I have been wracking my head around the understanding on how to calculate $N_{1}(f)$ and $N_{0}(f)$ from the communication matrix.
Definitions:
$N_{1}(f)$ = least cost of non-deterministic protocol that accepts $f^{-1}(1) = \{(x,y) : f(x,y) = 1 \}$
$N_{1}(f)$ = ceiling( log(number of monochromatic rectangles needed to cover $f^{-1}(1)$))
Examples:
I would like to connect understand how this gets translated into the following two examples:
Equality
We know that the communication matrix has 1s on the diagonal and everything else is covered with 0.
$N_{1}(f) = n$ - this makes sense, I need exactly $n$ rectangles to cover my 1s on the diagonal, one rectangle per cell.
$N_{0}(f) = \log n + 1$ - this does not make sense. How can you cover all 0s with only that many monochromatic rectangles?
Greater Than
We know that the communication matrix has 1s on the diagonal and on top of it and everything else is covered with 0s.
$N_{1}(f) = n$ - I am assuming the rectangles are now not just an individual cell in the diagonal, but the whole row.
$N_{0}(f) = n$ - I am assuming the rectangles are now the entire column.
If my understanding for $N_{0}(f)$ for the Greater Than is correct, then I do not understand how the colouring works for the Equality problem.