Rytter [Ryt87] (see also [RR92]) showed that any
unambiguous context-free language can be recognized in logarithmic
time by CREW-PRAM. In contrast, no such CREW algorithm is
known for any problem complete for NL, even in the nonuniform
setting. The problem is that, although NL is the class of
languages reducible to linear context-free languages, and
although
the class of languages
accepted by deterministic AuxPDAs in logarithmic
space and polynomial time coincides with the class of languages
logspace-reducible to deterministic context-free languages, and
LogCFL coincides with AuxPDA( ), it is not
known that UAuxPDA(
) or UL is reducible to unambiguous
context-free languages. The work of Niedermeier and Rossmanith
does an excellent job of explaining the subtleties and difficulties
here [NR95]. CREW algorithms are closely associated with a version of
unambiguity called strong unambiguity. In terms of Turing-machine
based computation, strong unambiguity means that, not only is there
at most one path from the start vertex to the accepting configuration, but
in fact there is at most one path between any two configurations
of the machine.
Strongly unambiguous algorithms have more efficient algorithms than
are known for general NL or UL problems. It is shown in [AL96]
that problems in Strongly unambiguous logspace have deterministic
algorithms using less than space.
The reader is encouraged to note that, in a min-unique graph, the shortest path between any two vertices is unique. This bears a superficial resemblance to the property of strong unambiguity. We see no application of this observation.
It is natural to ask if the randomized aspect of the construction can be eliminated using some sort of derandomization technique to obtain the equality UL = NL.
A corollary of our work is that UL/poly is closed under complement. It remains an open question if UL is closed under complement, although some of the unambiguous logspace classes that can be defined using strong unambiguity are known to be closed under complement [BJLR92].
It is disappointing that the techniques used in this paper do not
seem to provide any new information about complexity classes
such as NSPACE(n) and NSPACE . It is straightforward
to show that NSPACE(s(n)) is contained in USPACE
,
but this is interesting only for sublinear s(n).
There is a natural class of functions associated with NL, denoted FNL [AJ93]. This can be defined in several equivalent ways, such as
Many questions about #L remain unanswered. Two interesting complexity classes related to #L are PL (probabilistic logspace) and CL (which characterizes the complexity of singular matrices, as well as questions about computing the rank). It is known that some natural hierarchies defined using these complexity classes collapse:
In contrast, the corresponding #L hierarchy (equal to the class of problems ACAcknowledgment: We thank Klaus-Jörn Lange for helpful comments, and for drawing our attention to min-unique graphs, and for arranging for the second author to spend some of his sabbatical time in Tübingen. We also thank V. Vinay and Lance Fortnow for insightful comments.