How does data quality in a network affect heuristic solutions?

To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly used p-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p). The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 supply points we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000 (which is aggregated from the 1.5 million nodes). To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when the accuracy in the road network increase and the combinatorial problem (low p) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.

Processes and variations in language economisation

This article analyses the processes of reducing language in textchats produced by non-native speakers of English. We propose that forms are reduced because of their high frequency and because of the discourse context. A wide variety of processes are attested in the literature, and we find different forms of clippings in our data, including mixtures of different clippings, homophone respellings, phonetic respellings including informal oral forms, initialisms (but no acronyms), and mixtures of clipping together with homophone and phonetic respellings. Clippings were the most frequent process (especially back-clippings and initialisms), followed by homophone respellings. There were different ways of metalinguistically marking reduction, but capitalisation was by far the most frequent. There is much individual variation in the frequencies of the different processes, although most were within normal distribution. The fact that nonnative speakers seem to generally follow reduction patterns of native speakers suggests that reduction is a universal process.