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www.ethanepperly.com | ||
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nhigham.com
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| | | | | Backward error is a measure of error associated with an approximate solution to a problem. Whereas the forward error is the distance between the approximate and true solutions, the backward error is how much the data must be perturbed to produce the approximate solution. For a function $latex f$ from $latex \mathbb{R}^n$ to $latex \mathbb{R}^n$ | |
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nla-group.org
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| | | | | by Sven Hammarling and Nick Higham It is often thought that Jim Wilkinson developed backward error analysis because of his early involvement in solving systems of linear equations. In his 1970 Turing lecture [5] he described an experience, during world war II at the Armament Research Department, of solving a system of twelve linear equations | |
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thomvolker.github.io
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| | | | | Many different ways of calculating OLS regression coefficients exist, but some ways are more efficient than others. In this post we discuss some of the most common ways of calculating OLS regression coefficients, and how they relate to each other. Throughout, I assume some knowledge of linear algebra (i.e., the ability to multiply matrices), but other than that, I tried to simplify everything as much as possible. | |
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www.reedbeta.com
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| | | When you read BRDF theory papers, you'll often see mention of slope space. Sometimes, components of the BRDF such as NDFs or masking-shadowing functions are defined in slope space, or operations are done in slope space before being converted back to ordinary vectors or polar coordinates. However, the meaning and intuition of slope space is rarely explained. Since it may not be obvious exactly what slope space is, why it is useful, or how to transform things to and from it, I thought I would write down a ... | ||
