6 research outputs found
Synthesis of sup-interpretations: a survey
In this paper, we survey the complexity of distinct methods that allow the
programmer to synthesize a sup-interpretation, a function providing an upper-
bound on the size of the output values computed by a program. It consists in a
static space analysis tool without consideration of the time consumption.
Although clearly related, sup-interpretation is independent from termination
since it only provides an upper bound on the terminating computations. First,
we study some undecidable properties of sup-interpretations from a theoretical
point of view. Next, we fix term rewriting systems as our computational model
and we show that a sup-interpretation can be obtained through the use of a
well-known termination technique, the polynomial interpretations. The drawback
is that such a method only applies to total functions (strongly normalizing
programs). To overcome this problem we also study sup-interpretations through
the notion of quasi-interpretation. Quasi-interpretations also suffer from a
drawback that lies in the subterm property. This property drastically restricts
the shape of the considered functions. Again we overcome this problem by
introducing a new notion of interpretations mainly based on the dependency
pairs method. We study the decidability and complexity of the
sup-interpretation synthesis problem for all these three tools over sets of
polynomials. Finally, we take benefit of some previous works on termination and
runtime complexity to infer sup-interpretations.Comment: (2012
Modular Complexity Analysis for Term Rewriting
All current investigations to analyze the derivational complexity of term
rewrite systems are based on a single termination method, possibly preceded by
transformations. However, the exclusive use of direct criteria is problematic
due to their restricted power. To overcome this limitation the article
introduces a modular framework which allows to infer (polynomial) upper bounds
on the complexity of term rewrite systems by combining different criteria.
Since the fundamental idea is based on relative rewriting, we study how matrix
interpretations and match-bounds can be used and extended to measure complexity
for relative rewriting, respectively. The modular framework is proved strictly
more powerful than the conventional setting. Furthermore, the results have been
implemented and experiments show significant gains in power.Comment: 33 pages; Special issue of RTA 201