Sep 30 at 10:26 | comment | added | DrJay | Python uses double-precision floating-point numbers (based on the IEEE 754 standard) so, rather than use the above simplistic example, try (part) def computation(f): large_value = 1e+16 small_value = 1e-10 f += small_value f -= large_value # Subtract a large value f += large_value # Add back the large value return f f = 1. There are plenty of examples of floating-point errors, which is why it is still taught. Being aware of the pitfalls helps look for ways to prevent errors. | |
| Sep 29 at 20:59 | comment | added | gnasher729 | You say “drifting occurs”. Does it really? | |
| Sep 28 at 12:26 | comment | added | emonigma | Thank you! I ran the code with 40 digits of precision and do not find a divergence nor a discrepancy. | |
| Sep 27 at 10:45 | history | answered | DrJay | CC BY-SA 4.0 |