# Multiple Isotonic Regression

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The first two sections are based on a good tutorial on the isotonic regression, and the third section consists of the slides for the talk given by Prof. Cun-Hui Zhang at the 11th ICSA International Conference on Dec. 21st, 2019.

## Isotonic Regression

Intuitionally, **linear ordering isotonic regression** can be thought as approximating given series of 1-dimensional observations with non-decreasing function, while **general isotonic regression** is approximating given series of values which satisfy a given partial ordering.

Formally, given values $\mathbf{a}\in \IR^n$ and the associated positive weights $\w\in\IR^n > 0$,

where the set of constraints $E$ constitutes a acyclic graph.

In particular, the aforementioned linear ordering isotonic regression is one with constraint $y_1\le y_2\le\ldots\le y_n$. There is a simple linear algorithm for such special case, called **Pool Adjacent Violators Algorithm (PAVA)**.

## Pool Adjacent Violators Algorithm

For an optimal solution, if $a_i \ge a_{i+1}$, then $y_i = y_{i+1}$.

It is quite clear from the following sketch,

Formally, we can prove it by contradiction, i.e., suppose $y_i < y_{i+1}$, then

Since $y_i = y_{i+1}$, we can combine $(w_i, a_i)$ and $(w_{i+1}, a_{i+1})$ as a new point,

Repeating the combining procedure until the value $a_iā <= a_{i+1}ā$, and then the solution exactly equals to the corresponding combined point.

Actually, I had implemented the PAVA for a toy example in R and Julia, but I forgot completely (~~my guilt~~).

## Multiple Isotonic Regression

Pay attention to the notation difference, we can treat $y_i, \x_i$ in the slide as $a_i, y_i$ in the previous section, respectively. And there is one more additional non-decreasing function $f$. More formal description can be found in their paper,