finite-precision encoding vs using finite-precision

The two phrases are not directly comparable as they serve different purposes. 'Finite-precision encoding' refers to a specific method of representing numbers with limited precision, while 'using finite-precision' is a more general phrase indicating the utilization of such encoding. Both phrases can be correct depending on the context in which they are used.

Last updated: March 17, 2024 • 495 views

finite-precision encoding

This phrase is correct and commonly used in the context of representing numbers with limited precision.

This phrase is used to describe a specific method of encoding numerical data with a finite number of digits or bits to represent real numbers.

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There are also logical reasons why certain things don't work such as finite precision, encoding issues, and design limitations of certain tools.
counterexamples in a finite precision encoding is not always straightforward. 6 Experimental Evaluation. Using industry-standard practices, we generated twelve ...
Floating-point numbers are a finite precision encoding of real numbers. Floating- point operations are not closed: their result may have an absolute value greater ...
Floating-point numbers are a finite precision encoding of real numbers. Floating- point operations are not closed and may throw exceptions: their result may have ...

using finite-precision

This phrase is correct and commonly used to indicate the utilization of finite-precision encoding.

This phrase is used to describe the action of employing a method of encoding numerical data with limited precision, such as finite-precision encoding.

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Geometric reasoning using finite precision arithmetic presents great difficulties because of round-off error. Yet reasoning in a finite precision domain is an.
the potential loss of accuracy in numerical programs using finite-precision arith- ... program (using finite-precision arithmetics) conforms to what was expected by.
and roundoff error is due to using finite precision arithmetic. 2. The truncation error is O(h) and the roundoff error is O(ϵ/h), where ϵ ≈ 10. −15 in Matlab. 3.
Feb 11, 2003 ... Abstract. Two methods are proposed for correct and verifiable geometric reasoning using finite precision arithmetic. The first method, data ...

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finite-precision encoding vs using finite-precision