8 research outputs found
Simulation of Two-Way Pushdown Automata Revisited
The linear-time simulation of 2-way deterministic pushdown automata (2DPDA)
by the Cook and Jones constructions is revisited. Following the semantics-based
approach by Jones, an interpreter is given which, when extended with
random-access memory, performs a linear-time simulation of 2DPDA. The recursive
interpreter works without the dump list of the original constructions, which
makes Cook's insight into linear-time simulation of exponential-time automata
more intuitive and the complexity argument clearer. The simulation is then
extended to 2-way nondeterministic pushdown automata (2NPDA) to provide for a
cubic-time recognition of context-free languages. The time required to run the
final construction depends on the degree of nondeterminism. The key mechanism
that enables the polynomial-time simulations is the sharing of computations by
memoization.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
An in-between "implicit" and "explicit" complexity: Automata
Implicit Computational Complexity makes two aspects implicit, by manipulating
programming languages rather than models of com-putation, and by internalizing
the bounds rather than using external measure. We survey how automata theory
contributed to complexity with a machine-dependant with implicit bounds model
Memoization for Unary Logic Programming: Characterizing PTIME
We give a characterization of deterministic polynomial time computation based
on an algebraic structure called the resolution semiring, whose elements can be
understood as logic programs or sets of rewriting rules over first-order terms.
More precisely, we study the restriction of this framework to terms (and logic
programs, rewriting rules) using only unary symbols. We prove it is complete
for polynomial time computation, using an encoding of pushdown automata. We
then introduce an algebraic counterpart of the memoization technique in order
to show its PTIME soundness. We finally relate our approach and complexity
results to complexity of logic programming. As an application of our
techniques, we show a PTIME-completeness result for a class of logic
programming queries which use only unary function symbols.Comment: Soumis {\`a} LICS 201
A sound definitional interpreter for a simply typed functional language
In this paper, we develop, in the proof assistant Coq, a definitional interpreter and a type-checker for a simply typed functional language, and formally prove that the mentioned type-checker is sound with respect to the definitional interpreter via progress and preservation. To represent binders, we embark on the choice of “concrete syntax” in which parameters are just names (or strings)
On the Resolution Semiring
In this thesis, we study a semiring structure with a product based on theresolution rule of logic programming. This mathematical object was introducedinitially in the setting of the geometry of interaction program in order to modelthe cut-elimination procedure of linear logic. It provides us with an algebraicand abstract setting, while being presented in a syntactic and concrete way, inwhich a theoretical study of computation can be carried on.We will review first the interactive interpretation of proof theory withinthis semiring via the categorical axiomatization of the geometry of interactionapproach. This interpretation establishes a way to translate functional programsinto a very simple form of logic programs.Secondly, complexity theory problematics will be considered: while thenilpotency problem in the semiring we study is undecidable in general, it willappear that certain restrictions allow for characterizations of (deterministicand non-deterministic) logarithmic space and (deterministic) polynomial timecomputation
Mechanizing Abstract Interpretation
It is important when developing software to verify the absence of undesirable
behavior such as crashes, bugs and security vulnerabilities. Some settings
require high assurance in verification results, e.g., for embedded software in
automobiles or airplanes. To achieve high assurance in these verification
results, formal methods are used to automatically construct or check proofs of
their correctness. However, achieving high assurance for program analysis
results is challenging, and current methods are ill suited for both complex
critical domains and mainstream use.
To verify the correctness of software we consider program analyzers---automated
tools which detect software defects---and to achieve high assurance in
verification results we consider mechanized verification---a rigorous process
for establishing the correctness of program analyzers via computer-checked
proofs.
The key challenges to designing verified program analyzers are: (1) achieving
an analyzer design for a given programming language and correctness property;
(2) achieving an implementation for the design; and (3) achieving a mechanized
verification that the implementation is correct w.r.t. the design. The state of
the art in (1) and (2) is to use abstract interpretation: a guiding
mathematical framework for systematically constructing analyzers directly from
programming language semantics. However, achieving (3) in the presence of
abstract interpretation has remained an open problem since the late 1990's.
Furthermore, even the state-of-the art which achieves (3) in the absence of
abstract interpretation suffers from the inability to be reused in the presence
of new analyzer designs or programming language features.
First, we solve the open problem which has prevented the combination of
abstract interpretation (and in particular, calculational abstract
interpretation) with mechanized verification, which advances the state of the
art in designing, implementing, and verifying analyzers for critical software.
We do this through a new mathematical framework Constructive Galois Connections
which supports synthesizing specifications for program analyzers, calculating
implementations from these induced specifications, and is amenable to
mechanized verification.
Finally, we introduce reusable components for implementing analyzers for a wide
range of designs and semantics. We do this though two new frameworks Galois
Transformers and Definitional Abstract Interpreters. These frameworks tightly
couple analyzer design decisions, implementation fragments, and verification
properties into compositional components which are (target)
programming-language independent and amenable to mechanized verification.
Variations in the analysis design are then recovered by simply re-assembling
the combination of components. Using this framework, sophisticated program
analyzers can be assembled by non-experts, and the result are guaranteed to be
verified by construction