S. Parker. Partial order programming. In Proc. 16th Symo. on Principles of Programming Languages, pages 260--266, ACM, Press, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is analyzed in various stages of compilation and execution. Objects are instances of classes organized in a partial order, and their inheritance depends on the temporal order in which the objects are de ned. The idea of formalizing object inheritance in lattice theoretic terms has been proposed by [1, 18, 19, 23, 25, 29, 32] and others. There has even been considerable interest in further decomposing inheritance graphs into modules that are ecient to query [17, 24] The LCA operation is central to such object inheritance formalizations because it is the natural method to resolve object dependence. Analysis of ....

D. S. Parker. Partial order programming. In Sixteenth Annual ACM Symposium on Principles of Programming Languages, pages 260-266, Austin, Texas, 1989.

....integrates Subset, Relational and Equational assertions. Subset assertions are a special case of partial order assertions; indeed, partial order assertions evolved from subset assertions. Finally, we briefly compare of our work with that of Stott Parker s concept of partial order programming [P89]. He describes several variations of this concept in his paper, but the one that is most closely related to ours is the paradigm in which a program is a set of assertions of the form u i w f i (v) for i = 1 : n, where each f i is continuous, and the goal is to minimize u j , for some j. At a ....

S. Parker, "Partial Order Programming," Proc. 16th ACM POPL, pp. 260-266, Austin, TX, 1989.

....for example, while we focus on a minimal set of axioms necessary to establish the necessary semantic equivalences, 16] takes a different approach by starting with closed semirings and extending this to a class of cylindric algebras. Also related is Parker s work on partial order programing [30], which shows how a variety of programming problems can be formulated in terms of minimizing the value of an expression, given constraints that specify a partial order over the domain of computation. Our goals are both more limited and more ambitious than Parker s: they are more limited because, ....

D. S. Parker, "Partial Order Programming", in Proc. Sixteenth ACM Symposium on Principles of Programming Languages, Austin, TX, Jan. 1989, pp. 260-266.

....1. specification of the constraints of the problem; 2. specification of what is to be optimized; and 3. specification of the criteria for determining the optimal solution. While several approaches to optimization have been proposed in the logic programming and deductive databases literature [20, 23, 32, 43, 48, 61, 67, 77, 84], they do not allow one to fully specify all three of the above components. Our thesis is that the concept of preference provides a natural, modular, declarative, and efficient way for expressing the three components of an optimization problem. We describe a principled extension of constraint ....

....Sagiv [77] provide semantics for aggregation where the domain over which the aggregation is performed is a complete lattice and the program is monotonic. By Tarski s theorem [87] we are guaranteed the existence of a least fixed point for the aggregate operation. Partial Order Programming: Parker [67] introduced partial order programming as a programming paradigm that unified declarative programming and mathematical programming. A program in the framework was a set of assertions of the form u i w f i ( v) for i = 1; n, where each f i is continuous (and therefore monotonic) in the ....

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S. Parker. Partial Order Programming. In Proc. 16th ACM Symp. on Principles of Programming Languages, pages 260--266, 1989.

....[15,38,57] has been shown to be at the heart of type checking in implicitly typed polymorphic programming languages. Term inequalities have also been explored as a partial order theory for constraint logic programming [49,88] and, in general, as a form of partial order programming [87]. The decidability of uniform semi unification (see chapter 3) is proved independently in [96] 54] and [38] see also section 6.4) Another special case of semi unification, in which any identifier may occur at most once in left hand sides of term inequalities, is shown decidable in [57] The ....

D. Parker. Partial order programming. In Proc. 16th Annual ACM Symp. on Principles of Programming Languages, ACM, ACM Press, January 1989.

....the programmer to state the preference criteria to suit the application at hand. In constrast, existing approaches [11, 6] only provide optimization predicates that can be expressed as maximizing or minimizing some objective function. Similarly, in the partial order programming framework of Parker [31], one computes the greatest lower bound or the least upper bound of elements in a partial order. Likewise, Maher and Stuckey [27] incorporate optimization queries into a CLP system by mapping the solutions of a query to a partial order. Moreover, our semantics for optimization is expressed by ....

S. Parker. Partial Order Programming. In Proc. 16th ACM Symp. on Principles of Programming Languages, pages 260--266, 1989.

....the criteria for determining the optimal solution. The CLP framework provides a declarative approach only to one of these three components, namely, 1) since optimization is a meta level concept in constraint logic. While several approaches to optimization have been proposed in the LP community [GGZ91, Fag93, MS89, Par89, JOM93], it is not clear in these approaches that one can fully specify all three components of an optimization problem. The contribution of this paper lies in showing how the use of preference logic provides a natural, declarative, and general means of specifying optimization problems. We first briefly ....

.... query to a partial order; Fag93] describes a semantics for optimization predicates in CLP languages based on Kunen Fitting s semantics for negation; GGZ91] shows how under certain monotonicity conditions, optimization predicates such as minimum and maximum predicates can be efficiently computed; [Par89, JOM93] discuss partial order programming over lattice domains in which the notion of maximizing (minimizing) is incorporated directly into the semantics by taking least upper bounds (greatest lower bounds) BMMW89, WB93] discuss Hierarchical CLP (HCLP) which is an extension to CLP where (numeric) ....

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S. Parker. Partial Order Programming. In Proc. ACM Conf. on Principles of Prog. Lang., pages 260--266, 1989.

.... It was Parker who also advocated programming on non symmetric transitive relations like preorder or partial order relations for generalizing and subsequently combining several different programming paradigms, symbolic or numeric, like functional and logic programming among others [Parker, 1987] [Parker, 1989]. Another recent approach for integrating functional and logic programming, based on rewriting logic, takes possibly non deterministic lazy functions as the fundamental notion [Gonz alez Moreno et al. 1996] In order to deal in practice with such multi paradigm languages like e.g. Maude it is ....

Parker, D. S. (1989). Partial order programming. In POPL'89: 16th ACM Symposium on Principles of Programming Languages, pages 260--266. ACM Press.

....programming. It was Parker who also advocated programming on non symmetric transitive relations like preorder or partial order relations for generalizing and subsequently combining several different programming paradigms, symbolic or numeric, like functional and logic programming among others [Parker, 1987] [Parker, 1989] Another recent approach for integrating functional and logic programming, based on rewriting logic, takes possibly non deterministic lazy functions as the fundamental notion [Gonz alez Moreno et al. 1996] In order to deal in practice with such multi paradigm languages like e.g. ....

Parker, D. S. (1987). Partial order programming. Unpublished monograph.

....there are three components to an optimization problem: i) specification of the constraints of the problem; ii) specification of what is to be optimized; and (iii) specification of the criteria for determining the optimal solution. While several approaches to optimization have been proposed [5, 4, 9, 11], they do not allow one to fully specify all three of the above components. The contribution of this paper lies in showing how the concept of preference provides a natural, declarative, and efficient means of specifying these components. We first briefly illustrate these three components for the ....

.... queries into a CLP system; 4] describes a semantics for optimization predicates in CLP languages based on Kunen Fitting s semantics for negation; 5] shows how under certain monotonicity conditions, optimization predicates such as minimum and maximum predicates can be efficiently computed; [11] discuss partial order programming over lattice domains in which the notion of maximizing (minimizing) is incorporated directly into the semantics by taking least upper bounds (greatest lower bounds) 13] discusses Hierarchical CLP (HCLP) which is an extension to CLP where (numeric) strengths are ....

[Article contains additional citation context not shown here]

S. Parker. Partial Order Programming. In Proc. 16th POPL, pages 260--266, 1989.

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S. Parker. Partial order programming. In Proc. 16th Symo. on Principles of Programming Languages, pages 260--266, ACM, Press, 1989.

No context found.

S. Parker. Partial order programming. In Proc. 16th Symo. on Principles of Programming Languages, pages 260-266, ACM, Press, 1989.

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