I’m having a problem with using log10(x) to compress large numbers. When I convert a large number using math.log10(x) and then try to recover it with 10 ** log10(x), I always get a result that’s slightly different from the original number due to floating-point precision limits. Here’s a simple example in Python: import math original = 1180591620810475438080 log_value = math.log10(original) recovered = 10 ** log_value print(f"Original: {original}") print(f"log10(x): {log_value}") print(f"Recovered: {recovered}") print(f"Difference: {abs(original - recovered)}") Output: log10(x): 21.072099696512918 Recovered: 1.1805916208104748e+21 Difference: 786432.0 Sometimes the difference is small, but when I work with extremely large values or need high accuracy, this small error becomes significant. Is there any way to control or minimize this difference? Maybe with higher-precision libraries like decimal or mpmath, or completely different approaches to map large numbers into a smaller space with less loss? Thanks in advance! Continue reading...
