lp: Linear Programming and Logic Programming

this paper, after discussing the relationship of 2lp to other programming paradigms and introducing some of the features of the language, we present some examples of 2lp applications and discuss future directions. Other applications can be found in [McAloon,Tretkoff 2], [McAloon,Tretkoff 3]. 1. 2lp and other programming paradigm

Logic, Modeling, and Programming

Machine architecture, see [36], [1]. This architecture is commonly called the WAM. The 2LP interpreter employs an appropriately engineered version of the WAM, which is adapted to support the types int, double and continuous; it is a virtual machine which is emulated in C. A technical point that might be of interest: in the implementation of 2LP of [30], the linear programming module of 2LP does not stack simplex bases for backtracking; instead, a trail of changes to the polytope is kept and the changes are undone as needed. A 2LP program is compiled into code for this virtual machine. The theoretical analysis that underlies the 2LP implementation can be found in [13]. Languages which support logic programming constructs and constraints are called constraint logic programming languages or CLP languages, [23]. The first K. McAloon and C. Tretkoff /Logic, Modeling, and Programming 21 to support constraints on continuous variables were Prolog III, [12] and CLP(R), [25]. For work on CLP ..

LP Meets DP: Distributed Integer Goal Programming

: We present a vertical integration of linear programming, logic programming and distributed programming which in this paper is applied to a family of administrative problems that occur in many businesses and other organizations. These problems, which require integer goal programming, are both difficult to model and computationally demanding. 2LP is both a modeling language and a language that provides control for solving disjunctive programming problems. In 2LP integer goal programs can be modeled in an elegant manner. Most important to this work, 2LP supports a mechanism for bringing parallel computing to bear on these problems. Using the distributed programming library DP as a substratum, we have developed a scheme for parallelizing integer goal programs in an efficient way. The result is twofold: a family of applications of broad interest and a systematic way of parallelizing them on a distributed programming system. 1. Introduction A large set of very common situations that arise..