J. Jaar and S. Michaylov. A Methodology for Managing Hard Constraints in CLP Systems. In proceedings of Sigplan PLDI, Toronto, Canada, ACM Press 1991.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Figure 1. Telling a constraint X in r instantiated. An elegant solution to achieve such a suspension is to move to the concurrent constraint framework and to use an ask mechanism [40] but staying in the CLP approach a simple solution is to use some well known delay mechanism (freeze, wait, etc. [27]. In our approach this is achieved using a new indexical term val(X) that delays the activation of a constraint in which it occurs until X has been instantiated (see for example the below de nition of X 6= Y ) 2.3. Constraint Systems The simplest way to de ne constraints is to consider them ....

J. Jaar and S. Michaylov. A Methodology for Managing Hard Constraints in CLP Systems. In proceedings of Sigplan PLDI, Toronto, Canada, ACM Press 1991.

....nonlinear constraints is a complex problem, the constraint solver in CLP(R) is restricted to linear constraints. Nonlinear constraints are delayed until some variables in these constraints get unique values during the further computation process so that the delayed constraints become linear [16] (this approach is also taken in Prolog III [4] If a computation stops with some delayed nonlinear constraints, the system generates a maybe answer, i.e. it is not ensured that a solution exists. Example 1 Consider the following CLP(R) program to compute mortgage payments: ....

....consider other arithmetic functions like , sin, cos, pow, abs, min and max. These functions can be treated similarly to in our abstract interpretation algorithm. We will discuss this subject in Section 5. 3 More details about the operational semantics and the delay mechanism can be found in [15, 11, 16]. The main goal of this paper is the characterization of a class of programs which have no delayed constraints at the end of a computation. In order to keep our analysis simple, we transform CLP(R) programs into flat CLP(R) programs where each literal has the form p(X 1 ; X n ) all X i ....

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J. Jaffar, S. Michaylov, and R.H.C. Yap. A Methodology for Managing Hard Constraints in CLP Systems. In Proc. ACM SIGPLAN'91 Conference on Programming Language Design and Implementation, pp. 306--316. SIGPLAN Notices, Vol. 26, No. 6, 1991.

....X should be delayed until X has been instantiated. An elegant solution to achieve such a suspension is to move to the concurrent constraint framework and to use an ask mechanism [40] but staying in the CLP approach a simple solution is to use some well known delay mechanism (freeze, wait, etc. [27]. In our approach this is achieved using a new indexical term val(X) that delays the activation of a constraint in which it occurs until X has been instantiated (see for example the below definition of X 6= Y ) 2.3. Constraint Systems The simplest way to define constraints is to consider them ....

J. Jaffar and S. Michaylov. A Methodology for Managing Hard Constraints in CLP Systems. In proceedings of Sigplan PLDI, Toronto, Canada, ACM Press 1991.

....Science Foundation P9374 PHY. such as f6= g together with unary relations that declare the domain to which a variable belongs. Jaffar and his group s CLP(R) 9, 14] computes on the domain of trees over reals and supports decision of linear constraints among arithmetical terms [15, 19]. An analysis of the main differences among CLP(R) and RISC CLP is presented in [12] Sakai and Aiba s CAL (Contrainte avec Logique) 22] offers nonlinear constraints over the complex numbers by employing Buchberger algorithm as decision algorithm [2, 3] RISC CLP(Real) incorporates Grobner bases ....

J. Jaffar, S. Michaylov, and Roland H. C. Yap. A methodology for managing hard constraints in CLP systems. In Proceedings of the ACM-SIGPLAN Conference on Programming Language Design and Implementation, pages 306--316, Toronto, June 1991.

.... eal numbers. Existing implementations are complete for linear (in)equations only. This restriction is motivated by the doubly exponential complexity of the decision algorithm for the general case. Nonlinear equations are delayed until they get linear, or sufficiently simple to be solved at last [Jaffar et al. 91] 2 Maintaining the solved form Traditional procedural implementations of numerical linear equation solvers operate on the coefficient matrix of an equation system of given dimension. Gaussian (quantifier) elimination aims at the determination of the rank of the system by transforming the matrix ....

Jaffar J., Michaylov S., Yap R.: A Methodology for Managing Hard Constraints in CLP Systems, in Proceedings of the ACM SIGPLAN Symposium on Programming Language Design and Implementation, Toronto, Canada, 1991.

.... of Y decreases, its complementary increases) The most elegant solution to deal with that phenomenon is to move to the concurrent constraint framework and to use an ask mechanism, but staying in the CLP approach a simple solution is to use some well known delay mechanism (freeze, wait, [13]. In our approach this is achieved using a new indexical term val(X) which delays the activation of a constraint in which it occurs until X is ground, i.e. its domain is reduced to a singleton. So dif is defined as: x6=y (X,Y) X in fval(Y)g, Y in fval(X)g. The propagation scheme used in the ....

J. Jaffar and S. Michaylov. A Methodology for Managing Hard Constraints in CLP Systems. In proceedings of Sigplan PLDI, Toronto, Canada, ACM Press 1991.

....arithmetic constraints in several spheres of scientific activity, typical efforts to provide for them amidst constraint languages have brought mostly disappointments as the resulting solvers either lacked effectiveness or scalability. The delay strategy implemented in languages such as CLP(R) [10] and PROLOG III [1] yields an incomplete solver which will be effective only if the problem under attack is such that reasoning about linear constraints ultimately becomes sufficient. Unfortunately, this is seldom the case for interesting problems, even very simple ones. One classic example is the ....

J. Jaffar, S. Michaylov, and R. H.C. Yap. A Methodology for Managing Hard Constraints in CLP Systems. ACM SIGPLAN-PLDI, 26(6), 1991.

....arithmetic constraints in several spheres of scientific activity, typical efforts to provide for them amidst constraint languages have brought mostly disappointments as the resulting solvers either lacked effectiveness or scalability. The delay strategy implemented in languages such as CLP(R) [10] and PROLOG III [1] yields an incomplete solver which will be effective only if the problem under attack is such that reasoning about linear constraints ultimately becomes sufficient. Unfortunately, this is seldom the case for interesting problems, even very simple ones. One classic example is the ....

J. Jaffar, S. Michaylov, and R. H.C. Yap. A Methodology for Managing Hard Constraints in CLP Systems. ACM SIGPLAN-PLDI, 26(6):306--316, 1991.

....A working knowledge of PROLOG programming is assumed in this document; the book by Sterling and Shapiro [13] can serve as a suitable introductory text. Further technical information on CLP(R) is available on language design and implementation [6, 7] meta programming [3] and delay mechanisms [8]. Additionally, much has been written about applications in electrical engineering [2] differential equations [1] options trading [10] music theory [15] molecular biology [16] etc. This document is both an introductory tutorial and reference manual describing the compiler based implementation ....

.... 9, S = 1, M = 1, C1 = 0, C2 = 0, C3 = 0, C4 = 0, C1 = 1, C2 = 1, C3 = 1, C4 = 1, M = C1, C2 S M = O C1 10, C3 E O = N 10 C2, C4 N R = E 10 C3, D E = Y 10 C4, bit(C1) bit(C2) bit(C3) bit(C4) bit(0) bit(1) gendiffdigits(L) gendiffdigits(L, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) gendiffdigits( gendiffdigits( H T] L) select(H, L, L2) gendiffdigits(T, L2) select(H, H T] T) select(H, H2 T] H2 T2] select(H, T, T2) solve(S, E, N, D, M, O, R, Y) Critical Path Analysis This program uses local propagation to compute start, ....

J. Jaffar, S. Michaylov and R.H.C. Yap, "A Methodology for Managing Hard Constraints in CLP Systems", Proceedings ACM-SIGPLAN Conference on Programming Language Design and Implementation, Toronto, June 1991, 306--316.

....motivation is our observation that cyclic constraints involving eval rarely appear in practical meta programs. In essence, our implementation can be characterized as follows: any constraint of the form t = eval(X) where t is an arbitrary term and X a free variable is treated as a hard constraint [12], that is, the eval constraint is not solved but delayed until X becomes instantiated. Of course, this means that our implementation is not sufficiently powerful to determine the satisfiability of certain classes of constraints that involve cyclic dependencies. 28 Importantly, this fragment of ....

Joxan Jaffar, Spiro Michaylov, and Roland Yap. A methodology for managing hard constraints in CLP systems. In Proceedings of the ACM SIGPLAN Symposium on Programming Language Design and Implementation, pages 306--316, Toronto, Canada, June 1991.

....in the presence of backtracking. For example, if changes to the structure were trailed using some adaptation of Prolog techniques [261] then a cost proportional to the number of entries can be incurred even though no guard constraints are affected. The following material is a condensation of [134]. 11.1.1. Wakeup Systems For the purposes of this section, we will describe an instance of a constraint in the form p( 1 ; Delta Delta Delta ; n )C where p is the n ary constraint symbol at hand, 1 ; Delta Delta Delta ; n are distinguished variables used as templates for the arguments ....

J. Jaffar, S. Michaylov & R. Yap, A Methodology for Managing Hard Constraints in CLP Systems, Proc. ACM-SIGPLAN Conference on Programming Language Design and Implementation, 306--316, 1991.

....A working knowledge of PROLOG programming is assumed in this document; the book by Sterling and Shapiro [20] can serve as a suitable introductory text. Further technical information on CLP(R) is available on language design and implementation [12, 13] metaprogramming [7] and delay mechanisms [14]. Additionally, much has been written about applications in electrical engineering [6, 18] differential equations [5, 8] temporal reasoning [1, 2, 3] protocol testing [4] structural analysis and synthesis [15] mechanical engineering [21] user interfaces [23] model based diagnosis [24] ....

Joxan Jaffar, Spiro Michaylov, and Roland Yap. A methodology for managing hard constraints in CLP systems. In Proceedings of the ACM SIGPLAN Symposium on Programming Language Design and Implementation, pages 306--316, Toronto, Canada, June 1991.

....would be, e.g. N 2 or P 2. CLP(R) delays non linear constraints until they become linear. This means that as in [11] such variables in non linears are not eliminable even if they are textually dead. In CLP(R) this can be easily checked (using the non linear hard constraint data structures [13]) to prevent any reported variable which is involved in non linears from being removed. Some removal is possible by examining the form of the non linears. This heuristic can be generalised to any system which utilises an incomplete solver and also systems which provide delayed predicates such as ....

Joxan Jaffar, Spiro Michaylov, and Roland H.C. Yap. A methodology for managing hard constraints in CLP systems. In Proceedings of the ACM-SIGPLAN Conference on Programming Language Design and Implementation. ACM Press, 1991.

....for domains where (special cases of) projection can be accomplished cheaply and simplifies future constraint solving. Acknowledgements Notes 1 This program is equivalent for all queries to sumlist 1.0, not just the example query. 2 For more details on the handling of hard constraints see [6] 3 Only a single closure step is required, as the sharing information carries all possible sharing arcs. 4 In this case, the existing backtracking mechanisms within the solver are sufficient. 5 This is in fact what the CLP(R) implementation chooses. ....

J. Jaffar, S. Michaylov, and R.H.C. Yap. A methodology for managing hard constraints in CLP systems. In Proc. ACM-SIGPLAN Conference on Programming Language Design and Implementation, pp. 306--316, 1991.

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J. Jaar and S. Michaylov. A Methodology for Managing Hard Constraints in CLP Systems. In proceedings of Sigplan PLDI, Toronto, Canada, ACM Press 1991.

No context found.

J. Jaffar, S. Michaylov & R.H.C. Yap, A Methodology for Managing Hard Constraints in CLP Systems, Proc. ACM-SIGPLAN Conference on Programming Language Design and Implementation, 306--316, 1991.

No context found.

J. Jaffar, S. Michaylov & R.H.C. Yap, A Methodology for Managing Hard Constraints in CLP Systems, Proc. ACM-SIGPLAN Conference on Programming Language Design and Implementation, 306--316, 1991.

No context found.

J. Jaffar, S. Michaylov & R.H.C. Yap, A Methodology for Managing Hard Constraints in CLP Systems, Proc. ACM-SIGPLAN Conference on Programming Language Design and Implementation, 306--316, 1991.

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