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About 10,000 papers for “Floating point”

Floating Point

Ronald T. Kneusel · Texts in computer science · 2024

Cited by 0

Floating Point

Marco Nicola · Bulletin of Miscellaneous Information (Royal Gardens Kew)

Artist: Marco Nicola Title: Floating Point Released on September 2007 Tracklist: 01 The First Track (2:51) 02 27422 (6:42) 03 Unusual Travels I (14:38) 04 Waiting (5:23) 05 Floating Point (8:12) 06 27428 (4:04) 07 Unusual Travels II (12:08) Total duration: 54:03 http://marconicola.altervista.org/

Cited by 0

Floating Point

Antonio Lloris Ruiz, Encarnación Castillo, Luis Parrilla Roure et al. · Intelligent systems reference library · 2021

Cited by 0

Floating point

Larry D. Pyeatt, William Ughetta · Elsevier eBooks · 2019

Cited by 0

Floating Point

Marco Nicola · Bulletin of Miscellaneous Information (Royal Gardens Kew)

Artist: Marco Nicola Title: Floating Point Released on September 2007 Tracklist: 01 The First Track (2:51) 02 27422 (6:42) 03 Unusual Travels I (14:38) 04 Waiting (5:23) 05 Floating Point (8:12) 06 27428 (4:04) 07 Unusual Travels II (12:08) Total duration: 54:03 http://marconicola.altervista.org/

Cited by 0Open access

Floating Point

The Aphren Flyers · Bulletin of Miscellaneous Information (Royal Gardens Kew)

Cited by 0

tkphd/floating-point: A Primer on Floating-Point Inaccuracy

Keller, Trevor · Zenodo (CERN European Organization for Nuclear Research) · 2022

A primer on the floating-point representation of real numbers using C and C++, with comments on two's complement and IEEE 754. This code should not be relied upon, may contain errors, and is intended for educational purposes only.

Cited by 0Open access

Accurate floating-point operation using controlled floating-point precision

A. Zaki, Mohamed H. El-Shafey, Ayman M. Bahaa-Eldin et al. · 2011

Rounding and accumulation of errors when using floating point numbers are important factors in computer arithmetic. Many applications suffer from these problems. The underlying machine architecture and representation of floating point numbers play the major role in the level and value of errors in this type of calculations. A quantitative measure of a system error level is the machine epsilon. In the current representation of floating point numbers, the machine epsilon can be as small as 9.63E-35 in the 128 bit version of IEEE standard floating point representation system. In this work a novel solution that guarantees achieving the desired minimum error regardless of the machine architecture is presented. The proposed model can archive a machine epsilon of about 4.94E-324. A new representation model is given and a complete arithmetic system with basic operations is presented. The accuracy of the proposed method is verified by inverting a high order, Hilbert matrix, an ill-conditioned matrix that cannot be solved in the traditional floating point standard. Finally some comparisons are given.

Cited by 3

Floating-Point Arithmetic

Jo Van Hoey · Apress eBooks · 2019

You already know about integer arithmetic; now we will introduce some floating-point computations. There is nothing difficult here; a floating-point value has a decimal point in it and zero or more decimals. We have two kinds of floating-point numbers: single precision and double precision. Double precision is more accurate because it can handle more significant digits. With that information, you now know enough to run and analyze the sample program in this chapter.

Cited by 0

Floating-Point Verification

J. P. Harrison · TUGraz OPEN Library (Graz University of Technology) · 2020

Abstract: This paper overviews the application of formal verification techniques to hardware in general, and to floating-point hardware in particular. A specific challenge is to connect the usual mathematical view of continuous arithmetic operations with the discrete world, in a credible and verifiable way.

Cited by 0Open access

Floating-point arithmetic

Sylvie Boldo, Claude-Pierre Jeannerod, Guillaume Melquiond et al. · Acta Numerica · 2023

Floating-point numbers have an intuitive meaning when it comes to physics-based numerical computations, and they have thus become the most common way of approximating real numbers in computers. The IEEE-754 Standard has played a large part in making floating-point arithmetic ubiquitous today, by specifying its semantics in a strict yet useful way as early as 1985. In particular, floating-point operations should be performed as if their results were first computed with an infinite precision and then rounded to the target format. A consequence is that floating-point arithmetic satisfies the ‘standard model’ that is often used for analysing the accuracy of floating-point algorithms. But that is only scraping the surface, and floating-point arithmetic offers much more. In this survey we recall the history of floating-point arithmetic as well as its specification mandated by the IEEE-754 Standard. We also recall what properties it entails and what every programmer should know when designing a floating-point algorithm. We provide various basic blocks that can be implemented with floating-point arithmetic. In particular, one can actually compute the rounding error caused by some floating-point operations, which paves the way to designing more accurate algorithms. More generally, properties of floating-point arithmetic make it possible to extend the accuracy of computations beyond working precision.

Cited by 25Open access

Floating-Point Numbers

James Derry · Apress eBooks · 2025

Finding patterns and relationships in quantitative data collected in or gleaned from experiments is a common activity in STEM research. In this chapter, we will consider floating-point numbers datatype used in Python in modern digital computers, focusing on their accuracy and precision.

Cited by 0