A recent article [2] introduces a symbolic testing approach that accurately constructs and propagates floating-point constraints to reveal hidden errors not readily detected by traditional methods.

I am working on a viewshed* algorithm that does some floating point arithmetic. The algorithm sacrifices accuracy for speed and so only builds an approximate viewshed. The algorithm iteratively ...

Although floating point arithmetic standards – like the commonly used IEEE 754 – seek to minimize this error, it’s inevitable that across the range of a floating point variable loss of ...

A similar conclusionwas reached recently by Microsoft and Meta: Floating-point arithmetic is just much less efficient than integer arithmetic.

"Dr. Gustafson has recently finished writing a book, The End of Error: Unum Computing, that presents a new approach to computer arithmetic: the unum. The universal number, or unum format, encompasses ...

Floating-point arithmetic is necessary to meet precision and performance requirements for an increasing number of applications. Today, most 32-bit embedded processors that offer this functionality are ...

Because fixed-point DSPs are consumed in large volumes, their price per chip is a fraction of the price of a floating-point DSP. As a result, the only developers who can reasonably justify using a ...

That is fixed point arithmetic, and yes it works. The loss of dynamic range and corresponding resolution for small numbers means that floating point is usually preferred except for special ...

When the %SYSEVALF function evaluates arithmetic expressions, it temporarily converts the operands that represent numbers to floating point values. The result of the evaluation can represent a ...