Factor analysis is a statistical technique for reducing the number of factors responsible for a matrix of correlations to a smaller number of factors that may reflect underlying variables. In this study factor analysi...
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Recognizing the satisfiability of constraint Satisfaction Problems (CSPs) is NP-hard. Although several Machine Learning (ML) approaches have attempted this task by casting it as a binary classification problem, they h...
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Imperative and object-oriented programming languages are among the most common languages for general-purpose programming. these languages work well for handling many common tasks necessary for most applications. Howev...
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ISBN:
(纸本)9781450397032
Imperative and object-oriented programming languages are among the most common languages for general-purpose programming. these languages work well for handling many common tasks necessary for most applications. However, there are still many hard problems that remain difficult to implement directly in imperative languages. Declarative languages have worked well for solving many of these problems by providing a syntax that allows the user to focus on modeling the problem rather than on designing an algorithm. Logic programming languages, like Prolog, have seen success in constraint satisfaction problems, logical databases, and various NP-Hard problems. Unfortunately, these languages have not seen the same success in general-purpose programming, and most of the problems they solve do not exist in isolation. Furthermore, many imperative programmers are still unfamiliar with and unaware of logic programming. In this work, we aim to integrate a logical predicate, borrowed from logic programming, into an imperative language to facilitate a model-based approach to programming. Rather than attempting to embed Prolog directly into the language, we provide a logic for reasoning over imperative expressions using the common Boolean operators familiar to imperative programmers. While logic programming relies on unification, our predicate relies on variable assignment which is standard in imperative languages. We have implemented the predicate into the syntax of the Python programming language along with a solver built into the runtime for solving queries on the predicate. We demonstrate the use of the construct through solving commonly-known problems, such as graph coloring and the N-Queens problem. To evaluate the viability of our predicate, we compare the performance results of our predicate against current implementations of Prolog. By bringing this predicate construct to imperative languages, it is our hope that we can help to bridge the gap between imperative and logic prog
the addition of symmetry breaking constraints is one of the most successful symmetry breaking technique for constraint satisfaction problems (CSP). In this paper we present STAB, a method that adds some symmetry break...
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Many global constraints can be described by a regular expression or a DFA. Originally, the regular constraint, uses a DFA to describe the constraint, however, it can also be used to express a table constraint. thus, t...
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Variable ordering heuristics are one of the key settings for an efficient constraint solver. During the last two decades, a considerable effort has been spent for designing dynamic heuristics that iteratively change t...
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An important extension of constraint technology involves problems that undergo changes that may invalidate the current solution. Previous work on dynamic problems sought methods for efficiently finding new solutions. ...
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One of the critical problems in the call center industries is the staffing problem, since they must face variable demands and because staff costs represent a major part of the costs of these industries. Prom a modelin...
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We report on an empirical evaluation of a new probabilistic heuristic for constructive search in constraint satisfaction problems. the heuristic is based on the estimation of solution probability. We show empirically ...
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Multi-objective problems are frequent in the real world. In general they involve several incomparable objectives and the goal is to find a set of Pareto optimal solutions, i.e. solutions that are incomparable two by t...
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