This paper seeks to define robust,
efficient solvers of regressions of
nature with two goals:
(1) straightforward parameterization,
and (2) ``blocky'' solutions.
It uses an
hybrid norm characterized
by a residual
of transition between
and
for data fitting and another
for model styling.
Both the steepest descent and conjugate direction methods
are included.
The 1-D blind deconvolution problem is formulated
in a manner intended to lead to both
a blocky impedance function
and a source waveform.
No results are given.