1,387 research outputs found
Analysis of nonlinear constraints in CLP(R)
Solving nonlinear constraints over real numbers is a complex problem. Hence constraint logic programming languages like CLP(R) or Prolog III solve only linear constraints and delay nonlinear constraints until they become linear. This efficient implementation method has the disadvantage that sometimes computed answers are unsatisfiable or infinite loops occur due to the unsatisfiability of delayed nonlinear constraints. These problems could be solved by using a more powerful constraint solver which can deal with nonlinear constraints like in RISC-CLP(Real). Since such powerful constraint solvers are not very efficient, we propose a compromise between these two extremes. We characterize a class of CLP(R) programs for which all delayed nonlinear constraints become linear at run time. Programs belonging to this class can be safely executed with the efficient CLP(R) method while the remaining programs need a more powerful constraint solver
A practical approach to the global analysis of CLP programs
This paper presents and illustrates with an example a practical approach to the dataflow analysis of programs written in constraint logic programming (CLP) languages using abstract interpretation. It is first argued that,
from the framework point of view, it sufnces to propose relatively simple extensions of traditional analysis methods which have already been proved useful and practical and for which efncient fixpoint algorithms have been
developed. This is shown by proposing a simple but quite general extensión of Bruynooghe's traditional framework to the analysis of CLP programs. In this extensión constraints are viewed not as "suspended goals" but rather as new information in the store, following the traditional view of CLP. Using this approach, and as an example of its use, a complete, constraint system independent, abstract analysis is presented for approximating definiteness information. The analysis is in fact of quite general applicability. It has been implemented and used in the analysis of CLP(R) and Prolog-III applications. Results from the implementation of this analysis are also presented
Independence in constraint logic programs
Studying independence of literals, variables, and substitutions has proven very useful in the context of logic programming (LP). Here we study independence in the broader context of constraint logic programming (CLP). We show that a naive extrapolation of the LP definitions of independence to CLP is unsatisfactory (in fact, wrong) for two reasons. First, because interaction between variables through constraints is more complex than in the case of logic programming. Second, in order to ensure the efUciency of several optimizations not only must independence of the search space be considered, but also an orthogonal issue - "independence of constraint solving." We clarify these issues by proposing various types of search independence
and constraint solver independence, and show how they can be combined to allow different independence-related optimizations, from parallelism to intelligent backtracking. Sufficient conditions for independence which can be evaluated "a-priori" at run-time are also proposed. Our results suggest that independence, provided a suitable definition is chosen, is even more useful in CLP than in LP
Independence in CLP Languages
Studying independence of goals has proven very useful in the context of logic programming. In particular, it has provided a formal basis for powerful automatic parallelization tools, since independence ensures that two goals may be evaluated in parallel while preserving correctness and eciency. We extend the concept of independence to constraint logic programs (CLP) and
prove that it also ensures the correctness and eciency of the parallel evaluation of independent goals. Independence for CLP languages is more complex than for logic programming as search space preservation is necessary but no longer sucient for ensuring correctness and eciency. Two
additional issues arise. The rst is that the cost of constraint solving may depend upon the order constraints are encountered. The second is the need to handle dynamic scheduling. We clarify these issues by proposing various types of search independence and constraint solver independence, and show how they can be combined to allow dierent optimizations, from parallelism to intelligent
backtracking. Sucient conditions for independence which can be evaluated \a priori" at run-time are also proposed. Our study also yields new insights into independence in logic programming languages. In particular, we show that search space preservation is not only a sucient but also a necessary condition for ensuring correctness and eciency of parallel execution
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Proving Correctness of Imperative Programs by Linearizing Constrained Horn Clauses
We present a method for verifying the correctness of imperative programs
which is based on the automated transformation of their specifications. Given a
program prog, we consider a partial correctness specification of the form
prog , where the assertions and are
predicates defined by a set Spec of possibly recursive Horn clauses with linear
arithmetic (LA) constraints in their premise (also called constrained Horn
clauses). The verification method consists in constructing a set PC of
constrained Horn clauses whose satisfiability implies that prog
is valid. We highlight some limitations of state-of-the-art
constrained Horn clause solving methods, here called LA-solving methods, which
prove the satisfiability of the clauses by looking for linear arithmetic
interpretations of the predicates. In particular, we prove that there exist
some specifications that cannot be proved valid by any of those LA-solving
methods. These specifications require the proof of satisfiability of a set PC
of constrained Horn clauses that contain nonlinear clauses (that is, clauses
with more than one atom in their premise). Then, we present a transformation,
called linearization, that converts PC into a set of linear clauses (that is,
clauses with at most one atom in their premise). We show that several
specifications that could not be proved valid by LA-solving methods, can be
proved valid after linearization. We also present a strategy for performing
linearization in an automatic way and we report on some experimental results
obtained by using a preliminary implementation of our method.Comment: To appear in Theory and Practice of Logic Programming (TPLP),
Proceedings of ICLP 201
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