What is it?
Absolute error and relative error . Relative error counts correct significant digits, and it is what floating point controls: every operation is exact up to a relative error of at most in double precision.
Formulas
- the IEEE 754 model of arithmetic
Where it shows up in computing
Floating point is designed around a guaranteed relative error per operation.
Every numerical result should come with an error estimate; truncation and rounding errors are budgeted separately.
Where is it used?
Computing topics reachable from here, through the chain of ideas that leads to them:
⚛ Physics and simulation
- Numerical stability→Physics engines★★★★★
- Numerical stability→Physics engines→N-body gravitational simulation★★★★★
- Numerical stability→Numerical differentiation→Heat equation and diffusion★★★★★
- Numerical stability→Physics engines→Fluid dynamics and CFD★★★★★
- Numerical stability→Physics engines→Fluid dynamics and CFD→Weather and climate modelling★★★★★
ℒ AI and machine learning
- Numerical stability→Loss function★★★★★
- Conditioning→Loss landscape★★★★★
- Numerical stability→Loss function→Gradient descent★★★★★
- Numerical stability→Loss function→Logistic regression★★★★★
- Numerical stability→Loss function→Regularization★★★★★
- Numerical stability→Loss function→Support vector machines★★★★★
- +12
What depends on it
This page has the essentials. A fuller treatment (intuition, formal definition, worked example) is on the way.