Reducing the Influence of Tiny Normwise Relative Errors on Performance Profiles

Dingle, Nicholas J. and Higham, Nicholas J. (2011) Reducing the Influence of Tiny Normwise Relative Errors on Performance Profiles. [MIMS Preprint]

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It is a widespread but little-noticed phenomenon that the normwise relative error $\|x-y\| / \|x\|$ of vectors $x$ and $y$ of floating point numbers, where $y$ is an approximation to $x$, can be many orders of magnitude smaller than the unit roundoff. We analyze this phenomenon and show that in the $\infty$-norm it happens precisely when $x$ has components of widely varying magnitude and every component of $x$ of largest magnitude agrees with the corresponding component of $y$. Performance profiles are a popular way to compare competing algorithms according to particular measures of performance. We show that performance profiles based on normwise relative errors can give a misleading impression due to the influence of zero or tiny errors. We propose a transformation that reduces the influence of these extreme errors in a controlled manner, while preserving the monotonicity of the underlying data and leaving the performance profile unchanged at its left end-point. Numerical examples with both artificial and genuine data illustrate the benefits of the transformation.

Item Type: MIMS Preprint
Uncontrolled Keywords: normwise relative error, performance profile, floating point arithmetic, forward error, backward error
Subjects: MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis
Depositing User: Nick Higham
Date Deposited: 09 Nov 2011
Last Modified: 08 Nov 2017 18:18

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