Revisiting "What Every Computer Scientist Should Know About Floating-point Arithmetic"

12/04/2020

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The differences between the sets in which ideal arithmetics takes place and the sets of floating point numbers are outlined. A set of classical problems in correct numerical evaluation is presented, to increase the awareness of newcomers to the field. A self-defense, prophylactic approach to numerical computation is advocated.

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Revisiting "What Every Computer Scientist Should Know About Floating-point Arithmetic"

Eliminating Unstable Tests in Floating-Point Programs

An Efficient Summation Algorithm for the Accuracy, Convergence and Reproducibility of Parallel Numerical Methods

Computation of parabolic cylinder functions having complex argument

DSSIM: a structural similarity index for floating-point data

Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems

Numerical Replication of Computer Simulations: Some Pitfalls and How To Avoid Them

Modeling still matters: a surprising instance of catastrophic floating point errors in mathematical biology and numerical methods for ODEs

Revisiting "What Every Computer Scientist Should Know About Floating-point Arithmetic"

Related Research

Eliminating Unstable Tests in Floating-Point Programs

An Efficient Summation Algorithm for the Accuracy, Convergence and Reproducibility of Parallel Numerical Methods

Computation of parabolic cylinder functions having complex argument

DSSIM: a structural similarity index for floating-point data

Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems

Numerical Replication of Computer Simulations: Some Pitfalls and How To Avoid Them

Modeling still matters: a surprising instance of catastrophic floating point errors in mathematical biology and numerical methods for ODEs