An unfortunate reality of trying to represent continuous real numbers in a fixed space (e.g. with a limited number of bits) is that this comes with an inevitable loss of both precision and accuracy.

These include under- and overflow, but also specific floating point inputs. Unlike in scientific calculations where even minor inaccuracies tend to propagate and cause much larger errors down the ...

AutoRNP is a dynamic analysis tool for automatically detecting and repairing high floating-point errors in numerical programs. It includes following functions: Detecting high floating-point errors in ...

as this can cause overflow or underflow errors. To ensure numerical stability and accuracy, use libraries or functions such as numpy, scipy, or math in Python. Floating-point arithmetic is widely ...

Estimating worst-case roundoff errors is a crucial step in floating-point program analysis and verification. Successful tools in this area are based on symbolic ...

It is difficult to catch these errors in the early design stage without ... This paper proposes an easy-to-use Python library for IEEE-754-based floating-point numbers with arbitrary exponent and ...