Numerical computing with IEEE floating point arithmetic - including one theorem, one rule of thumb, and one hundred and one exercices

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Automatic Random Variate Generation for Unbounded Densities (2006, background): “But the usual floating point arithmetic used today contains a discrete subset that is by far most dense around 0 [Overton 2001].”
Most Tensor Problems Are NP-Hard (2009, methodology): “ly our notion of reducibility in Section 1.4. 3While the "-accuracy complexity model is more realistic for numerical computations, it is not based on the IEEE oating-point standards [Kahan 1997; Overton 2001]. On the other hand, a model that combines both the exibility of the "-accuracy co”
A lower bound error for free-run simulation of the polynomial NARMAX (2016, background): “As it is well known that on floating point standard, we do not have commutative and distributive operations, they may exhibit different pseudo-orbits (Goldberg, 1991; Institute of Electrical and Electronics Engineers (IEEE), 2008; Overton, 2001).”
Introduction to Numerical Methods in Differential Equations (2006, background): “A more in-depth presentation of the properties and limitations of IEEE floating-point arithmetic can be found in Overton [2001].”
Verification methods: rigorous results using floating-point arithmetic (2010, neutral): “For another, very readable discussion see Overton (2001). Let a finite subset F ⊆ R ∪ {−∞,+∞} be given, where ∞ ∈ F and −F = F.”