Xu.Greedy algorithm for the general multidimensional knapsack problem[J]. Annals of Operations Research .2007(1)Akcay Y, Li H, Xu SH (2007) Greedy algorithm for the general multidimen- sional knapsack problem. Annals of Operations Research 150(1):17...
Method: n_last Divides the data into a specified number of groups. The algorithm finds the most equal group sizes possible, using all data points. Only the last group is able to differ in size. E.g. group sizes: 11, 11, 11, 11, 13 Method: n_rand Divides the data into a specified...
Our experiments show that our method diff is faster than linear in most cases, whilst using more space. The UNIX diff command is based on the greedy algorithm by Myers and Miller [11] for the unit cost function. Since their algorithm fills the values of the dynamic programming table in ...
[41] proposed a generalization privacy-preserving method suitable for the scenarios of 1:M records (an individual can have multiple records) with multiple sensitive attributes. Temuujin et al. [42] developed a more efficient l-diversity algorithm for preserving privacy of dynamically published ...
| with the greedy algorithm. The first proof is from Johnson [20]. Lov´ asz [23] obtained the same factor with a different method. Later, Chv´ atal extended the result to the weighted set cover problem [8], in which the subsets S ...
Gumbel-softmax optimization method builds a mixed algorithm that combines the batched version of GSO algorithm and evolutionary computation methods. The key idea is to treat the batched optimization variables—the parameters as a population, such that the evolutionary operators, e.g., substitution, ...
And, a generalized SAS macro can generate optimized N:1 propensity score matching of subjects assigned to different groups using the radius method. Matching can be optimized either for the number of matches within the maximum allowable radius or by the closeness of the matches within the radius....
The heuristic reduction method is one of the desirable methods for overcoming the drawbacks of the discernibility matrix approaches. Shen et al. [28], [29], [30] presented QuickReduct algorithm, which started with empty subset, and then added the most significant feature into the candidate set...
This approach makes the framework easily applicable to many combinatorial optimization problems without any change in the method and given the proper training step for each problem separately. The training process of DRLH makes it adaptable to different problem conditions and settings, and ensures that...
Thus the compressor uses a greedy algorithm for building the dictionary. The optimal algorithm would consider all possible dictionaries and their effect on compression, but this would be prohibitively time-consuming. To perform dictionary encoding, the compressor uses a order-1 semi-static Markov ...