Reducing the time required to find the Kemeny ranking by exploiting a necessary condition for being a winner

Rico, Noelia; Vela, Camino R.; Dı́az, Irene

doi:10.1016/j.ejor.2022.07.031

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…In comparison with the results obtained for the CPU in [11], in this case the execution times in CPU are achieved due to the bounds of the algorithm, which makes the algorithm very variable and unpredictable as the execution time cannot be known in advance. The best runtimes in CPU for n = 13 gives similar results than the GPU but is very affected by the number of voters and whether this is odd or even and also slowing the algorithm when it increases.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The algorithm proposed has been tested for a total of 19600 profiles of rankings. These profiles have been also used in the work [11], so using them provides a fair comparison between the results obtained for the CPU algorithm in relation to the ones obtained with the GPU algorithm proposed in this work. The list of profiles of rankings have been synthetically generated as follows:…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the last years, Azzini and Munda [7] introduced an exact algorithm that greatly improved the runtime in relation to others published before [8,9]. We have improved these results in [10,11], ensuring the feasibility of the execution up to 13 alternatives. However, the runtime of the branch and bound algorithms (B &B) proposed in these works depends on the characteristics of the input profiles and cannot be exactly known prior to the execution.…”

Section: Introductionmentioning

confidence: 82%

“…Currently, in the literature there are several algorithms that solve the Kemeny ranking. These are implemented for CPUs [7,9,11]. The ones showing the best results are those that are designed as branch and bound (B &B) algorithms.…”

Section: Developing a Kemeny Algorithm For The Gpumentioning

confidence: 99%

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Kemeny ranking aggregation meets the GPU

2023

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Ranking aggregation, studied in the field of social choice theory, focuses on the combination of information with the aim of determining a winning ranking among some alternatives when the preferences of the voters are expressed by ordering the possible alternatives from most to least preferred. One of the most famous ranking aggregation methods can be traced back to 1959, when Kemeny introduces a measure of distance between a ranking and the opinion of the voters gathered in a profile of rankings. Using this, he proposed to elect as winning ranking of the election the one that minimizes the distance to the profile. This is factorial on the number of alternatives, posing a handicap in the runtime of the algorithms developed to find the winning ranking, which prevents its use in real problems where the number of alternatives is large. In this work we introduce the first algorithm for the Kemeny problem designed to be executed in a Graphical Processing Unit. The threads identifiers are codified to be associated with rankings by means of the factorial number system, a radix numeral system that is then used to uniquely pair a ranking with the thread using Lehmer’s code. Results guarantee constant execution time up to 14 alternatives.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 82%

Section: Developing a Kemeny Algorithm For The Gpumentioning

confidence: 99%

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Kemeny ranking aggregation meets the GPU

2023

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“…This process was repeated 50 times, obtaining 50 different rankings, then aggregated using scoring ranking rules and assigning each candidate with a score, finally obtaining variable importance. Although many different ways may obtain a consensus ranking (28,29,30,31,32), the Borda count is a quite simple convex-ranking-rule (8), already successfully applied similarly by Rúa et al (2023).…”

Section: Analysis Of Relevant Featuresmentioning

confidence: 99%

About identification of features that affect the estimation of citrus harvest

Bóbeda¹,

Mazza²,

Rico³

et al. 2023

Rev. FCA UNCUYO

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Accurate models for early harvest estimation in citrus production generally involve expensive variables. The goal of this research work was to develop a model to provide early and accurate estimations of harvest using low-cost features. Given the original data may derive from tree measurements, meteorological stations, or satellites, they have varied costs. The studied orchards included tangerines (Citrus reticulata x C. sinensis) and sweet oranges (C. sinensis) located in northeastern Argentina. Machine learning methods combined with different datasets were tested to obtain the most accurate harvest estimation. The final model is based on support vector machines with low-cost variables like species, age, irrigation, red and near-infrared reflectance in February and December, NDVI in December, rain during ripening, and humidity during fruit growth. Highlights: Red and near-infrared reflectance in February and December are helpful values to predict orange harvest. SVM is an efficient method to predict harvest. A ranking method to A ranking-based method has been developed to identify the variables that best predict orange production.

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Decision field theory-combined multi-attribute group decision-making method for incomplete linear ordinal ranking

Liu

Zhang

2023

Applied Soft Computing

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Reducing the time required to find the Kemeny ranking by exploiting a necessary condition for being a winner

Cited by 6 publications

References 18 publications

Kemeny ranking aggregation meets the GPU

Kemeny ranking aggregation meets the GPU

About identification of features that affect the estimation of citrus harvest

Decision field theory-combined multi-attribute group decision-making method for incomplete linear ordinal ranking

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