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Least squares is a form of approximation, and is used to make a prediction based on experimental data.

## Example

Given a set of points $P = (x_1, y_1) (x_2, y_2)...(x_n, y_n)$, and the deviation from the points $d=y_i - (ax_i + b)$ minimize $D=\sum_{i=1}^n (y_i - (ax_i+b))^2$ by changing values a and b.

Finding the minimum involves talking a look a the derivates.

• $\frac{\delta D}{\delta a}=\sum_{i=1}^n 2 (y_i - (ax_i + b)) (-x_i)$
• $\frac{\delta D}{\delta b}=\sum_{i=1}^n 2 (y_i - (ax_i + b)) (-1)$

Removing unnecessary 2 constants (solving to approach 0), and simplifying:

• $\frac{\delta D}{\delta a}=\sum_{i=1}^n (y_i - (ax_i + b)) (-x_i) = \sum (a x_i^2 + x_i b + - x_i y_i)$
• $\frac{\delta D}{\delta b}=\sum_{i=1}^n (y_i - (ax_i + b)) (-1) = \sum (a x_i + b - y_i)$

This can be re-written as a 2x2 linear system:

• $a \sum x_i^2 + b \sum x_i = \sum x_i y_i$
• $a \sum x_i + b n = \sum y$

Note that some systems may be more complex, and may involve more than two paramaters.

## Recursive least-squares algorithm

The Recursive least-squares formula is designed for real-time estimation, rather than performing a batch result each time an entry is added.[1]

$\Theta = PB$
$P = [\sum_{i=1}^N (\phi(t) \phi^T(t))]^{-1} = (\Phi \Phi^T)^{-1}$
$P^{-1} = [\sim_{i=1}^N (\phi(t) \phi^T(t))] = (\Phi \Phi^T) : [itex]b = \sum{i=1}^N y(t)\phi(t)$

## References

1. Identification, Estimation, and Learning - Lecture 2 - MIT OpenCourseware
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