  Given a real-valued weighted function $f$ on a finite dag, the $L_p$ isotonic
regression of $f$, $p \in [0,\infty]$, is unique except when $p \in [0,1] \cup
\{\infty\}$. We are interested in determining a ``best'' isotonic regression
for $p \in \{0, 1, \infty\}$, where by best we mean a regression satisfying
stronger properties than merely having minimal norm. One approach is to use
strict $L_p$ regression, which is the limit of the best $L_q$ approximation as
$q$ approaches $p$, and another is lex regression, which is based on lexical
ordering of regression errors. For $L_\infty$ the strict and lex regressions
are unique and the same. For $L_1$, strict $q \scriptstyle\searrow 1$ is
unique, but we show that $q \scriptstyle\nearrow 1$ may not be, and even when
it is unique the two limits may not be the same. For $L_0$, in general neither
of the strict and lex regressions are unique, nor do they always have the same
set of optimal regressions, but by expanding the objectives of $L_p$
optimization to $p < 0$ we show $p{ \scriptstyle \nearrow} 0$ is the same as
lex regression. We also give algorithms for computing the best $L_p$ isotonic
regression in certain situations.
